MIT News

Mentees, colleagues, friends share remembrances of Susumu Tonegawa

David Orenstein | The Picower Institute for Learning and Memory — Wed, 04 Nov 2026 14:25:00 -0500

Before he passed away on July 11, Susumu Tonegawa had been a professor at MIT for more than 40 years. At a memorial service Oct. 16, colleagues, mentees and other friends spanning almost the whole arc of his illustrious MIT career gathered with members of his family to celebrate his life with recollections and stories that praised not only his science and achievements, but even more so his humanity.

The picture that 16 speakers collectively painted was one of a man who simultaneously demanded excellence and yet consistently demonstrated kindness and a joyous enthusiasm for science (and art and baseball).

Although he was a 1987 Nobel laureate for his breakthrough immunology research and is renowned for his groundbreaking research on memory, the testimonials dwelled little on his awards. Instead, they focused on a much more personal and intimate degree of inspiration and impact.

Scientist and mentor

Picower Professor Myriam Heiman, director of The Picower Institute for Learning and Memory that Tonegawa founded as MIT’s Center for Learning and Memory in 1994, had been a colleague of Tonegawa’s for 15 years. But she shared that his work in the fields of immunology and memory had been an inspiration to her long before that — ever since she was an undergraduate student at Princeton University. There, she studied his work to explain the diversity of antibodies during an immunology class at the same time she was reading a Proust novel dedicated to how memory and art can restore and make immutable what would otherwise be lost to time.

Professor Amy Keating, head of Tonegawa’s home department of Biology, hailed his passion for molecular biology as an inspiring example for students. She noted that he took big risks to learn the emerging subject in the 1960s by moving across the Pacific Ocean to enroll in graduate school in San Diego. And while he achieved historic results by applying molecular biology to both immunology and neuroscience, he only did so after persisting through worries that he and his fellow PhD students shared about the future of the field when they graduated in 1968.

Much like Tonegawa learned molecular biology from the ground up, former graduate student Arvind Govindarajan PhD '05, now at McKinsey & Co., joined Tonegawa’s lab in 1999 based on his curiosity about the brain, rather than based on having had any prior neuroscience training. He affirmed that Tonegawa encouraged his mentees to be ambitious and to dream big, even if they didn’t start out familiar with the subject matter. Govindarajan quoted Tonegawa as saying, “It is irrelevant what you know. It only relevant what you want to work on.” And similarly encouraged him to “have no fear, if the problem is interesting, go forth and do it, but when you publish, it has to be perfect.”

Another early mentee, Tom McHugh PhD '01 of the RIKEN Center for Brain Science in Japan, similarly described Susumu’s assertive encouragement, quoting Tonegawa as saying, “A journal doesn’t make your paper good, your paper makes the journal good.” But McHugh also noted that Tonegawa advised against ever resting on one’s laurels by often quoting the Japanese proverb: “after victory, tighten your helmet.”

Although Tonegawa pushed for excellence, even down to the details, 1980s mentee Juan Lafaille of New York University reminded the gathering, “he was, at least, as demanding of himself as he was of us.”

And Tomás Ryan, a postdoc in Tonegawa’s lab from 2010 to 2016, emphatically seconded the idea that Tonegawa’s advice was not only worth receiving, but also passing on. He shared a list of seven lessons that guide how he runs his own lab at Trinity College in Dublin, from where he flew in to join the ceremony.

“In Ireland we often say, you should never meet your heroes, you’ll only be disappointed,” Ryan said. “For me, the exception to this maxim was Susumu … I was incredibly fortunate to have Susumu as a mentor.”

Builder of communities

Many other speakers remembered Susumu as a community builder, not least because he founded the Picower Institute.

MIT School of Science Dean Nergis Mavalvala praised Tonegawa for building a community of neuroscience at MIT that has had impact well beyond his pioneering work on how memories are stored, recalled, and associated with feelings and relationships. The Picower Institute today comprises 16 labs and about 400 people, and hundreds more have trained in its labs over the decades.

“It takes a vision bigger than your own science and your own lab to be part of founding something this influential, and I really admire him for that,” Mavalvala said.

Matt Wilson, the first professor Tonegawa hired into the “Center for Learning and Memory,” recalled Tonegawa’s absolute confidence. Wilson for instance, didn’t bother renovating any lab space, because he believed (correctly, it turned out) that Tonegawa would deliver on the promise of an entirely new building (Building 46).

“The thing that really struck me was not just his infectious enthusiasm, but the absolute certainty that he had that this was going to be a success, that this was going to have impact,” Wilson said. “We built everything from scratch.”

Govindarajan recalled how Tonegawa involved himself deeply in the design of Building 46, working with architect Charles Correa to make sure every lab space would be the best it could be. And MIT Corporation member and longtime Tonegawa friend Barrie Zesiger fondly recalled a story about how when the building was new, Tonegawa stood with her at a huge window overlooking the train tracks that run directly underneath. They stayed perfectly still with their hands on the window as the train ran under, but after it passed he was elated that the vibrations were so minimal.

“It was like a child, the joy on his face,” she recalled.

Biology Professor Chris Kaiser recalled how as an assistant professor he worked with Tonegawa when he was recruiting young faculty to the new learning and memory center, including Wilson, Troy Littleton, and Elly Nedivi.

Zesiger said, “He loved his learning and memory colleagues — loved them all, all of you.”

Honest, joyous, and kind

Picower Professor Li-Huei Tsai, among the later of Tonegawa’s Picower faculty recruits, said she frequently sought his advice during her 17 years as Picower Institute director. He was “unfailingly honest,” yet warm and supportive, she said. And he never lost his sense of wonder.

“I came to value that honesty enormously,” Tsai said. “It is only now beginning to sink in that I will no longer be able to turn to him for his sage advice.’’

Longtime and current lab member Michele Pignatelli also admired the childlike passion and wonder that Tsai and so many others recalled. He recounted how, when he first learned from Tonegawa’s wife Mayumi that Tonegawa had passed away, he initially felt heartbreak and anxiety, but that quickly transitioned to “an overwhelming sense of gratitude.”

“Working with Susumu has been the greatest honor of my life,” Pignatelli said. “We will keep the flame burning.”

In his own personal tribute to Tonegawa, Peter Mombaerts PhD '92, a graduate student from Tonegawa’s immunology days who is now the director of Max Planck Research Unit for Neurogenetics in Frankfurt, was one of the few to dwell even for just a moment on Tonegawa’s Nobel Prize. But he did so only to make the point that Tonegawa did not suffer from “Nobelitis,” a loss of humility and perspective said to sometimes afflict those who’ve achieved their field’s highest honor.

In an especially poignant anecdote illustrating Tonegawa’s capacity for humility and kindness, current lab postdoc Quentin Ferry shared that he has a young daughter with a set of disabilities that often leave people unsure of how to interact with or understand her. She is often therefore left out. But at a lab cookout last summer, Ferry heard his daughter laughing. Tonegawa was playing with her joyfully.

“He didn’t see a condition to be nervous around, he saw a little girl who wanted to play,” Ferry said. “We will all remember Susumu’s brilliance. What I wish is that we also carry forward his kindness.”

Memorial fund

At the memorial, Picower Institute director Myriam Heiman announced that a generous donor has pledged $100,000 to launch the Susumu Tonegawa Memorial Fund, an endowed fund that will provide enduring support for curiosity-driven research at the Picower Institute.

Additional gifts will help grow the fund and expand its long-term impact, ensuring that Tonegawa's spirit of scientific discovery continues to inspire future generations of researchers. To contribute, give online or email memorial-gifts-office@mit.edu.

MIT-led team receives funding to develop a wearable monitor for early disease detection

Adam Zewe | MIT News — Wed, 04 Nov 2026 00:00:00 -0500

MIT researchers will lead a four-year, up to $38 million project, funded by the U.S. Advanced Research Projects Agency for Health (ARPA-H), aimed at developing a noninvasive, wearable patch that continually monitors biomarkers of heart disease and sepsis.

The project is called PYTHIA, which stands for “predictive platform for physiological and therapeutic health intervention and adaptive management.”

According to the project plan, the patch, which will capture clinical-grade health information in real time, will be powered by heat from the patient’s body. It could provide essential information to help clinicians diagnose heart failure and sepsis early, before the diseases have caused irreversible damage.

The researchers believe the minimally invasive device could also track the progress of a patient’s recovery after they are discharged from the hospital, and assist clinicians in developing more effective therapeutic approaches.

Catching common killers

Heart disease is the number one cause of death in the U.S., while sepsis kills at least 350,000 U.S. adults each year and is the leading cause of death in hospitals, according to the Centers for Disease Control and Prevention.

“We targeted these diseases because they are very lethal, but we also see opportunities for detecting biomarkers related to heart disease and sepsis to offer a solution that could really help improve diagnosis and treatment,” says principal investigator Ahmad Bahai, a professor of the practice in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics at MIT.

Bahai will be joined on the project by MIT researchers Joel Voldman, the William R. Brody Professor of EECS; Tim Swager, the John D. MacArthur Professor of Chemistry; Hadley Sikes, the Willard Henry Dow Professor in Chemical Engineering; and Collin Stultz, the Nina T. and Robert H. Rubin Professor of EECS; with additional clinical and industry collaborators.

“We have created an ecosystem of R&D, with MIT at the center of it, to facilitate not only leading-edge research, but also a path toward translation into a device that can be used in the clinic,” Bahai says. “Our goal with this project is to impact the future of personalized health care.”

PYTHIA grew out of the MIT Program for Health Sciences and Semiconductors (HS+S), which Bahai co-founded when he joined MIT three years ago. The program focuses on leveraging advances in semiconductor technology and nanotechnology to invent new, transformative approaches to diagnostics, therapeutics, and drug discovery.

Inspired by his previous successful work on continuous glucose monitors, Bahai conducts research on a wide range of technologies including electrochemical devices, optics, magnetics, and ultrasonics in his “Waves, Bites, and Molecules” lab. His group’s goal is to offer clinical-grade wearable devices that track biomarkers continuously to enable real-time intervention techniques.

The glucose monitor research inspired Bahai and his colleagues to consider other biomarkers they could monitor in real-time using semiconductor technology, which led to the launch of PYTHIA.

The chip-based, wearable patch will monitor heart failure and sepsis biomarkers in interstitial fluid, which is the fluid under the skin. The biomarkers, molecules in blood plasma that are related to the onset of these diseases, are hard to detect because they exist in very low concentrations.

To measure the biomarkers in detail, the system will execute a chemical reaction that binds certain chemicals with the biomarker molecules in the interstitial fluid, making them easier to identify. The advanced microelectronics in the patch will conduct measurements, capturing biomarker data with enough sensitivity for clinical applications.

Since the device will conduct measurements continuously, amassing a huge amount of data, PYTHIA will incorporate on-chip AI models to translate raw data into useful information for clinicians.

“This is kind of like having the patient give a blood test every 15 minutes or so, but it happens automatically. That is the power of this technology,” Bahai says.

From here, the team will start developing and testing electronic components for the tiny, low-power chip. Then they plan to advance the wearable device through pre-clinical and clinical trials, to the point that it can be commercially scaled and manufactured, Bahai says.

“This project has real potential to improve health care because, in addition to leveraging MIT’s leading-edge science and technology, we are also teaming up with clinical and industrial collaborators with direct experience of the practical constraints, the regulatory environment, and the clinical need,” he says. “The impact of this research could be huge.”

PYTHIA is funded through the ARPA-H Delphi Program led by Program Manager Leonard Tender. Delphi research teams apply a microelectronics design strategy to biosensing in an effort to develop devices that can safely, securely, and continuously monitor the body’s internal health signals.

The project described here is supported by ARPA-H under agreement number AY2AX000243. The content of this announcement does not necessarily represent the official views of the Advanced Research Projects Agency for Health.

Dream, build, destroy, repeat

Anne Wilson | Department of Mechanical Engineering — Wed, 04 Nov 2026 00:00:00 -0500

The MIT Combat Robotics Club (CRC) uses the tagline “build, battle, destroy, repeat,” which is a pretty spot-on description for a club that embraces learning opportunities through destruction.

“We make fighting robots,” explains Ryan Duarte, the 2025-26 CRC captain and current captain of a sub team for CRC’s ARES robot. “We try and make something indestructible to kill someone else's creation [that] they've tried to make indestructible … and they're trying to kill ours. In other competitions, the winner is who scores more points. You can tell at the end of a match by looking at [the robots] who the winner is.”

The club, which was founded in 2001 and is housed in the MIT Edgerton Center, was rebooted in 2023 with new members and designs. CRC sub teams compete in the National Havoc Robot League (NHRL) Professional Robot Fighting League. Competitions are hosted approximately every two months, including “house battles” on campus for the MIT community and local teams. Fans of the hit show “BattleBots” might remember some of CRC’s past robots, including Uppercut, SawBlaze, the Dentist, and Overhaul.

ARES, named for the Greek god of war, competes in the 30-pound weight class (other NHRL categories include the 3lb and 12lb weight classes). The robot combines a “Beater Bar,” or vertical spinner, with a compact, dual redundant poly-v belt brushless weapon power system on a high-performance brushless four-wheel drive with lightweight aluminum, titanium, and ultra-high-molecular-weight polyethylene chassis.

Abi Dixon, 2026-27 CRC captain, says the fighting machine was created from “from steel, titanium, aluminum, and passion.” The robot was a top seed going into last winter’s 2025 NHRL World Championships, but failed to emerge victorious. “Our two losses with ARES were against two opponents which shared a similar weapon type which our underdeveloped armor couldn't withstand,” says Duarte. The team saw the losses as an opportunity to make improvements.

“A unique thing about our outlook for these competitions is that no matter how poorly a match goes on our end, afterwards the dominant emotion is still pride,” says Dixon.

“Being in this community and on this team has changed me,” she says, because “how can you put so much energy, so much love, so much blood, sweat, and tears into a machine and then be totally OK with putting it inside the cage and it exploding, and then literally picking up your pride off the floor, smushing it all back together, and getting back in there? You grow an understanding of finding the wins, even when things look like a complete loss.”

ARES, captained by Duarte, along with fellow Department of Mechanical Engineering (MechE) undergraduates Henry Dang, Kendall Binkley, and Liam Gault, has been undergoing improvements. The team took second place at an April competition and third in October. CRC is simultaneously upgrading ARES and another robot, KRATOS, while continuing progress toward a next generation of robots.

At MIT, the CRC is open to everyone regardless of year, major, or prior experience. It is entirely student-run, self-taught, and highly collaborative. Participants engineer tough, reliable, performant systems and have an opportunity to learn a variety of skills, from computer-aided design and finite element analysis to CNC machining and welding.

“Before MIT, I never had the opportunity to independently say, ‘I'm going to make this,’ and complete everything in the engineering process from start to finish. CRC embodies that vision, and solidified my desire to become an engineer,” says Eleni Morales, CRC team member, 2025-26 co-manufacturing chair, and undergraduate in MechE.

CRC welcomed 54 new members this fall semester. As an onboarding exercise, the new members are building 1-pound, or “ant weight,” combat robots in teams of three students.

“We remain committed to making the team accessible for students with all levels of technical experience (including none!) through a structured onboarding process that prioritizes each and every member feeling as though they have technical ownership over a part of their sub team’s robot,” says Dixon.

The team will host their third annual MIT Combat Robotics House Battle on Nov. 14 at the new Met Warehouse.

3 Questions: Building more resilient minerals supply chains

Marika McCoola | Office of the Vice President for Energy and Climate — Tue, 03 Nov 2026 16:50:00 -0500

Global development and prosperity have become increasingly dependent on minerals and metals. This growing dependence has drawn attention to the mining industry and underscored national economic and security vulnerabilities across supply chains. As demand for these materials rises, the challenge for expanding capacity will be to develop scalable pathways to produce, use, and steward them.

In this interview, Professor Elsa Olivetti, co-director of the MIT Minerals Stewardship Consortium, discusses how collaboration can strengthen mineral and metal supply chains and focus research on practical impact.

Q. In its first decades, MIT had a Department of Mining and Metallurgy. In recent years, we’ve seen increasing interest in mining and minerals at MIT. What explains this resurgence?

A: Six of MIT’s first 14 graduates in 1868 earned degrees in geology and mining. In 1889, one of those graduates, Robert Hallowell Richards, was the MIT professor who explicitly linked metallurgy to mining engineering within Course III [Materials Science and Engineering]. MIT shaped its curriculum to meet the nation’s increasing need for advanced materials for railroads, industrial machinery, and electrification.

It feels very similar to our time. We’ve all heard about the growing demand for minerals to support the energy transition, defense applications, data centers, and global economic development. That includes tiny amounts of gallium for defense radar systems and vast quantities of steel, aluminum, and copper for the grid. But each element and supply chain has its own “personality.” For example, gallium is a by-product of the process to make aluminum, so increasing its supply requires coordination beyond mining.

Mining and mineral processing are environmentally intensive — and becoming more so. It's harder to obtain newly mined minerals, even from places where mining already occurs. Ore bodies are deeper, lower-grade, and more often located in socially and environmentally sensitive regions. Mine projects face shifting water availability, extreme climate events, the concerns of nearby communities, fluctuating mining costs, and advancing digital technologies that change how ore is monitored, extracted, and processed over a mine’s life.

And that explains why we are seeing a renewed focus on mining and minerals at MIT. To meet this moment and serve the nation, not only do we need advances in science and engineering for extraction, but also innovations in the markets and governance to incentivize developers and communities to engage continuously and transparently. Our campus is compelled by mining’s broad, systems-level impact on people, the complex science, and the entrepreneurial opportunity.

Q. President [Sally] Kornbluth has called curiosity-driven science our “intellectual rocket fuel.” What does fundamental research contribute to solving the challenges of mining and critical minerals?

A: What’s exciting to me is that these challenges call for fundamental research across MIT.

As an example, rare-earth elements have very similar chemical properties, making them difficult to separate from each other for the different end use applications we depend on. Researchers are exploring engineered microbes that can bind one metal selectively and withstand harsh conditions. This work draws on basic advances in producing engineered proteins and computational models that predict how metals bind. Other teams are investigating electrochemical approaches to extraction, studying how ions behave in ore minerals including oxides and sulfides to improve separation.

We also need contributions from different disciplines. Researchers in earth, atmospheric, and planetary sciences are partnering with colleagues in aeronautics and astronautics to advance remote sensing of changes in land use. Experts in automation and control are developing ways to keep sensitive separation processes within narrow temperature ranges, while mechanical engineers study fluid dynamics relevant to deep-sea mining. Some minerals are essential but traded in small volumes, raising distinct economic questions. Urban planners examine how community knowledge can inform more equitable and resilient decisions.

And we have to move ideas from the lab to the field. MIT’s Office of the Vice President for Energy and Climate, the MIT Energy Initiative, and other programs are seeding early-stage work. Some projects have attracted follow-on support, including the U.S. Department of Energy’s Genesis Mission, to advance technologies toward pilot testing. MIT startups Lithios and RockZero are pursuing lithium extraction from brines and hard rock, respectively. SiTration, Nth Cycle, and Found Industries are pioneering metal recovery from mining and industrial waste. These examples show how basic discoveries can lead to technologies and policy impact — and how challenges in the field can prompt new research questions.

Q. We see regular calls from the government and others to re-shore the production and refinement of critical minerals and rare-earth elements. What is the role MIT can play, and where do you see opportunities for collaboration?

A: Building domestic and allied capacity is more than just opening mines. Extraction, separation, refining, and manufacturing are technically demanding, and the economics are challenging. Trade restrictions can disrupt supply at any point along the supply chain. A domestic magnet industry, for example, needs reliable access to separated rare earth oxides. Otherwise, we replace a dependence on imported magnets with a dependence on imported materials. There must be capability across the entire supply chain to establish resilience, and it needs to be done responsibly and with a robust workforce. This will take collaboration across industry, government, and research institutions.

MIT can play a strong role as a convener, particularly among industry partners.

In September 2025, we launched the MIT Minerals Stewardship Consortium (MSC) with three inaugural members — BHP, Vale, and Rio Tinto — which MIT Sloan Professor Chris Knittel and I lead. All of this came together when staff from MIT’s Industrial Liaison Program came to me and said, "We've been having conversations with companies, and they're describing similar needs. Is there an opportunity to have them work together with MIT?" The MSC is a platform to gather stakeholders from across the supply chain with specific aims, such as identifying barriers to technology adoption, to curate research questions and collaborative next steps.

The consortium brings together faculty expertise in operations, urban planning, aerospace engineering, materials science, and economics. Through the MSC and other campus efforts, MIT connects industry, government, startups, and researchers to identify shared priorities and move research toward practical impact.

The U.S. administration and other governments are mobilizing capital, driving regulation, and building international and corporate alliances to strengthen critical mineral supply chains. Through the MSC and engagement across campus, MIT can bring together stakeholders to identify priorities, coordinate efforts, and ensure we help build competitive, resilient supply chains.

Featured video: Changing the conversation about endometriosis

Center for Gynepathology Research — Tue, 03 Nov 2026 16:00:00 -0500

Endometriosis affects more than 265 million women worldwide — 1 out of every 10 women of reproductive age, according to the United Nations — yet it remains one of the most misunderstood, under-researched, and chronically stigmatized conditions in modern medicine.

A new documentary, “End of the Cycle,” presented by The Endometriosis Collective, is changing that conversation about this painful disease, one powerful story at a time.

Directed by Sammy Jaye and Soraya Simi, the film weaves together intimate personal narratives, cutting-edge science, historical revelations, and cultural commentary to challenge decades of misconceptions. The result is both deeply personal and urgently scientific, sending out a clarion call for research investment, medical accountability, and systemic change.

The film features a talented ensemble of women living with endometriosis — comedian Amy Schumer, U.S. Olympian Brittany Brown, actress Janel Parrish, actress and musician Fọlákẹ́ Olówòfóyèkù, dancer Julianne Hough, and influencer and filmmaker Sammy Jaye Lang. When asked to describe endometriosis in three words, their responses cut to the heart of the condition: “agony, isolation, harmful.”

The film also profiles 20th century icon Marilyn Monroe, who struggled with endometriosis until her death in 1962 at age 36.

But “End of the Cycle” goes beyond depicting suffering; it also depicts action, resilience, and hope. Alongside patient stories, the film features leading researchers and clinicians, including MIT Professor Linda Griffith, director of the Center for Gynepathology Research at MIT. Griffith brings both her pioneering scientific expertise and her personal journey living with endometriosis — a perspective that has shaped her life’s work.

“There’s a fair degree of consensus about the definition of endometriosis,” Griffith explains in the film, “but we need to understand the basic process of how normal tissue is regulated so that we can understand the diseased tissue better. That’s my goal.”

Today, her research at MIT is pioneering new approaches to understanding and treating endometriosis. “Only by working together across all our disciplines and putting all our talents in all the different places they are needed, we will find answers, treatments, cures, and even prevention,” Griffith says.

On Sept. 30, more than 250 members of the MIT community gathered alongside industry partners, clinicians, patient advocates, and endometriosis researchers to mark Menstruation Science Day at MIT, centered around a screening of “End of the Cycle.” The event brought together students, faculty, donors, entrepreneurs, and leaders in women’s health for a single purpose: to challenge misconceptions about endometriosis and to catalyze the next wave of research, innovation, and care.

Griffith moderated an expert panel that followed. The panelists — Sammy Jaye Lang, filmmaker with the Endometriosis Collective; Steve Palmer, senior communications associate at Baylor College of Medicine; Eliezer Calo, associate professor of biology at MIT; Stephanie Morris, an obstetrician-gynecologist at Newton-Wellesley Hospital; and Aleshia Carlsen-Bryan, associate director of prehealth advising at MIT Health — offered multiple perspectives on where women’s health innovation stands and what it will take to accelerate progress.

Video by Sammy Jaye and Soraya Simi | 1 hour, 21 minutes

Building connections across the research infrastructure

MIT.nano — Tue, 03 Nov 2026 14:30:00 -0500

Running a modern research laboratory requires more than maintaining sophisticated instruments. Researchers need to complete training, obtain access, schedule and operate equipment, navigate billing, and more. For large, shared facilities, coordinating these activities across hundreds of instruments and thousands of users can be a significant undertaking. As research becomes increasingly interdisciplinary, another challenge is emerging: finding the right capability in the first place.

Addressing these challenges was a central theme of the 2026 NEMO User Meeting, hosted by MIT.nano Oct. 5–6. The meeting brought together 65 representatives from 25 laboratories and organizations, including facility directors, laboratory managers, technical staff, software developers, industry representatives, and government researchers. Sponsored by the Northeast Microelectronics Coalition (NEMC) hub, the meeting focused on sharing approaches to lab management and building connections among facilities.

At the center of the meeting was the NanoFab Equipment Management and Operations (NEMO) system, an open-source platform that provides tools for managing shared research facilities while allowing individual laboratories to adapt the system to their own needs. The meeting highlighted an important characteristic of the NEMO community: The same platform can serve a very diverse set of facilities, and while the scale or the scientific missions of the facilities may differ, their core operational challenges are often remarkably similar.

For Anna Osherov, associate director of Characterization.nano and organizer of the meeting, the collaborative model is one of the most compelling aspects of NEMO.

“Open source changes the way we think about technology development,” she says. “We can build on what others have already developed, contribute improvements back to the community, and collectively create a platform that is more capable than what any one facility could develop on its own.”

NEMO was originally released as open-source software by the National Institute of Standards and Technology (NIST) in 2018. Its community edition (NEMO-CE), launched in 2023, allows facilities to develop and share extensions to the platform. At the October meeting, participants from organizations including MIT; Stanford, Columbia, and Princeton universities; the University of Pennsylvania; NIST; NASA’s Jet Propulsion Laboratory; and Atlantis Labs presented tools to address challenges ranging from chemical management and user administration to equipment status, training, and workflow automation.

The range of presentations illustrated how differently configured facilities are adapting a common platform to their own needs. Stanford, for example, presented real-time displays for equipment status and reservations, while Columbia shared its experience migrating to NEMO and extending the system for billing and facility workflows. Princeton demonstrated automated process charts for monitoring tool performance, and the University of Pennsylvania presented approaches to chemical management.

Overall, the projects demonstrated the premise behind the NEMO community: a solution developed to address a problem at one facility can become a resource for others.

“Together we can do way more than any one of us individually can,” said Vladimir Bulović, director of MIT.nano, during his opening remarks. “NEMO is the platform that gave us, finally, a streamlined way to do so.”

From individual facilities to a connected research ecosystem

As the NEMO community grows, another opportunity emerges. Universities and research organizations operate specialized facilities, each with unique equipment, expertise, and capabilities. But researchers cannot use a capability they are unaware of or cannot easily find.

This is particularly important for microelectronics R&D, where progress often depends on moving quickly between specialized capabilities. A researcher may need fabrication at one facility, advanced characterization at another, specialized microscopy somewhere else, and a process or analysis capability available at only a handful of institutions.

Finding those capabilities, and knowing where and when they can be accessed, can itself become a bottleneck.Traditionally, networks of facilities have relied on static webpages or databases to describe available resources. But as facilities evolve, this information can quickly become outdated.

At the meeting, MIT.nano presented Hydra, a new open-source platform being developed with support from NEMC as a key component of the NEMC Technical Concierge, a program that aims to help researchers identify and access specialized capabilities across a network of facilities. Hydra is designed to connect multiple NEMO installations and draw information directly from the systems that facilities already use, rather than requiring someone to manually update a separate database. The result is a compilation built from the ground truths of actively-used facility management systems. “Hydra does not replace any lab management system,” Osherov explained. “It connects, synchronizes, and keeps data up to date across all connected systems in real time.”

In this model, NEMO provides the operational source of information within an individual facility, while Hydra makes that information discoverable across facilities.

For a researcher, that could mean finding a specialized capability without first needing to know which laboratory might have the relevant equipment.

“The focus is to make the distributed infrastructure supporting research more discoverable and usable,” Osherov said.

Building infrastructure that can change

The approach reflects a broader philosophy: Research infrastructure needs to evolve as research itself changes. “We need to build a flexible research space and a community-oriented space so that people feel empowered to go to it, use it, and then redefine it as the years progress, adapting it to the needs of the next generation of researchers,” said Bulović.

That flexibility applies to operational systems as well as physical laboratories. New instruments and research methods emerge, user communities change, and facilities develop new requirements. An open-source platform allows individual facilities to respond to those changes while providing a mechanism for improvements to be shared.

At MIT.nano alone, NEMO supports more than 1,600 active researchers using more than 200 tools, with approximately 160,000 hours of tool usage each year. For Jorg Scholvin, associate director of Fab.nano, operating at that scale makes the efficiency of everyday processes, and the ability to adapt and change quickly, important.

“A small improvement in an administrative workflow can be multiplied across thousands of users and tens of thousands of equipment interactions,” he said.

The NEMO wave

The sense of community and positive outlook for the future of shared facilities was apparent throughout the two-day meeting. Closing discussions looked ahead to the role of data integration, automation, and artificial intelligence in lab operations.

“The community is taking on a life of its own,” said Mathieu Rampant, CEO and co-founder of Atlantis Labs, describing a model in which facility managers can share ideas and solve problems together. “It’s more sustainable,” he added as he talked about having to do less as the NEMO-CE manager because the facilities managers are talking to each other — sharing ideas and solving problems as a network: “the NEMO wave,” Samantha Roberts, director of the City University of New York nanofabrication facility, called it. “Everyone is invested in it and cares about it. It takes a lot of effort to change your lab management system and if we keep this going, we can build out something that’s really cool and that works.”

For Osherov, the purpose remains straightforward.

“Ultimately, this is about enabling research. The better we can manage the operational side of our facilities, the more effectively we can make our instruments, expertise, and capabilities available to the research community.”

In microelectronics, where innovation increasingly depends on access to specialized and distributed capabilities, that availability matters. The ability to find the right capability at the right facility, using information drawn directly from actively used systems, can help researchers move more quickly from an idea to an experiment to real-world application and impact.

Lemelson-MIT brings invention education to classrooms from kindergarten to college

Tracy Jean-Chronberg | Lemelson-MIT Program — Mon, 02 Nov 2026 16:25:00 -0500

MIT was founded on the idea that people learn best by doing. For nearly 25 years, the Lemelson-MIT Program in the MIT School of Engineering has carried that idea into K–12 classrooms across the country, helping teachers guide students as they find real problems and invent solutions to them.

Now the program is expanding that work into connected pathways that follow students from the early grades through college.

This month, Lemelson-MIT awarded multiyear Invention Education Pathways grants and stipends to 19 schools, districts, and colleges across three regions: Northern California, Tennessee, and Massachusetts/Connecticut. The partners include elementary, middle, and high schools, as well as a community college and two state universities, together forming routes for students to experience invention at every stage of their education.

The initiative arrives as MIT President Sally Kornbluth has launched MIT for America, a strategic effort to widen access to quality STEM education nationwide, from kindergarten to community college. One of its three focus areas, Design, Make and Innovate, aims to give students more chances to build, test, and prototype their way toward solutions, and to support teachers in bringing hands-on work into their classrooms. Invention education does exactly that.

Learning the way inventors work

In invention education, students work in teams to identify problems that matter to them and their communities, then develop prototypes of original solutions. Along the way, they apply knowledge from multiple disciplines and develop both technical skills and durable human skills such as empathy, collaboration, and communication. They also gain confidence in their own abilities.

“Understanding user needs is critical in product design,” says Michael Cima, faculty director of the Lemelson-MIT Program and a professor of materials science and engineering. “It’s the foundation everything else is built on. Invention education builds that understanding early, teaching students to ask whom they’re designing for and what is truly needed before they ever build a prototype.”

Rather than offering a one-time project or an after-school club, the pathways approach aims to give students repeated, connected invention experiences as they advance. Lemelson-MIT staff will work alongside educators in each region to assess, design, and implement learning experiences that help students apply scientific, technical, engineering, and mathematics (STEM) knowledge to real-world problems. Schools and colleges can adopt Lemelson-MIT curriculum resources selectively to jump-start or strengthen their offerings.

“We have learned that invention education can benefit all students, especially when students can experience learning through the invention process across multiple grade levels,” says Stephanie Couch, executive director of the Lemelson-MIT Program. “Our goal is to create opportunities for learning through invention education during the regular school day, beginning in the early grades and continuing into adulthood.”

Every student an inventor

The three regions were chosen through a nationwide search for schools and districts committed to building strong invention pathways from kindergarten through college and to increasing the number of inventors from diverse backgrounds.

That commitment rests on a simple observation from Lemelson-MIT’s work: Given the chance to invent, students of every background surprise themselves, and their teachers, with what they can do. The program’s experience, case studies, and published research show that invention education helps students, especially those underrepresented in STEM, see themselves as capable STEM learners and inventors. Students in the program’s flagship high school initiative, InvenTeams, have earned 24 U.S. patents.

The benefits extend into the workforce. “Employers benefit from graduates that have the types of capabilities they need, but have trouble finding,” Couch says. “Invention education connects students’ knowledge and passions with the technical and durable human skills employers are looking for.”

Participating institutions

Northern California: California State University at Stanislaus; Columbia College; Columbia Union School District; Denair Unified School District; Folsom Cordova Unified School District; Stockton Unified School District; and Visions in Education University Prep Early College Academy
Tennessee: Apison Elementary School; Battle Academy; Hixson Middle School; Normal Park Museum Magnet School; and Red Bank elementary, middle, and high schools
Massachusetts and Connecticut: Berlin High School; Cambridge Rindge and Latin School; New Covenant School; Watertown High School; and Westfield State University

Looking ahead

Over the next three years, the Lemelson-MIT Program will work closely with educators in all three regions toward a shared goal: making invention education a regular part of the school day, rather than an occasional enrichment activity.

Lemelson-MIT hopes that what these partners learn together will help show how invention pathways can take root in schools and colleges across the country.

Faces of MIT: Vera Grbic

Katy Dandurand | MIT Human Resources — Mon, 02 Nov 2026 15:45:00 -0500

Vera Grbic, communications officer at the Institute Discrimination and Harassment Response Office (IDHR), is an expert at creating clear, informative collateral. Whether she is improving the IDHR website to help people more easily find the support or resources they need or using newsletters and annual reports to explain IDHR's work and the policies that guide it, Grbic believes it is vital for members of the MIT community to know where to turn and what resources are available to them.

Grbic earned a BA in communications, with a minor in English literature, from the University of Ottawa, and an MA in communications from Carleton University. Both institutions are in Canada's capital city of Ottawa.

Before joining MIT in 2021, Grbic worked in a mix of federal government, nonprofit, and research organizations. After graduate school, she worked as a freelance journalist, during which she developed a skill she still relies on today: collaborating with subject-matter experts to ensure that creative communications are grounded in fact. As a senior communications officer for the National Research Council of Canada, she worked with engineers and scientists in areas such as artificial intelligence, climate research, aerospace, and physiology. She elevated their research and innovations to key audiences and helped create new partnership opportunities. In this role, she translated complex information into engaging communications that furthered the organization's goals. Grbic describes this work as the "through-line" connecting her previous experience to what she does today at MIT.

IDHR addresses concerns related to discrimination, discriminatory harassment, and sexual misconduct by providing education, individualized supportive measures, information about available resources, and complaint resolution pathways that include both restorative and investigative processes. Grbic develops IDHR’s annual communications strategy and has spearheaded several key initiatives, including a website and annual report redesign and the launch of IDHR’s Data Dashboard. She collaborates with colleagues across MIT to gather content ideas for newsletters, data and stories for annual reports, and feedback on communications materials. She often partners with IDHR's prevention education and outreach team, which delivers training to the community.

Grbic notes that being a communications professional means constantly learning, whether that entails researching a new topic, finding creative ways to tell a story, or meeting subject-matter experts who broaden her perspective. Accuracy is at the core of her approach. While she strives to find creative ways to explain policies and complex processes in ways that are accessible to everyone, collaboration and fact-checking are intrinsic to her work. Given the wide range of responsibilities within communications, Grbic is a jack-of-all-trades. Her experience includes creative communications, social media management, long-form storytelling, event production, report writing, video production, and website management.

In addition to her communications responsibilities, Grbic co-organizes the annual Change-Maker Awards, which honor members of the MIT community who work diligently to prevent sexual misconduct and combat harassment. She takes great pride in helping lead an event that recognizes individuals who are making a meaningful difference in building a safer culture at MIT. Grbic describes the award winners as “true inspirations.”

Grbic recognizes that nothing is done in a vacuum. She values the input of her team members and colleagues across MIT and is proud to incorporate their feedback into her content. Because no two projects are exactly alike, she enjoys the creativity and variety that her work offers.

Soundbytes

Q: What are your favorite projects that you have worked on?

A: I was part of a team that helped develop and launch the most recent sexual misconduct surveys for students and employees. I also strategized a campaign to clarify IDHR services, which included whole-scale website improvement: rewriting many sections, adjusting navigation, improving visual design, and publishing a short video explaining our process. There was also a poster campaign about the services we offer the community. MIT is awash with important storytelling, so getting that messaging to people who need it is vital. These projects brought me pride because taking the time to understand a community's experiences can have a meaningful impact.

Q: How would you describe the MIT community?

A: Smart, in a well-rounded way. Everyone is an incredibly hard worker and thinks in deep, research- and experience-backed ways on how to solve complex projects.

As an employee who has worked with students on Institute-wide initiatives, committees, and awards, I have seen how much they care about making the world a better place. The staff and faculty are gracious and bring a strong work ethic to everything they do in support of the MIT community.

Q: Are you involved in any areas of the Institute that are not directly related to your job?

A: I recently started participating in the MIT Women’s League, which I am excited to explore. When I was pregnant, I took prenatal classes at MIT, including a yoga class where I met people I am still in touch with years later. I also played in a volleyball league at the recreation facilities, which made me see the facilities from a different angle.

I’ve attended many free conferences and events hosted by MIT clubs and schools. For example, a children’s clothing swap has been organized for years, which also provides an opportunity to connect with other families in the community. I also prioritize keeping up with current topics and have attended two AI conferences organized by the Institute.

While these opportunities may feel secondary to the workplace, they are important. I’m glad I am in a space that helps feed my physical health and has opportunities for broader learning.

Powering up for a pivotal moment

Leda Zimmerman | MIT Energy Initiative — Mon, 02 Nov 2026 14:55:00 -0500

Utilizing waste carbon at scale, assessing public trust in artificial intelligence and data centers, and building a quantum toolkit for critical applications were among the topics energy experts explored at the MIT Energy Initiative (MITEI)’s annual research conference on Sept. 22-23.

The complexity of the energy transition demands “a truly interdisciplinary and international approach,” said Paula Hammond, Institute Professor and dean of the MIT School of Engineering, kicking off the conference, which was themed “Transitioning energy at a pivotal moment.”

More than 200 people attended Energizing@MIT: MITEI’s Annual Research Conference in person and online, including faculty, scientists, and researchers; representatives of global energy firms; nonprofit organizations; and academic institutions.

With the rapid proliferation of artificial intelligence and data centers, the challenge of the energy transition grows ever greater, requiring “researchers who can pursue fundamental questions about materials, chemistry, and energy systems … engineers who can design new technologies and determine how they might function at scale … and experts in economics, policy, human behavior, and sociology who can help us understand how those technologies will interact with communities and with the world around us,” said Hammond.

From keynote speakers to panelists, seed grant innovators to startup entrepreneurs, the conference featured exemplars in all these categories.

Deploying today’s technologies

Eric Toone, chief technical officer and managing partner at Breakthrough Energy, set the tone for day one with a keynote address that was clear-eyed in his assessment. “If energy is prosperity,” he told the audience, “at least today, prosperity is emissions.” Yet, Toone was optimistic when he heralded examples of Breakthrough Energy-funded ventures, from iron-air batteries to vacuum-insulated glass windows, which can reduce the carbon costs of energy.

“We can absolutely develop new technologies that provide the exact same service, but at zero carbon,” he said.

The conference made Toone’s claim tangible as it spanned the full arc of decarbonization — from deploying what we know today to uncovering what may be possible tomorrow.

There are “projects that are actually being developed, companies that are putting steel in the ground now, electrons onto the grid to deliver capacity now, or in the very, very near term,” said John Parsons, deputy director for research at the MIT Center for Energy and Environmental Policy Research. Parsons moderated a panel on the revival of nuclear fission power.

Highlights included Crane Clean Energy Center’s restoration of the Three Mile Island Unit 1 nuclear plant, set to restart in mid-2027; work by Électricité de France (EDF) to extend and modernize France’s 57-reactor nuclear fleet, adapting plants to climate impacts; and GE Vernova’s development of mature light-water reactor technology, with a new plant under construction in Ontario.

Carbon utilization offers another path to mitigating carbon dioxide (CO₂) emissions. “The land and ocean are passively taking up almost 50 percent of anthropogenic CO₂ emissions,” said Carolyn Ruppel, MITEI’s deputy director for science and technology, who moderated the panel. “The idea is to find uses for some of the rest of it.” The session showcased research exploring a host of technologies for making products with captured CO₂.

Nicola Ferralis, a research scientist in the MIT Department of Materials Science and Engineering (DMSE), highlighted the importance of large-scale uses for carbon waste, such as structural carbon for vehicles and buildings. “We need to find applications that are actually able to match the supply by mass,” he said.

Admir Masic, an associate professor in the MIT Department of Civil and Environmental Engineering and DMSE, described self-heating concrete panels used in real-world conditions in Sapporo, Japan, as well as new concrete materials that can store energy.

“The pace of the transition to a renewable-energy economy will strongly rely on the availability of scalable, bulk energy-storage solutions,” said Masic.

Kripa K. Varanasi, a professor in the MIT Department of Mechanical Engineering, is working on ways to make technologies such as emissions-plume capture at power plants economically viable.

In a panel devoted to startups with links to MITEI or MITEI member companies, speakers described moving novel energy technologies from research to commercialization: Alfredo Rivela of Adaion has developed an AI tool for electrical utilities that optimizes their use of the grid; Eddie Obropta of Raptor Maps trains drones to inspect solar farms; Michele Guarnieri’s HiBot has created a robot that detects issues in power lines; and Michael Wang of Lithios is conducting field pilots that use electrochemistry to separate lithium from low-grade brines.

Successful ventures, the speakers agreed, begin with a clear customer problem that can be validated by industry users and that offers a path to scaled deployment. For industry users, “innovation is not a corporate virtue,” said Steve Hellman, managing partner at Alliance Partners. “It’s an existential requirement.”

Early-stage research and basic science

The conference also offered a bounty of promising early research on technologies and approaches for cutting carbon emissions. A panel on MITEI’s Seed Innovation Fund Program projects introduced MIT scientists who are separating and recycling rare earth elements; designing algorithms to decide which power plants should run to meet electricity demand; and modeling the impacts on human and natural systems of development and decarbonization.

A poster session revealed the next generation of energy researchers in such fields as chemistry, architecture, aeronautics and astronautics, and computer science. Graduate students are developing a wide array of technologies for carbon mitigation and new energy systems, such as a steam electrolysis process for hydrogen production; models of end-to-end decarbonization in buildings; and designs for hydrogen- and methane-fueled aircraft.

The conference highlighted the importance of fundamental science in feeding the pipeline of new energy technologies. Wednesday’s keynote speaker, Danna Freedman, the F. G. Keyes Professor of Chemistry at MIT and inaugural director of the MIT Quantum Initiative (QMIT), traced scientists’ evolving capacity to harness quantum computing as a tool for solving a range of problems, including those in energy.

“What we want to do at QMIT is solve the hard problem, and the hard problem isn’t developing these ridiculously intricate and beautiful quantum tools,” said Freedman. “It is connecting the quantum tools with the applications that will change the world.”

A companion panel explored one such application: quantum sensing, which leverages fundamental physics to capture a nanoscale view of materials and their properties. For instance, Lukasz Zielinski, scientific advisor to the Integrated Sensing and Measurement Program at SLB, uses a lidar camera with single-photon detectors to identify methane plumes. Moderator Paola Cappellaro, the Ford Professor of Engineering in the MIT Department of Nuclear Science and Engineering, described her work using diamond-based sensors for detecting tiny magnetic fields from battery current, potentially helping measure battery conditions without disturbing the battery electrically. Such quantum sensors, Cappellaro said, can be “practical and impactful.”

Data centers and public trust

One critical challenge facing energy researchers and industry emerged in sessions throughout the two days: the explosive growth of AI and the energy-hungry data centers that fuel this technology. As Deepjyoti Deka, a MITEI principal research scientist, put it, “the big test for AI right now is whether AI can reduce energy usage, improve efficiency, as well as bring in innovation faster than the rate at which it is going to consume electricity to get trained and deployed.”

“New ‘hyperscale’ facilities built for artificial intelligence can use more power than entire cities,” said Steve Clemmer, director of energy research in the Climate and Energy Program at the Union of Concerned Scientists, sharing a recent analysis of data center proliferation. With new facilities being 10 to even 10,000 times larger than data centers built just a decade ago, he said, wholesale electricity costs will rise, and “increased reliance on fossil fuels will result in tens of billions of dollars in health damages, and trillions in climate damages.” He noted that decision-makers can mitigate these harmful impacts through clean energy development, strong ratepayer protections, and other safeguards.

Given mounting opposition to data centers, Kathryn Kline Tyndall, associate director of energy at the Bipartisan Policy Center, recommended that industry proponents provide early, accessible information about power, water, quality-of-life, land-use, environmental, and economic impacts, tailored to each community in which they hope to set up shop.

Larry Susskind, the Ford Professor of Urban and Environmental Planning in the MIT Department of Urban Studies and Planning, recommended that developers go further, and demonstrate credible benefits to communities. To achieve this, Susskind suggests a facilitated negotiation process between developers and local groups whose object is a community benefits agreement — a deal that might involve altering the data center design, and providing workforce development, infrastructure investment, and clean energy partnerships.

Ultimately, developers must accept that they can’t rush community acceptance; they have to earn it by giving residents a meaningful role in shaping projects and sharing their benefits.

“You’re going to have to proceed at the speed of trust,” said Susskind.

A flexible new route to designer materials built from nanoparticles

Zach Winn | MIT News — Mon, 02 Nov 2026 05:00:00 -0500

Creating better-performing materials comes down to having more control over their structure at the smallest possible scale. One particularly effective way to accomplish that is using nanoparticles, which have highly customizable shapes and sizes and have already shown promise in applications across energy, medicine, and electronics.

But the process of turning nanoparticles into useful new materials has been hampered by the difficulty of controlling the number and direction of the links between each particle and its neighbors. Researchers have tried changing the shape of particles or precisely aligning the rigid connector chains between them, but both of those methods limit the material structures nanoparticles are able to form and are difficult to scale.

A team including researchers from MIT is taking a different approach. The researchers developed a tunable way to use super-flexible connector molecules that regulate the packing of nanoparticle networks by intentionally deforming based on their surrounding environment. In a paper published today in Nature Materials, the researchers showed how connector flexibility and electric charges can be used to tune the network’s structure, and they used the technique to create new kinds of nanoparticle-based materials.

The approach could offer a more versatile way to create high-performing materials for photonics, chemical catalysis, medicine, agriculture, and more.

“People are interested in nanoparticle superlattices because of the promise of building designer materials where you have more say in what types of structures they can actually build into,” says Rob Macfarlane, the Richard P. Simmons (1953) Professor in Metallurgy in MIT’s Department of Materials Science and Engineering. “This is a new approach in nanoparticle assembly that hasn’t been investigated before. It’s exciting to have a new tool that is so counterintuitive. It’s the exact opposite of what the dogma has been for 20 years: that you need to make connector ligands rigid, and any deviation causes assembly to fail. We showed if you just crank it all the way to the opposite extreme, it works just as well.”

Joining Macfarlane on the paper are MIT PhD candidate Nicholas Sbalbi; University of Delaware doctoral student Stephen Kronenberger; MIT undergraduate Hana Shinzawa; Matthew Ye PhD ’26; and former University of Delaware professor Arthi Jayaraman.

More flexible structures

A foundational truth in materials science is that a material’s structure governs its function.

“That means if you know what your building blocks are and how they’re arranged, you can explain what properties materials have,” Macfarlane says. “Controlling that is how you make materials that have new properties.”

Nanoparticles give scientists a flexible building block to make materials with desired properties, and individual nanoparticles are already used in applications as diverse as drug delivery, energy storage, computing, and sensing. But assembling them into larger structures has proven to be a challenge because of how tightly they tend to pack together.

To overcome that challenge, researchers make nanoparticles different shapes, which changes how they connect. They can also make only specific parts of the particles’ surfaces sticky so they form different patterns.

“The challenge with both of those approaches is they’re difficult to do synthetically as we make the particles,” Macfarlane explains. “The methods for defining stickiness that have been developed are always bespoke: They work for one material, at one size scale, in one condition. It’s good for studying, but you can’t do much with it.”

Both methods also require connector molecules, called ligands, at the surface of the nanoparticles to be rigid so they stay at the specific angles needed to maintain the formation researchers want, which further limits their functionality.

For their new study, the researchers wanted to turn that approach on its head.

“We thought, ‘Let’s do the exact opposite: make the bonds so flexible it doesn’t matter what direction they’re initially pointing.’ The ligands can then reorganize as the particles come together, and that reorganization itself limits how many neighbors each particle can bind,” Macfarlane says.

Macfarlane’s group has spent years creating ways to make different kinds of particles from grafts of polymers. The nanoparticles in their new system have flexible ligands with electrically charged tips, some positively charged and others negatively charged. The opposites attract, so the ligands bend to connect with opposite charges.

“The idea is, instead of defining a specific direction the bonds are going to occur in with complicated synthesis methods, we just said, ‘The bonds are going to be as flexible as they want to be, and they’ll spontaneously go to whatever valency is going to maximize bond stability,’” Macfarlane says.

In experiments led by Sbalbi, the researchers showed they could control the connections by changing the nanoparticle sizes and ligand chain lengths. They also used the technique to assemble the nanoparticles into previously unseen crystal structures.

“Without having to predesign where the particles connect or what their shapes are, we can control the number of bonds each particle has,” Macfarlane explains. “The amount of flexibility of the polymer dictates how many bonds it has. It allows us to lower the number of bonds each particle has and make lattices we have never seen before.”

A new path toward customizable materials

Macfarlane believes the system is a step toward getting custom nanoparticle-based materials out of the research lab and into the world.

“We in the nanoparticle assembly community talk about having completely independent control over what your particle is and what assembly it goes into,” Macfarlane says. “In theory, that should be the case. In reality, it absolutely has not been.”

More broadly, the researchers say their approach establishes ligand flexibility as a new path to making material structures that can be customized at scale.

“Nanoparticle superlattices offer a way to create designer materials where your building blocks have all sort sorts of unique properties,” Macfarlane says. “But just densely packing these nanoparticles together limits your applications. This offers a way to get less dense formations of nanoparticles, and that’s where a lot of the interesting applications are.”

The work was supported, in part, by the National Science Foundation, the Department of Energy, the U.S. Army and the U.S. Navy. This work was carried out, in part, through the use of MIT.nano's facilities.

Regina Barzilay and Jim Collins win Pritzker Prize for AI and Science Research Excellence for work in AI-enabled antibiotic discovery

Alex Ouyang | Abdul Latif Jameel Clinic for Machine Learning in Health — Mon, 02 Nov 2026 00:35:00 -0500

Regina Barzilay, MIT’s School of Engineering Distinguished Professor of AI and Health, and James Collins, MIT’s Termeer Professor of Medical Engineering and Science, are the recipients of the 2026 Pritzker Prize for AI in Science Research Excellence.

The award recognizes their joint work in “pioneering AI-enabled antibiotic discovery, integrating deep learning, bioengineering, and experimental validation to identify novel antibacterial candidates against drug-resistant pathogens.”

Established in 2024 by the University of Chicago in partnership with Caltech, the Pritzker Prize for AI in Science Research Excellence recognizes contributions that jointly advance AI and the natural sciences or engineering.

Barzilay and Collins, who are faculty co-leads of the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) and affiliates of the MIT Schwarzman College of Computing and the MIT School of Engineering, published their first breakthrough paper together in 2020 with then-MIT postdoc and first author Jonathan Stokes (who worked in the Collins lab from 2017 to 2021) in Cell. The paper described the first artificial intelligence-discovered antibiotic, called halicin, named for the fictional AI system HAL 9000 in Stanley Kubrick’s 1968 film 2001: A Space Odyssey.

A broad-spectrum antibiotic that was originally studied as a potential diabetes drug, halicin was identified by a deep learning model trained on 2,500 molecules. Upon testing, the research team found that halicin was able to kill antibiotic-resistant bacteria (i.e., “superbugs”) such as Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis. A discovery process that once took years was completed in just a few weeks. Amidst the growing alarm around the global antimicrobial resistance crisis, the results demonstrated that AI could prove to be an effective tool for rapidly identifying new and effective antibiotics.

Following halicin, Barzilay and Collins worked with Stokes (now an assistant professor at McMaster University) to use machine learning to identify abaucin in 2023, a narrow-spectrum antibiotic demonstrating effectiveness against A. baumannii, an antibiotic-resistant bacteria commonly found on hospital doorknobs and in the wounds of U.S. soldiers stationed in Iraq and Afghanistan. Notably, abaucin’s “narrow spectrum” efficacy against a single species of treatment-resistant bacteria is its main advantage, minimizing the likelihood of bacteria developing resistance and harming beneficial bacteria in the gut.

“Historically, antibiotic discovery has depended on laborious, trial-and-error strategies,” says Collins. “This process is slow, costly, and unpredictable, often yielding few viable antibiotic candidates. With generative AI, we're now seeing a shift from AI being used as a tool for discovery to AI being used as a design tool. This shift opens new frontiers in antibiotic discovery that could bring us the next generation of lifesaving drugs.”

In Barzilay’s view, “despite significant progress in molecular modeling, we are still in early stages of drug discovery. With AI, we can only automate parts of the pipeline, rather than totally transforming the discovery process. To make it happen, we need to work really hard to build realistic AI models of human biology.”

MIT in the media: “Unpack My Cambridge" with President Sally Kornbluth

Mon, 02 Nov 2026 00:00:00 -0500

Cambridge is booming with innovation, food, music, and history, highlighted in detail by the Cambridge Office for Tourism’s “Unpack my Cambridge” video series.

Reporting from Kendall Square – “the most innovative square mile on the planet” – MIT President Sally Kornbluth shares some of her favorite spots, and sweets, around the city.

To help you plan a visit in real life, more information on the different stops is below. You can find even more on the Cambridge Office for Tourism’s Kendall Square neighborhood page.

MIT Welcome Center

Kornbluth encourages visitors to begin your MIT journey with a tour at the Welcome Center, directly across from the Kendall Square T station.

On your tour you will visit landmarks such as the iconic MIT Dome and take a look at the many public art installations around campus. For prospective students and families, this is also an opportunity to learn more about the admissions process as you immerse yourself in the MIT community.

MIT Museum

Ready for some science? Kornbluth welcomes you to visit the MIT Museum, which has wonderful science exhibits, and is now home to MIT Future Fest.

The inaugural Future Fest, which MIT plans to hold annually, was a five-day festival exploring the future of science, technology, art and design through more than 50 public talks, tours, performances, exhibitions, installations, open laboratories, and more.

Throughout the year, admission to the MIT Museum is free for MIT community members as well as Cambridge residents. Current exhibits include “Sensing Oceans” and the original 33-inch Starship Enterprise from the opening credits of the “Star Trek” series, which is docked at the museum until March 28, 2027.

Harvard Bridge Smoot Markings

Kornbluth’s tour makes a stop at the Harvard Bridge, a short walk from campus, where you will likely see the words “Smoot” painted on the sidewalk, accompanied by a number between 1 and 364.4.

“I remember when I first visited Cambridge, I was walking across the bridge on Mass Ave, and I noticed these markings,” Kornbluth shares.

This standard distance measurement corresponds to the length of one Oliver Smoot ’62, an MIT graduate who, with the help of his fraternity measured the entire bridge using his height (5 feet 7 inches) in 1958. See if you can find the marking that adds an ear!

Charles River

Kornbluth invites you to “not only to enjoy Cambridge indoors but also enjoy Cambridge outdoors” on the banks of the Charles River, which runs parallel to MIT’s campus. She refers to the Charles as one of Cambridge’s ‘gems,’ complete with “walking paths, the bike paths, and kayaking opportunities.”

Thomas Tull Concert Hall

If you’re in the mood for some music, you’re in luck. “Turns out that the students at MIT, many of them are professional quality musicians,” Kornbluth says, as she highlights MIT’s own Thomas Tull Concert Hall, located inside the Edward & Joyce Linde Music Building on campus.

At Tull Hall, you can check out a concert by one of MIT’s jazz, classical, or contemporary ensembles, attend a talk from a guest speaker, or even catch a live DJ set.

Whichever performance you choose, you can expect some quality sound, brought to you in part by thoughtful details such as the building’s high ceilings and curved wall panels. “Using MIT's know-how, it was constructed for the best acoustics,” Kornbluth notes.

Eastern Edge Food Hall

With all the activities on and off campus, it’s easy to work up an appetite. As Kornbluth quips, “MIT is certainly in the food game.”

She recommends the wide, globally inspired selections available at the new Eastern Edge Food Hall, including tacos, burgers, and barbecue. Eastern Edge is located along Main Street and easily accessible from the Kendall/MIT station on the MBTA Red Line.

In the video, Kornbluth takes a moment to enjoy a chocolate vanilla soft serve twist from Eastern Edge’s Bacaro, a venetian inspired cafe and bar.

Ice cream and beyond

“If you like ice cream, Cambridge is a good place for you,” Kornbluth emphasizes. “I'd be remiss if I didn't mention Momma's Grocery for maple, Honeycomb Creamery, J.P. Licks. Lots of opportunities!”

Looking for more eats? A new page on OpenTable highlights restaurants throughout Kendall Square.

“We hope to see you here soon on a visit to MIT, whether you are a prospective student, whether you are a tourist or whether you are just someone who likes really cool things,” says Kornbluth.

Watch and share

“Unpack My Cambridge” with MIT’s Sally Kornbluth can be found on the Cambridge Office for Tourism’s LinkedIn, Facebook, and Instagram pages.

The social campaign also features reels with author Alice Hoffman; chef Jason Doo; former Mayor Denise Simmons; Mayor Sumbul Siddiqui, among other Cambridge denizens.

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MIT engineers design a safer, more efficient vaccination system for farmed fish

Zach Winn | MIT News — Mon, 02 Nov 2026 00:00:00 -0500

Fish that are cultivated in pools or ponds are an increasingly important source of food around the world. Unfortunately, disease outbreaks can cause substantial losses in aquaculture.

Fish farmers use vaccines to prevent disease outbreaks just like regular farmers, but vaccinating a fish is a lot harder than vaccinating a cow. The most effective way to vaccinate fish today uses needles, but there are several downsides to this process, both for the fish and the farmers.

Now a team of researchers at MIT and the U.S. Department of Agriculture has developed a potentially better way to deliver vaccines. The approach uses water-resistant, food-grade microneedles that can lightly penetrate and remain attached to fish skin before decomposing. In a new paper published in ACS Nano, the researchers described their system and showed it could be used to deliver a range of cargos, such as bacterial vaccines.

“To feed the world we need more fish, which are a major source of protein and nutrition,” says Benedetto Marelli, a professor in the Department of Civil and Environmental Engineering. “Aquaculture is a very effective way of producing a large amount of fish. Our approach could give farmers a more precise tool to maintain fish health. Farmers are really concerned about the health care of these fish, so providing them with better tools is really important.”

Joining Marelli on the paper are Meng Li, an MIT postdoc and first author on the paper; Jad Raad MEng ’24; Yunteng Cao PhD ’22, a postdoc at Yale University; Yue Hu, a former MIT postdoc; Colleen Wolfe MEng ’23; Doyoon Kim, a former MIT postdoc; Bradley Frank, a current MIT postdoc; and Gregory Wiens, a researcher at the U.S. Department of Agriculture’s National Center for Cool and Cold Water Aquaculture.

Better vaccine delivery

Aquaculture holds enormous potential to address food insecurity, reduce energy consumption, and decarbonize global food systems. In 2021, for the first time, farmed aquatic animals exceeded wild-caught aquatic animals in global food production by weight.

“The world’s population is going to grow by almost 2 billion people by 2050,” Marelli says. “Fish provide access to essential nutrition. They are high in protein and healthy fat, which are difficult to make, so the world is trying to produce more fish. But there are many challenges associated with fisheries.”

To prevent losses to disease, farmers use a range of automated and manual vaccination strategies, each with their own drawbacks. Some treatments can go into fish food, but those might not reach every fish. Another method is putting the treatment directly in a pool and immersing the fish in the treated water. That approach requires far higher quantities of treatment and, again, isn’t 100 percent effective.

The most effective way to vaccinate fish is with metal needles. The process requires farmers to sedate the fish, then get them out of the water and onto a conveyor belt, where workers hurry to vaccinate the fish and get them back in the water to minimize harm.

“Workers inject fish one by one,” Li explains. “It’s high intensity, there’s a risk of cross contamination of blood-borne diseases, and workers may accidentally jab themselves because they are working so quickly. So it’s labor intensive, costly, stressful for fish, and dangerous for workers.”

Marelli’s lab has been developing silk microneedles to deliver nutrients to plants for years. The silk has been classified as safe for human consumption in food coatings and can degrade in the body or in the environment over time.

A few years ago, Marelli learned about the challenges with vaccinating fish at a conference in Southeast Asia, where aquaculture is an especially important part of the aquatic food supply. His lab received support from the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) to explore adapting the silk platform he’d developed for underwater use for fish. But the early silk designs would quickly dissolve when exposed to water, releasing the vaccine immediately and failing to penetrate fish skin.

Li, a biomedical engineer, started to work on the project in Marelli’s lab beginning in 2022. In 2024, she received a fellowship from the USDA, which provided funding as well as important connections with collaborators at the agency and beyond. For example, the U.S. Geological Survey gave the researchers access to a DNA vaccine for rainbow trout.

Marelli credits Li with finding a way around silk’s tendency to soften when exposed to water. The system described in the new paper uses a thin layer of shellac, an insect-secreted resin used in food coating and pills, to make silk microneedles water-resistant by providing a moisture barrier that remains intact before fish injection.

“The material choice is critical because we’re dealing with food, even though we expect the microneedle to fall out of the fish or to be degraded in their body,” Li says. “Our criteria were safety for fish and humans alike.”

The researchers showed the microneedles can store a range of vaccines, and they demonstrated different versions of the microneedles that lasted longer in the water or diffused vaccines more quickly.

“In some cases, you may want the vaccine released within 15 minutes, but in others, the release can last 10 or even 24 hours,” Li says. “That could be better for other therapeutic uses beyond vaccines.”

The microneedles were also able to preserve half of the DNA plasmids’ structure when stored for four weeks in dry conditions.

“That stability is important, particularly for the last mile of delivery, because aquaculture is mostly done in rural areas,” Marelli says. “This allows farmers to not need to rely on the infrastructure to maintain the cold chain.”

Protecting fish

Just like terrestrial animal vaccines, fish vaccines are most effective when administered early in life before disease exposure. That means the fish that farmers want to inject are small, further complicating vaccination. The researchers believe their approach could offer a better way to vaccinate small fish, and they tested it in rainbow trout.

“In trout aquaculture, one of the biggest challenges we face is preventing disease early in a fish’s life, because traditional needle‑based vaccination just doesn’t work well until the fish reach about 30 grams, which leaves the smallest juveniles unprotected during a critical growth period,” says Wiens. “What our research shows is food‑grade silk fibroin microneedles can deliver vaccines underwater to fish as small as 1 to 2 grams. We designed these single‑use microneedles to pierce wet skin, stay attached while the fish swim, and release either bacterin or DNA vaccines in a controlled way.”

The researchers say there is still work to be done before their device can be seamlessly used at scale in fish farms. But they believe this paper is an important first step.

“We have all the components to believe this technology could be developed further,” Marelli says. “There are so many challenges with aquaculture and fish vaccination right now. We need to support farmers to operate safely, improve working conditions, protect their business, make vaccination more accessible, and improve the living conditions of farmed fish and ultimately the quality of the food we eat. This new technology could be a much more effective and safer approach.”

The work was supported, in part, by MIT’s Abdul Latif Jameel Water and Food Systems Lab and the U.S. Department of Agriculture.

MIT affiliates named 2026 Schmidt Sciences AI2050 Fellows

Fri, 30 Oct 2026 16:15:00 -0400

Five MIT faculty members and two additional alumni were recently named to the 2026 cohort of AI2050 Fellows. The honor is announced annually by Schmidt Sciences, Eric and Wendy Schmidt’s philanthropic initiative that aims to accelerate scientific innovation.

Conceived and co-chaired by Eric Schmidt and James Manyika, AI2050 is a philanthropic initiative aimed at helping to solve hard problems in artificial intelligence. Within their research, each fellow will contend with the central motivating question of AI2050: “It’s 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome?”

AI2050 Senior Fellow

Sendhil Mullainathan is the Peter de Florez Professor at MIT with dual appointments in the departments of Economics and Electrical Engineering and Computer Science who is also a principal investigator at the MIT Laboratory for Information and Decision Systems. His current research is at the intersection of algorithms and people, with a central focus on launching The Bike Shop@MIT, a new research center that works toward more human-centered AI through scalable bicycles for the mind, and innovative new gears for machine learning algorithms. Mullainathan is the co-founder of ideas42, a nonprofit to apply behavioral science; MIT's Abdul Latif Jameel Poverty Action Lab, which promotes the use of randomized control trials in development; and Dandelion Health, which combines proprietary AI tools with real-world patient data. He's the co-author of "Scarcity: Why Having Too Little Means So Much."

Among other awards, he is a recipient of the MacArthur “Genius Grant”; has been designated a “Young Global Leader” by the World Economic Forum; was labeled a “Top 100 Thinker” by Foreign Policy Magazine; and was named to the “Smart List: 50 people who will change the world” by Wired Magazine (U.K.). He serves on the board of the MacArthur Foundation, is affiliated with the NBER and BREAD, and is a member of the American Academy of Arts and Sciences. His AI2050 project focuses on the translation of patterns detectable by AI into testable theories.

AI2050 Early Career Fellows

Alex Damian is an assistant professor in the Department of Mathematics, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science, who is also a principal investigator at the MIT Laboratory for Information and Decision Systems. His research focuses on the mathematical foundations of deep learning, with an emphasis on optimization dynamics and representation learning. He was previously a research fellow at the Kempner Institute at Harvard University. He received his PhD in applied and computational mathematics from Princeton University and his BS in mathematics from Duke University. His AI2050 project focuses on effective AI model training.

Bailey Flanigan is a Theodore T. Miller (1922) Career Development Assistant Professor in the Department of Political Science, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS). She received her PhD in computer science from Carnegie Mellon University in 2024. In 2024-25, she was a Harvard Data Science Initiative Fellow, hosted at the Harvard Ash Center for Democratic Innovation. Her research combines tools from across disciplines (including social choice theory, game theory, algorithms, statistics, and survey methods) to advance political methodology and strengthen public participation in democracy. She is specifically interested in sampling algorithms, opinion measurement/preference elicitation, and the design of democratic innovations like deliberative minipublics and participatory budgeting. Her AI2050 project focuses on gathering structured public input that lawmakers can use when making complex decisions.

Yunha Hwang is the Samuel A. Goldblith Career Development Assistant Professor in the Department of Biology, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science. She is also a principal investigator at the MIT Laboratory for Information and Decision Systems. Hwang serves as the co-founder and chief scientist at Tatta Bio, a scientific nonprofit dedicated to advancing genomic AI for biological discovery. She received her PhD in biology from Harvard University and BS in computer science from Stanford University. Her research interests span machine learning for microbial evolution, biomanufacturing, and open science. Her AI 2050 project focuses on determining biological enzyme functions.

Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science. He is also a principal investigator at the MIT Laboratory for Information and Decision Systems. Before that, he was a postdoc at the Harvard Center for Research on Computation and Society. Raghavan received his bachelor’s degree in electrical engineering and computer science from the University of California at Berkeley, and PhD from Cornell University. His research interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. His AI2050 project focuses on mitigating negative effects, and strengthening positive aspects, of widespread AI reliance.

MIT alumni Irene Chen PhD '22 and Allison Z. Koenecke '14 as well as former postdoc Juan Carlos Perdomo were also named AI2050 Early Career Fellows.

J-PAL North America launches ARTEMIS to train early-career researchers

Katherine Rodriguez Marquez | J-PAL North America — Fri, 30 Oct 2026 15:30:00 -0400

The Applied Research Training in Evaluation, Methods, and Impact Studies (ARTEMIS) program, launched by J-PAL North America, equips early-career researchers with high-level technical training and practical skills to design randomized evaluations (also referred to as a randomized controlled trials, or RCTs) on issues of poverty in the U.S. context. Fully funded and highly rigorous, the program's inaugural cohort of 20 participants — including PhD students through pre-tenure faculty — is receiving training, mentorship, and proposal development support through December.

ARTEMIS is the newest offering within J-PAL North America’s Economics Pathways program, which broadens and strengthens the pipeline of economics researchers by providing support to aspiring scholars at every stage — from undergraduate interns to research staff and, now, academic researchers preparing to launch their first randomized evaluations.

ARTEMIS programming commenced this past June with the Summer Institute — an intensive weeklong course at MIT Endicott House in Dedham, Massachusetts, that focused on delivering technical content and building participants' practical skills and confidence to design and pursue funding for rigorous evaluations to address poverty in the United States. During the Summer Institute, participants had the opportunity to learn from researchers in the J-PAL network, including Gerald Daniels, associate professor of economics at Howard University, who served as a lecturer for the program. “ARTEMIS is an amazing program that bridges the gap for researchers who haven't previously had the opportunity to work with randomized evaluations,” notes Daniels. “It takes rigorous methodologies, like randomized evaluations, and makes them approachable, helping scholars translate their research ideas into high-impact evaluation designs."

The inaugural cohort brought together early-career researchers from institutions across the United States, each tackling a research question addressing an issue related to poverty that is well-suited for a randomized evaluation. Their interests spanned the breadth of J-PAL’s work, touching on education, climate, health and labor markets, racial equity, and social protection — reflecting the wide range of policy questions that rigorous, evidence-based research can help answer.

“I really appreciated being a part of the ARTEMIS Institute this summer,” says Felipe Juan, an ARTEMIS inaugural cohort participant. “I initially thought the randomized evaluation methodology would be out of reach for me, but the summer training helped to break down the practical steps of conducting an RCT and identify creative ways to approach experimental designs.”

Bolstered by the intensive Summer Institute training, ARTEMIS participants are now engaging in six months of continued study design and proposal development support, including proposal webinars with J-PAL grants staff and office hours with senior technical staff who will advise on study design and proposal challenges. ARTEMIS participants will also participate in a research design workshop, where participants present their early-stage RCT designs and receive feedback from the broader J-PAL research network. Finally, participants will receive introductions to researchers in the J-PAL network with shared research interests to forge connections that may lead to future collaboration and opportunities. Through this sustained mentorship model, ARTEMIS is designed to help participants bridge the gap between developing a promising research idea and launching a funded, policy-relevant randomized evaluation.

For early-career researchers, this hands-on, sustained support has already proven to be transformative. “Participating in ARTEMIS Summer Institute was an incredibly valuable experience. The combination of lectures by experts in the design and implementation of randomized controlled trials, hands-on workshops, and thoughtful feedback helped me sharpen my research questions and think more carefully about research design, implementation, statistical power, and meaningful stakeholder engagement,” says Hoyoung Yoo, an ARTEMIS 2026 participant. “I also greatly appreciated the opportunity to learn from J-PAL faculty and staff and exchange ideas with an inspiring group of fellow researchers. I left the training with a clearer path toward transforming an early-stage idea into a feasible, rigorous, and policy-relevant evaluation.”

For many, the program’s value also extends beyond technical training to the community and long-term relationships it builds. “J-PAL North America’s support has continued well beyond the Summer Institute, which has been especially valuable, and the connections I made have already started shaping how I am approaching the work,” notes Tessie Krishna, an ARTEMIS 2026 participant. “I left with a clearer proposal, a wider network, and a real sense of belonging in the J-PAL community. I am grateful to the J-PAL team for creating something so thoughtful and welcoming.”

By building the technical capacity and confidence of the next generation of researchers, ARTEMIS is advancing J-PAL’s global mission to reduce poverty by ensuring that policy is informed by rigorous, scientific evidence. Every individual trained through ARTEMIS is joining a growing network of researchers working to answer the most critical questions of today.

ARTEMIS is made possible by grants from Arnold Ventures, Russell Sage Foundation, and the Alfred P. Sloan Foundation. Visit the J-PAL website for more information about ARTEMIS and the Economics Pathways program.

Study pinpoints states with steepest warming trends since 1980

Mark Dwortzan | Center for Sustainability Science and Strategy — Fri, 30 Oct 2026 15:15:00 -0400

Global warming, the observed increase in the Earth’s average global surface temperature since preindustrial (1850-1900) times, currently stands at around 1.63 degrees Celsius. While this number is expected to continue to rise, local temperatures will not go up uniformly around the world. In regions where warming trends are most pronounced, local adaptation and resilience measures will be especially critical to minimizing risks to ecosystems, agricultural productivity, the built environment, human health, and economic well-being.

To that end, a new open-access study in the journal Scientific Data identifies the highest-risk regions for warming within the United States. Using data from well-known, high-resolution climate and weather datasets (MERRA2 and ERA5) covering the years 1980-2022, researchers at the MIT Center for Sustainability Science and Strategy (CS3) report global warming-attributed trends in monthly-averaged maximum daily temperature for every U.S. state.

The study finds that Alaska, Utah, New Mexico, Colorado, and Nevada have experienced the strongest average warming trends; the lowest occurred in Hawaii, Montana, North Dakota, South Dakota, and Arkansas. Across the country, warming trends were most pronounced during the summer and fall, particularly in September and November. Alaska has the highest historical maximum daily temperature warming (an average of 2.5 C of monthly-averaged maximum daily temperature increase per degree of global warming), while Hawaii has the lowest average historical warming, at 0.8 C.

The authors expect that barring unforeseen local anomalies, these state-by-state temperature trends will continue unabated as ongoing global emissions from human activities continue to raise the average global temperature.

“Based on these results, we encourage decision-makers to assess states that have undergone the steepest increases in maximum and minimum daily temperatures, and identify where continued trends would indicate a rapid progression toward thresholds beyond which natural and managed resources, ecosystems, and infrastructure can function safely,” says MIT CS3 Deputy Director C. Adam Schlosser, a co-author of the study. “In such cases, triaging and adaptive measures would take a high priority. By directing resources to where they are needed most, state and regional planners could significantly reduce climate-related physical and financial risks.”

Unlike previous efforts to report temperature trends across the United States, this study aggregates average maximum, minimum, and mean daily temperature data at the state level for each month of the year, providing actionable information for planners in government, industry, and civil society.

Q&A: Hey AI, who should I vote for this year?

Abby Abazorius | MIT News — Fri, 30 Oct 2026 15:00:00 -0400

People are peppering artificial intelligence chatbots everyday with questions, but when it comes to politics and the looming 2026 U.S. midterm elections, what role are AI platforms playing? Could an AI chatbot sway a voter to switch parties or re-examine their political beliefs?

MIT researchers have built a new dashboard, called the LLM Election Observatory, to examine, in real-time, how popular AI chatbots are responding to questions about the 2026 midterm elections. The team behind the dashboard — led by Professor Adam Berinsky, Assistant Professor Chara Podimata, and Professor Charles Stewart III — has extensive experience studying voting and election administration, AI and misinformation, and how AI systems are impacting human decision making. In an effort to better understand how AI is shaping the election, they designed the dashboard to simulate how individuals across the country communicate with chatbots.

The team tracks how different AI systems respond to the same fixed set of questions on a range of topics, including election certification, hot-button issues, result predictions, news coverage, and more. Every few days, the LLM Election Observatory team runs the questions through the chatbots again, monitoring how their responses vary as the election gets closer depending on user location, political party affiliation, identity and other factors. The goal of this work is not to merely understand how AI systems spread information, but also better comprehend how they are shaping political discourse and voter understanding of political issues across the country.

MIT News chatted with Podimata, the Class of 1942 Career Development Professor, an assistant professor of operations research and statistics in the MIT Sloan School of Management, and a member of the Laboratory for Information and Decision Systems; and Berinsky, the Mitsui Professor and a professor in the Department of Political Science, about their work with the LLM Election Observatory, how AI could impact the midterms, and what voters should keep in mind when they interact with chatbots.

Q: What was the inspiration for this project and what are you hoping to learn through the LLM Election Observatory?

Podimata: People are increasingly using AI chatbots to find and make sense of political and election-related information. That makes it important to understand what information these systems actually provide, not just at one moment in time but throughout an election cycle. This is particularly challenging because the models themselves are continually getting updates, the political information inputs available to them are themselves changing, and their answers can depend on how a user asks a question or what information the user provides about themselves.

One basic problem is that we currently do not have a good historical record of this information environment. If an AI system gives a particular answer about an election today and gives a different answer several weeks later, it can be very difficult to reconstruct after the fact what voters would have seen at each point in time. One important goal of our project is simply to systematically document that environment as it develops and make the resulting data available to other researchers and to the public.

Ultimately, factual accuracy is only the first frontier. We absolutely need to understand whether chatbots provide accurate political information, but we also need to pay just as much attention to the subtler ways in which they may shape people’s understanding of political issues. Differences in framing, emphasis, uncertainty, and cited sources can matter enormously, even in the absence of outright misinformation.

Q: Professor Podimata, you studied how LLMs shaped political discourse and information during the 2024 election. What were the main takeaways of that research and how did those findings inform this new project?

Podimata: The LLM Election Observatory builds on my previous work during the 2024 presidential election, which found evidence of meaningful variation in AI models’ responses over time and across prompt framings, as well as differences in how models characterized candidates and implicitly anticipated the election outcome.

The 2026 midterms give us an opportunity to expand that work substantially. Midterm elections are particularly interesting because there are many state and local contests unfolding simultaneously, alongside broader national political narratives. Our new audit survey therefore asks both contest-specific questions about the U.S. House and Senate candidates, but also broader questions about voting information and other aspects of the election information environment.

Q: Why do you think it’s so important to be studying how chatbots are responding to political queries across different groups of users?

Podimata: We all build our own “microcosms” through our interactions with chatbots. And yet, we lack systematic ways of understanding how these individualized information environments are shaped by who we are or how they evolve over time as we and the information environment changes.

For example, people may not realize that what their favorite chatbot tells them about a political issue may be quite different from what their friend’s favorite chatbot tells them about the exact same issue. And importantly, these differences matter even when the information being provided is factually correct. Chatbots may frame the same facts differently, emphasize different aspects of an issue, express different levels of uncertainty, or draw on entirely different sources, even when responding to identical queries between different users. As a result, two users may walk away with very different perceptions of the same underlying facts, without ever realizing that their experiences differed.

In some ways, this brings back many of the concerns we (as researchers and society) have grappled with in the context of social media. On social media, users and researchers could, at least to some extent, observe the information circulating across different Facebook groups, Instagram accounts, or TikTok communities. With chatbots, these information environments are largely invisible to anyone outside the individual conversation. We cannot easily observe what different users are being told, let alone systematically compare those responses or track how they change over time. This is precisely why projects like the LLM Election Observatory are so important: By systematically collecting and publicly sharing chatbot responses, we can begin to make these otherwise invisible differences observable and open to scrutiny.

Q: What sorts of questions and AI responses are you tracking through the dashboard?

Podimata: There are several questions we ultimately want this dataset to help us study. For example: Do different models provide systematically different information for the same questions? Do their responses change over the course of the campaign? Does the answer a particular model gives change depending on how a user frames the question or describes themselves (e.g., does it change if the model gets informed that the user is a Democrat or a Republican)? And, finally, what are the sources that the models pull information from when answering election-related questions?

Our public dashboard is an important part of the project. We want people both from and outside of academia to be able to see the data we are collecting as the election unfolds rather than only seeing the results months later in an academic paper. The dashboard therefore presents the underlying data and some preliminary descriptive analyses, and we plan to make the dataset available for other researchers to study as well. We expect the dashboard to evolve as the data and our methods evolve.

Through this dashboard, we wanted to build on the scientific methodology we developed in 2024 but also to make the research much more accessible and useful to the public while the election is actually unfolding. Not only that, but the addition of MIT political science experts in the team cannot be overstated; Adam and Charles have really helped expand the questions that we are asking in our audit.

Q: What do you wish voters across the country knew about AI chatbots when it comes to searching for election information?

Berinsky: People should be sure to remember that just because a chatbot sounds confident doesn’t mean it is accurate. AI systems are designed to answer the questions that people ask them in an authoritative voice even when they are wrong, incomplete, or leaning on a weak source. These answers are a reasonable place to start, but voters shouldn’t stop there.

Q: What are your findings thus far?

Podimata: We have not yet done the full, longitudinal analysis, so we are only going to talk about broad strokes here, but a few broad patterns seem to be visible in the 2026 data. Across the different user identities we present, we see differences in how the answers are presented, in which sources a model cites, and in tone — sometimes even when the underlying facts are the same. What we cannot yet say is how stable those differences are, how they move over the course of the election, or how a real person would read and react to them. Our future analyses will take this up. It also underscores why the continuous record of responses we are collecting matters more than any single snapshot that you might see in the days around the election.

Q: Professor Berinsky, your research has also shown that in some cases AI chatbots can be harnessed to help refute misinformation. Can you talk a bit about this?

Berinsky: In my undergraduate course on information and social media I have been talking for a while about how AI can cut both ways. On one hand, these tools can generate misleading claims in text, pictures, and video at a scale we have never seen. Basically, AI can help bad actors “flood the zone” with misinformation. On the other hand, some of the most promising recent work demonstrates that evidence-based dialogues with a chatbot can durably reduce belief in conspiracy theories and other beliefs that are not supported by the balance of publicly available evidence.

AI has the potential to do such debunking at scale — something we have long struggled to do. The lesson from decades of misinformation research is that any technology (be it print, radio, or television in earlier eras) is not inherently good or bad for promoting truth. The devil is in the details in how it is built and deployed. Independent auditing like this project can help us assess the ultimate impact of such technologies.

Q: Can you talk a bit about your overall goal with this research?

Podimata: AI is a transformative technology, and people understandably have very different expectations (and feelings) about what it will mean for society at large. All of us have people in our lives that are overly enthusiastic about AI, others that are deeply concerned, and many more that are somewhere in between trying to understand what this technology can and cannot do. Our goal here is not to begin from the assumption that AI is either good or bad for elections. Instead, we hope to shed some light onto one part of this transformation: what information major AI systems are actually giving people about an election as it unfolds.

MIT engineers create nanoparticles for delivering personalized cancer vaccines

Anne Trafton | MIT News — Fri, 30 Oct 2026 00:30:00 -0400

A nanoscale particle with a unique structure, developed at MIT, holds potential as a customizable delivery vehicle for personalized cancer vaccines.

The backbone of the vaccine is a modified, harmless bacteriophage called M13, which forms a nanofiber that can be coated with cancer antigens. By precisely controlling the density of those cancer proteins, the researchers found that they could create a vaccine that prevents the development of colon tumors in mice.

This type of delivery particle could be adapted to carry many different antigens, allowing for the creation of personalized vaccines to treat individual patients’ tumors, the researchers say.

“The great thing about this is that it’s modular and we could switch out different antigens on demand, so you could design it to be patient-specific and target a known antigen for a particular kind of cancer,” says Angela Belcher, the James Mason Crafts Professor of Biological Engineering and Materials Science and Engineering and a member of the Koch Institute.

Belcher is the senior author of the study, which appears today in the journal Advanced Healthcare Materials. Shengnan Huang, research scientist at the Koch Institute, is the paper’s lead author. Jack Rudrum, Huaiyao Peng, and Hao Nghi Luu, graduate students at the Koch Institute, are also authors of the paper.

Viral vaccine assembly

For more than 20 years, Belcher’s lab has been deploying the M13 bacteriophage in a variety of applications. This virus, which is harmless to humans, consists of a single strand of DNA coated in a protein shell, or capsid.

Belcher has previously developed techniques for generating materials by engineering these viruses to self-assemble into nanoscale scaffolds. By coating these scaffolds with inorganic materials, they can create functional devices such as batteries, semiconductors, solar cells, and catalysts, with precise control over the electronic, optical, and magnetic properties of the materials.

More recently, Belcher’s lab has been developing methods to control features of the phage that could allow it to be repurposed for medical applications, including vaccine delivery.

“We’re taking this toolkit that we’ve worked on and developed over decades and formatting it to be specific for vaccine therapeutics,” Belcher says. “Some of the lessons learned from making batteries and all these other things are now being fine-tuned to make personalized vaccines.”

The capsid that surrounds the M13 phage forms a long filament made from viral proteins. To create vaccines, the researchers modify one of these proteins, known as pVIII, by attaching an antigen to the end of the protein. This can be done by adding the gene for the antigen to the virus’s DNA.

Some of the lab’s previous work had suggested that changing the density of cancer antigens attached to the capsid could affect the strength of the resulting immune response. In the new study, Huang wanted to find a way to control that density.

“The antigen is the specific target we want to initiate the immune response against, which means we have to ensure the right dose of antigen to make sure that we can have a strong immune response,” she says.

Huang discovered that she could increase the density of the modified pVIII proteins by adding one or two negatively charged amino acids to the protein sequence. The density could be further fine-tuned by adding a genetic mutation that slows down production of the antigen protein.

In this study, the researchers also figured out how to boost the phage’s own ability to act as a vaccine adjuvant, by changing the genetic sequence of the virus’s DNA. Most vaccines need to be delivered along with an adjuvant such as aluminum hydroxide, which helps to stimulate a strong immune response. However, the M13 phage can act as an adjuvant itself because single-stranded DNA activates a pathway called TLR9, which is part of the body’s innate immune system.

“We can control the exact length. We can control the exact antigen expression, density, pattern, the exact adjuvant amount. A lot of times with a vaccine, you’ll have to put a different adjuvant with it, but now it’s all genetically encoded,” Belcher says.

Targeting tumors

Using these techniques, the researchers showed that they could design phages that express an antigen called SIINFEKL at densities ranging from 6 percent to 70 percent. SIINFEKL, a small protein that can be engineered into tumors, is often used as a model to test vaccine effectiveness.

The researchers tested two of these vaccines in mice, one with 6 percent density and one with 35 percent, and found that the one with 35 percent was the most protective.

After vaccination with the higher-density particles, mice were much less likely to develop tumors when later exposed to melanoma cancer cells expressing the SIINFEKL antigen. These mice also generated more T cells targeting SIINFEKL. This is, in part, because dendritic cells, which help to activate T cells, were more likely to take up vaccines with higher antigen density, the researchers found.

In another set of experiments, the researchers created and tested a vaccine carrying a neoantigen, which is only present in the tumor cells. This protein, known as ADPGK, is specifically expressed by colon tumors in a mouse model of cancer. When given the higher density vaccine, these mice were much less likely to develop colon tumors.

In this study, the researchers tested the vaccine as a way to prevent cancer from developing, and in their previous work, they adapted it for use to treat mouse colon tumors that have already formed. In the future, they also hope to use it to combat other types of cancer.

“This interesting work adds another cog to improve the translational potential of the M13 phage platform for cancer immunotherapeutics,” says Alberto Danielli, an associate professor of molecular biology at the University of Bologna, who was not involved in the study. “I look forward to seeing whether this platform is also effective against established tumors and whether additional tumor antigens can be displayed with comparable molecular and immunological efficacy.”

This platform could be especially well-suited for personalized cancer vaccines, because many different antigens could be swapped into the vaccine.

“Because this is a modularly engineered platform, we can change the antigen we are interested in. For colon cancer, we use this specific antigen. For other types of cancer, we can just plug in other antigens and they can be assembled in this platform,” Huang says.

The research was funded, in part, by the Koch Institute Frontier Research Program, the Marble Center for Cancer Nanomedicine, and the U.S. Defense Advanced Research Projects Agency.

Next Haunt: A scary “problem set” in the basement of Next House

Sarah Foote | Division of Student Life — Thu, 29 Oct 2026 11:55:00 -0400

You know it’s autumn when the scent of pumpkin spice latte begins to waft through the air at the Dunkin’ in the Stratton Student Center, and students in MIT’s Next House residence hall diligently begin to piece together the many parts needed to showcase Next Haunt — a combination of a Halloween haunted house and escape room, built in the basement of the hall.

Every fall since 2014, students — also known as hauntmasters — spend several weekends building the room and preparing puzzles, physical tasks, or word challenges to obtain a code to open locks that let participants escape the house. This year’s theme, titled “Where the Light Ends,” follows “Terror Under Destruction,” “Next Stop: MBTA,” and last year’s theme: “Get in the HOLE.”

Next Haunt is free and open to all MIT students, also known as “test subjects.” Participants typically spend around 20 minutes in small, dark, enclosed rooms, working with four to six teammates to find the clues they need to escape.

Along the way, they have to navigate zombies — played by student actors — who may shout at them or splash them with blood (fake) while they solve the puzzles. If a zombie touches a participant, they become “paralyzed” and can no longer move, but they can still communicate and relay information to their teammates.

Teams must work closely, strategize, delegate tasks, and communicate effectively as the clock counts down. The group with the quickest escape time gets bragging rights, and every group signs their names on a wooden board to show that they participated.

MIT junior Kiyoko Hayano, Next Haunt co-producer, says, “I became involved in Next Haunt during my first semester at MIT because I had experience with larger-scale painting. I split the build director’s role with two other freshmen, including Michelle Liang, our current assistant producer. I thought this would be a great opportunity to take a break from psets and work with my hands, and fell in love with the club.”

Safety first

Before the room is built and the puzzles and costumes are created, careful planning takes place to ensure the escape room is safe, fire-resistant, and passes an on-site inspection. MIT’s Campus Construction and Environment, Health, and Safety offices are involved from the start, and all students who partake in the build are required to attend safety orientation and tool training.

“We currently have 18 people on our executive team, and we have 12 outside volunteers come help over the first few weekends of Next Haunt build,” says Hayano. “We then run Next Haunt for six-and-a-half hours every night for two weekends at the end of October.”

Once the build is complete, several alumni volunteers conduct a “test run” to make sure the puzzles and clues are hard, but not impossible, and anyone who wants to leave the room before the allotted time can easily find the door out built with Velcro closures.

Escaping by solving the puzzles is not easy, and not all participants are successful. Last year, only 16 percent of the 73 teams successfully got out of the room. The winning team escaped in just under 15 minutes.

Alumnus and previous Next Haunt producer Allen Lin ’26 notes, “I became involved with Next Haunt in my freshman year after being a test subject myself, and I loved the creative and building process so much I wanted to join. My favorite part was seeing all the teams go through the house, especially on the last day when we hosted past Haunt alumni. It was just as rewarding to see how much our test subjects enjoy the house as it was to share the many laughs and moments of camaraderie among my team. I’m excited to come back as an alum and see what the Next Haunt team built this year!”

Environmentally mindful

At the end of Next Haunt, the escape room is dismantled, and anything that can be repurposed safely is stored for the following year’s build. The executive crew then goes to a Boston-area escape room to celebrate their hard work.

“It’s a joy working collaboratively with students and connecting them to campus partners who lend their expertise as the build develops," says Milo Alto, manager of special projects, housing and residential services. “Students get very creative every year with the theme and costumes, but they also learn valuable leadership skills along the way — time management, delegation, working with peers, and implementation of a detailed plan. It’s also a great way to build community.”

Interested MIT students can sign up to help plan and work on the 2027 build.

Magnetic sensors track muscle movement, for better prosthetic limb control

Anne Trafton | MIT News — Thu, 29 Oct 2026 06:00:00 -0400

MIT researchers have shown that they can accurately track muscle movement using tiny magnetic beads implanted in the muscle. These measurements could make it easier for people with certain types of amputations to control their prosthetic limbs.

For decades, researchers have been working to develop neuroprosthetic limbs, which use neural information from muscles remaining after an amputation to guide the prosthetic limb. Measurements of a muscle’s length, velocity and activation can be fed into the control algorithms that guide the limbs, allowing individuals with leg amputation to walk with a more natural gait, or those with arm amputation to manipulate objects more effectively.

Until now, these devices have relied on muscle activity measurements from electrodes placed on the surface of the skin. However, those measurements have not proven reliable enough to ensure safety while walking with a neuroprosthetic leg.

The MIT team hopes that their new magnetic tracking system, which offers more accurate measurements, will enable commercial development of such limbs.

“For four decades, the standard methodology for neurally controlling prostheses has been to use skin electrodes to measure muscle activation. However, to control a prosthetic limb, joint position, torque, or impedance has to be controlled, and one cannot directly determine such mechanical signals using electrical activation alone,” says Hugh Herr, a professor of media arts and sciences, co-director of the K. Lisa Yang Center for Bionics at MIT, an associate member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

In the new study, which appears today in Nature Sensors, the researchers demonstrated the accuracy of the magnetic measurements in three participants with below-knee amputations.

“This interface is minimally invasive, so it could be accessible to more people. You place two beads in the muscle and you have access to all of this rich information about the muscle dynamics,” says Christopher Shallal, an MIT graduate student in the Harvard-MIT Program in Health Sciences and Technology and one of the lead authors of the paper.

The paper’s other lead author is Cameron Taylor PhD ’20, an assistant professor of biomedical engineering at the University of North Carolina at Chapel Hill.

Tracking muscles

For many years, Herr’s lab has been developing human-machine interfaces that can be used to control an artificial limb in a way that makes it more like using a natural limb. Obtaining accurate measurements of how the user is moving the muscles at the amputation site is critical to the success of this type of prosthesis.

Electrodes on the surface of the skin can reveal the electrical activation of the muscle below, and those measurements can be converted to estimates of how much a muscle is contracting or flexing. Based on that input, a robotic controller generates commands that guide the prosthetic limb in the user’s intended direction.

However, that measurement approach, known as electromyography, does not always provide accurate enough information. The electrical signal is not a perfect proxy for muscle dynamics; for example, when a muscle is held at a fixed length for several seconds, the signal fades. Additionally, the electrodes may pick up signals from muscles other than the intended target, and sweat can interfere with detection of the signal.

Last year, Herr and his colleagues reported a “bionic knee” prosthesis that uses electrodes implanted in the muscles to provide accurate measurements and feed them into a robotic controller. This approach proved successful in a small clinical study, but alongside that work, Herr’s team has also been working on a less invasive approach that relies on magnets instead of electrodes.

With this approach, known as magnetomicrometry, pairs of magnets are implanted into muscles. When a muscle contracts or stretches, external sensors placed on the skin can measure the change in distance between the magnets, allowing muscle length and velocity to be calculated.

“Magnets can be tracked wirelessly through the body, so you can get very specific muscle signals and measure the mechanical output of the muscle without needing to be extremely invasive,” Shallal says.

Better control

In a 2021 study led by Taylor, Herr’s lab first demonstrated the magnet-based technique in animal models. The new Nature Sensors study extends the approach to humans with long-term magnetic implants. For the study, the researchers recruited three participants who have below-knee amputations.

All of these participants had a type of amputation that creates what’s called an agonist-antagonist myoneural interface (AMI). With this surgical procedure, muscle pairs are reconnected during surgery so that they still dynamically communicate with each other within the residual limb. This sensory feedback, called proprioception, helps the wearer of the prosthesis to decide how to position and move their prosthetic limb.

Participants had magnets implanted in up to four muscles of the ankle joint. The surgeries were performed at Brigham and Women’s Hospital in collaboration with plastic surgeon Matthew Carty, a co-author of the study. The beads are 3 millimeters in diameter, about the size of a sesame seed. The magnets are made from an alloy of neodymium, iron, and boron, and are coated with gold and a polymer called parylene C to make them biocompatible. The magnetic signals can be read from a depth of up to 6 centimeters from the skin’s surface, using a customized external sensor that is worn over the skin, positioned over the muscles that are being tracked.

Each of the participants in this study also had electrodes implanted into the same muscles that received magnetic beads. This allowed the researchers to compare the accuracy of three different types of measurements: magnetomicrometry, electromyography with implanted electrodes, and electromyography with electrodes on the surface of the skin.

During the experiments, the participants were not wearing a prosthetic limb but were asked to perform tasks such as moving a cursor on a screen to a particular target using their muscle movements. Using each of the three measurement techniques, the signals generated by the muscle movements were translated into control signals that, in this case, moved the cursor but in the future could be fed into the controller of a robotic ankle-foot prosthesis.

“The cursor on the screen represented the degrees of freedom that they would control on a robotic ankle-foot prosthesis. This virtual neuroprosthetic task allowed us to compare the performance across different neural interfaces, and our results suggest that the magnet-based approach can offer more accurate and stable control,” Shallal says.

The researchers are now performing a larger study in which they are evaluating how well people can control prosthetic limbs using measurements from the magnetic beads.

The research was funded, in part, by the Yang Tan Collective at MIT as well as a National Science Foundation Graduate Research Fellowship and an MIT HEALS Graduate Fellowship.

New system gives users more control over their online experience

Adam Zewe | MIT News — Thu, 29 Oct 2026 00:00:00 -0400

A user who is frustrated with changes a social media company made to their favorite messaging platform, like the introduction of an annoying AI chatbot, can’t do much to address the situation, short of leaving the platform.

Now, MIT researchers are offering an alternative path.

They’ve created Social.Wiki, a system that allows users to easily edit any social application made within Social.Wiki, and choose which of the edits made by others they want to see.

For example, a user could change a messaging site on Social.Wiki to remove an AI chatbot and instead add a restaurant voting feature so their group chat can finally decide where they are going out for dinner.

“The infrastructure that we are all dependent on online is very rigid. It is based on these one-size-fits-all applications that we don’t have a lot of power to change. This project is about trying to imagine what a different web would look like where anyone can participate,” says Theia Henderson, an electrical engineering and computer science (EECS) graduate student, and lead author of a new paper on Social.Wiki.

The researchers envision a world in which, if a user is unhappy with their social app, they can edit it to change it however they like — without getting cut off from their social connections.

The platform enables users with no coding expertise to create a range of websites, including social sites like photo sharing networks, discussion forums, and dating apps. It handles all back-end work, eliminating the need for creators to set up servers or arrange databases.

Social.Wiki employs a decentralized infrastructure that allows users to retain ownership of their data as they create, edit, and navigate social websites within the platform. It also includes a “data guard” that protects user data from malicious or buggy applications.

Through case studies and user tests, the researchers demonstrated the breadth of applications people can create with Social.Wiki, including a rideshare app where riders propose the prices they are willing to pay and a collaborative canvas for creating art together.

While Social.Wiki is still in its early days, the researchers plan to continue refining the platform as they deploy it to a wider audience.

“We want to empower the creation of far more social applications that meet the needs of unusual small groups. Each group is unusual, but everyone is in unusual groups. Everybody has their own needs for social applications that we want to be able to address,” says David Karger, professor of EECS, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of the paper.

Henderson and Karger are joined on the paper by EECS graduate students Carmel Schare and Ana Dodik; Clemens Klokmose, a professor of computer science at Aarhus University in Denmark; Ziv Epstein, a postdoc in the MIT Media Lab; and David D. Clark, an MIT research scientist. The paper will be presented at the ACM Symposium on User Interface Software and Technology in November.

Letting users choose

Social.Wiki is a Wikipedia-like system in which users can navigate to different web applications or create their own web application within the Social.Wiki ecosystem. Each website has an “edit” button that allows any user to change the content of the site, from the text color on a message board to the pricing algorithm in a ride-sharing app.

Clicking the “edit” button in Social.Wiki opens a page that lets users view and edit the source code manually or prompt an AI assistant to make changes to the site.

This platform builds on the researchers’ prior work on Graffiti, a web framework with a decentralized infrastructure that makes building personalized applications easier.

With Social.Wiki, the researchers sought a collaboratively editable system that would allow anyone to revise web applications to be ideally suited for their needs and the needs of groups they belong to.

To enable this structure, Social.Wiki relies on a strategy the researchers call “plural governance.” Instead of a central moderator deciding which content users will see, each Social.Wiki user can choose the rules that determine which edits they will see on Social.Wiki webpages.

Users can also identify “trusted editors,” so they will see edits from individuals or organizations they consider reliable.

For instance, a user could customize their rules, or “governance policy,” so they only see edits to a site that have been endorsed by at least two of their friends. When the Social.Wiki browser loads a site for a user, it applies the governance policy that user selected to decide which version of the site to present.

For example, based on each user’s choices and edits, the system might show one user a ride-sharing app with a uniform pricing algorithm while another user might see the same app with a dynamic pricing algorithm.

“No website is tied to a particular machine. This means we have no centralized ownership over the websites or the underlying infrastructure,” Henderson explains.

Security and creativity

In this decentralized infrastructure, user data are stored separately from the websites that access it, which helps keep data secure.

Social.Wiki also employs a data guard, which prompts a user to review each request from a website that is attempting to access their data.

“In Social.Wiki, these websites are held in common and anybody can edit them, but the data guard makes sure that each user stays in control of what happens to their data,” Karger adds.

The researchers evaluated Social.Wiki through several case studies. For one, they developed a messaging platform that allows participants to modify the chat’s styling and interface without affecting the experience of others. In another, they built a collaborative ride-sharing app that enables riders to adjust the fare they want to pay using a transparent “living wage” calculator.

They also sponsored a hackathon in which users built Social.Wiki applications.

Participants developed a range of creative sites, including a housing co-op communication tool that organizes chats on a map of the co-op, as well as a garden-themed microblogging site where posts are “planted” as flowers.

“Even the little bit that has been done so far with Social.Wiki demonstrates that there is so much more that could be done with social apps if users could specialize them to meet a particular group’s needs,” Karger says.

Moving forward, the researchers are working to deploy Social.Wiki to a broader audience and gather feedback from more diverse user groups. They also want to make technical upgrades to enable end-to-end encryption on private message data and provide better developer tools.

This work was funded, in part, by Schmidt Sciences and the National Science Foundation (NSF).

Capturing the complete process of embryonic development

Shafaq Zia | Whitehead Institute — Wed, 28 Oct 2026 16:30:00 -0400

For decades, scientists have been able to look at individual moments in the development of an embryo: a snapshot of a developing brain, a picture of how the heart takes shape, or a glimpse of cells beginning to form the nervous system.

But what if instead of taking snapshots, they could watch the entire process unfold?

A new study out of the lab of MIT Professor Jonathan Weissman, a member of the Whitehead Institute for Biomedical Research and a Howard Hughes Medical Institute investigator, offers one of the most detailed views yet of how a mammal is built, cell by cell.

By following the family history of over a million cells as a mouse embryo develops, the researchers have created a kind of developmental blueprint, or movie, showing how cells divide and ultimately become the tissues and organs in the body.

This open-access work, published Oct. 8 in the journal Cell, in collaboration with Zachary Smith’s group at Yale University and the Max Planck Institute for Molecular Genetics, provides a comprehensive view of a process that has long been studied in pieces: how a single fertilized egg gives rise to hundreds of different kinds of cells, arranged in the right places and at the right times to build an animal.

“This is really a view of how the information in the genome plays out over space and time to go from linear DNA to a three-dimensional animal,” Weissman says.

The map could have broad implications for understanding birth defects and cancer. Many birth defects arise when cells change course as tissues and organs are being assembled. A detailed map of typical development could help researchers pinpoint where and when those processes go awry. In cancer, the same approach could help scientists trace when cells acquire traits such as the ability to spread or resist treatment.

The map could also inform stem cell research by revealing the sequence of cell states that leads to mature, functional cell types.

In the 1980s, scientists ventured into similar territory by painstakingly following the development of Caenorhabditis elegans, a tiny worm whose transparent body contains only about 1,000 cells. Using microscopy, they could trace how individual cells divided and where their descendants ended up.

But achieving something similar in a mammal was a challenge on a vastly different scale. Mammalian embryos contain far more cells, develop inside the uterus — out of sight — and do not follow a completely predetermined script.

The new study tackles this challenge using a technology developed in the Weissman Lab called PEtracer. The system acts somewhat like a flight recorder inside a cell. PEtracer makes small, heritable changes to DNA that are passed down as cells divide. By reading those changes later, scientists can work backward to reconstruct which cells came from which ancestors, essentially building a family tree for the cells in the developing embryo.

In this work, the researchers combined these family trees with information about what genes were turned on in each cell and where these cells were located in the embryo. They analyzed over a million cells from mouse embryos collected at multiple points during development, allowing them to follow not just where cells ended up, but when they began heading toward particular fates.

A new study from Jay Shendure’s group at the University of Washington, published concurrently in the journal Science, uses a related approach called DNA Typewriter to track cell divisions in developing mouse embryos and build a record of their cellular history.

Both studies provide a much more comprehensive look at mammalian development than existed previously. The Weissman team, however, studied embryos at closely spaced stages of development, looking at multiple embryos at each stage. This gave them a more detailed picture of how development unfolds and allowed them to ask how consistently cells follow the same paths from one embryo to another.

With this detailed timeline, the researchers could also determine when cells commit to particular fates, how many early cells go on to form a tissue, and how they ultimately contribute to the developing organ.

While earlier studies might focus on one cell population, this approach puts those individual stories into the context of the entire developing animal — a perspective that is particularly useful for understanding disease.

Cancer, for example, is not a single event, but a process in which cells acquire new characteristics over time, including the ability to spread or resist treatment. A lineage map could allow researchers to trace when those characteristics emerge and how cells change as they acquire them.

“Development and cancer both depend on decisions cells make about what they will become,” says Luke Koblan, a postdoc in the Weissman Lab and co-first author of the paper. “By tracing how cells develop in mouse embryos, we hope to better understand how cancer cells change in ways that help them spread or resist treatment.”

The work could also provide a new guide for stem cell research. Scientists have spent decades trying to coax stem cells into becoming mature, functional versions of particular cell types for research or potential therapies. But reproducing the precise conditions in an embryo is difficult.

“If we could have a step-by-step recipe for how to create every cell type in the body,” says graduate student and co-first author William Colgan, “that would be incredibly powerful for stem cell biology.”

Weissman compares the work to the Human Genome Project: knowing the sequence of the human genome gave scientists the ingredients — but not the instructions for how those ingredients are used — to build an organism.

“The genome is like the ingredient list for a four-course meal,” he says. “It tells you what the ingredients are, but it doesn’t tell you which ones you use when, how much you use, or where you put them. Without the recipe, it’s impossible to know how those ingredients can become the meal.”

This new map begins to fill in the details of that missing recipe. The result is a resource that researchers can use to explore mammalian development in a fundamentally new way. Rather than studying development as a collection of separate processes, scientists can now begin to see how those processes fit together into one unfolding story.

Seven MIT affiliates elected to the National Academy of Medicine for 2026

Bendta Schroeder | Koch Institute — Wed, 28 Oct 2026 14:55:00 -0400

During its annual meeting on Oct. 19, the National Academy of Medicine (NAM) announced election of 100 new members, including MIT faculty member Michael Yaffe and six MIT alumni: Steven Allen Abrams ’79; Susan L. Ettner PhD ’91; Diane Medved Harper ’80, SM ’82; Albert Icksang Ko ’81; Jennifer Lewis ScD ’91; and Alexander Marson PhD ’08.

Election to the academy is considered one of the highest honors in the fields of health and medicine and recognizes individuals who have demonstrated outstanding professional achievement and commitment to service.

Michael Yaffe, David H. Koch (1962) Professor of Science, is the founding director of the MIT Center for Precision Cancer Medicine, as well as a professor in the MIT departments of Biology and Biological Engineering and a member of the MIT Koch Institute for Integrative Cancer Research. He is also an attending surgeon and intensivist at the Beth Israel Deaconess Medical Center, where he specializes in injury and surgical oncology, and was recently appointed as senior advisor to the director of the National Cancer Institute (NCI) for the NCI Intramural Research Program.

The NAM recognized Yaffe for “the discovery of phospho Ser/Thr-binding domains in proteins and how they regulate signaling pathways controlling the cell cycle or in response to stress.” His contributions in this area were pivotal to “the emergence of systems biology and bioinformatic approaches to map motifs across the kinome and characterize biochemical principles that determine kinase-substrate selectivity.”

Yaffe’s work in cell signaling more broadly seeks to understand how signaling pathways that control DNA damage responses, survival, cell cycle progression, and immune activation are integrated at the molecular and systems levels to control tumor outcomes after treatment, and to translate discoveries into more effective cancer drug combinations and treatment strategies.

Yaffe earned his doctoral degree in biophysical chemistry and his medical degree from Case Western Reserve University. Before joining the MIT faculty in 2000, he completed residencies in both general surgery at University Hospitals of Cleveland and surgical oncology at New England Deaconess Hospital, a fellowship in surgical critical care at Harvard Medical School’s Harvard-Longwood Critical Care Program, and a postdoctoral fellowship with Lewis Cantley at Harvard Medical School. Yaffe is a decorated veteran of the war in Afghanistan and retired as a colonel from the U.S. Army Reserve Medical Corps in 2021. He is also the scientific editor-in-chief of Science Signaling, an elected member of the Association of American Physicians, and a 2021 MacVicar Faculty Fellow.

MIT alumni who were elected to the NAM for 2026 are affiliated with the following departments:

Steven Allen Abrams ’79 is an alumnus of the Department of Biology.
Susan L. Ettner PhD ’91 is an alumna of the Department of Economics.
Diane Medved Harper ’80, SM ’82 is an alumna of the departments of Chemical Engineering and Materials Science and Engineering.
Albert Icksang Ko ’81 is an alumnus of the departments of Chemistry and Biology.
Jennifer Lewis ScD ’91 is an alumna of the Department of Materials Science and Engineering.
Alexander Marson PhD ’08 is an alumnus of the Department of Biology.

Established originally as the Institute of Medicine in 1970 by the National Academy of Sciences, the National Academy of Medicine addresses critical issues in health, science, medicine, and related policy, and inspires positive actions across sectors.

“I am thrilled to welcome these outstanding health and medicine leaders into the National Academy of Medicine,” said NAM President Monica M. Bertagnolli. “Their extraordinary talent and commitment to advancing evidence-based guidance and solutions for our country’s greatest health-care challenges will strengthen our efforts to improve health for all.”

How the biggest Alzheimer’s risk gene disrupts the process of insulating neurons

David Orenstein | The Picower Institute for Learning and Memory — Wed, 28 Oct 2026 14:25:00 -0400

One of the main ways that the APOE4 gene variant, the biggest genetic risk factor for late onset Alzheimer’s disease, undermines brain circuit function is by disrupting the process of insulating neurons with a fatty sheath that optimizes their electrical transmission. A new study led by neuroscientists at the Picower Institute for Learning and Memory at MIT provides several lines of evidence to implicate a specific molecular pathway in that disruption, and uses that insight to demonstrate an intervention for preventing it in lab experiments.

The study, published Oct. 15 in Cell Stem Cell, shows that APOE4 leads to a suppression of “Wnt” signaling in the oligodendrocyte cells that provide neurons with a fatty insulation, called myelin. When Wnt signaling is deficient, they showed, the cells accumulate droplets of lipids rather than deploying fats to properly myelinate the neurons’ axon branches. When the researchers restored Wnt signaling in APOE4-carrying oligodendrocytes, myelination rebounded, both in human cell cultures and in mice modeling Alzheimer’s.

The research team, led by Picower Professor Li-Huei Tsai and study lead author Leyla Akay PhD ’25, a former graduate student in Tsai’s lab, discovered Wnt’s key role via multiple methods, including a screen in which they used CRISPR gene editing to systematically knock out each protein-coding gene in the genome, one by one, in cultures of human APOE4-carrying oligodendrocytes. The screen showed that when genes promoting Wnt activity in oligodendrocytes were lacking, lipid droplets accumulated, and that when genes suppressing Wnt were lacking, lipid droplets were, too.

“This proof of principle of doing CRISPR screens for genetic modifiers of APOE4-associated lipid accumulation in a central nervous system cell type is very exciting,” says Tsai, who also directs MIT’s Aging Brain Initiative. “I think this type of screen not only provides new insights into cellular pathways contributing to lipid metabolism defects, but really also has the potential to provide new targets for drug development.”

For want of Wnt

In 2022, Tsai’s Lab, led by Akay, Djuna Von Maydell PhD ’25, Jose Davila-Velderrain, and Joel Blanchard (also a co-author of the new study), showed that APOE4 caused lipid droplet accumulation in oligodendrocytes and upset myelination. But, Akay says, that droplet accumulation could happen for many reasons. The new study sought to dig deeper into the underlying molecular mechanisms.

“This CRISPR screen allowed us an unbiased way to test every single possibility,” Akay says. “What kind of lipid enzymes are involved in this process? And what are upstream pathways that could be targeted?”

In parallel with the CRISPR screen, the team also analyzed the chromatin accessibility of the DNA in human oligodendrocyte cell cultures and in postmortem human brain samples, in each case comparing APOE4-carrying samples to ones carrying the neutral APOE3 variant. This analysis of ATAC-seq data allowed them to assess which genes were most accessible for expression versus which ones were locked down from transcription. The chromatin analysis showed that genes promoting Wnt signaling were less accessible in APOE4 carriers.

“There was a striking overlap between ATAC-seq and CRISPR screen results,” Tsai says. “Both pointed to a dysregulation of the Wnt signaling pathway.”

Toward a therapy

Further analysis, including direct observations in cells at the lab bench, revealed how Wnt signaling became depressed in cultured APOE4 oligodendrocytes. Two proteins, beta-catenin and GSK3b, each compete to regulate Wnt — beta-catenin promotes it, and GSK3b suppresses it. The team found that in APOE4 oligodendrocytes compared to APOE3 ones, there was reduced beta-catenin expression and excessive GSK3b expression.

The team then tested interventions that manipulate GSK3b activity. In human APOE3 oligodendrocyte cultures, they showed that increasing GSK3b with a chemical called Dif3 increased lipid droplets. In APOE4 oligodendrocytes, they showed that inhibiting GSK3b with a chemical called CHIR99021 reduced droplets. In more complex “organoid” cultures combining APOE4 oligodendrocytes with neurons, they found that inhibiting GSK3b increased myelination and promoted the growth of neural axons.

The tests suggested that GSK3b inhibition could be a proof-of-concept therapeutic strategy for restoring Wnt signaling and myelination. As a final test, they treated mice carrying APOE4 and genetically modeling Alzheimer’s tau pathology with doses of CHIR99021. The treated mice exhibited a reduction in lipid droplets in the hippocampus of the brain, a key memory-making region. Moreover, they also showed increased myelination there, compared to untreated controls.

In addition to highlighting suppression of Wnt signaling, the CRISPR screen and chromatin analysis provided key clues about the molecular mechanisms by which loss of Wnt alters how oligodendrocytes metabolize lipids. Akay and Tsai say they are eager to follow up on those clues. They are also interested in investigating GSK3b’s linkage to tau phosphorylation, a major feature of Alzheimer’s pathology.

In addition to Tsai, Akay, and Blanchard, the paper’s other authors are Anna Bright, Daiyun Dong, Ethan Smith, Robert Hurt, Carles Boix, Kate Louderback, Janine Medrano, Dingcheng Sun, Haonan Lin, Oisín King, Gwyneth Welch, Hiba Nawaid, Emre Agbas, Alan Jiang, Adele Bubnys, and Ji-Xin Cheng.

The National Institutes of Health, the Robert and Renee E. Belfer Family Foundation, the Manthripragada McManama Alzheimer's Fund, the CAMA Seed Fund, the J. Crayton Pruitt Foundation, Joseph P. DiSabato and Nancy Sakamoto, and the Freedom Together Foundation provided funding for the study.

Raw data from the study is available on the Open Science Framework. Akay and Tsai have applied for a patent on the CRISPR screen results.

A soft robot could help doctors diagnose and treat disorders of the small intestine

Anne Trafton | MIT News — Wed, 28 Oct 2026 14:00:00 -0400

Disorders of the small intestine are often difficult to diagnose and treat. More than 20 feet long, an inch in diameter, and located deep within the body, this organ is harder to access than other parts of the digestive tract, such as the colon or stomach.

To overcome those challenges, MIT engineers have designed a new robotic enteroscope that can move easily through the narrow passage of the small intestine, allowing it to perform imaging, deliver drugs, and remove small polyps or tissue samples for biopsy.

In designing the new robot, the researchers were inspired by the locomotion of snakes, as well as the scaly skin that helps them gain traction on slippery surfaces. A special textured coating helps the robot remain attached to the mucosal lining of the intestine as it inches its way through the narrow tube.

Similar to an endoscope, the robot contains a variety of tools to help detect and treat any abnormalities that it might encounter.

“When we evaluate the small intestine, we do it for either diagnosis or treatment. If we find a little polyp that needs to be removed, this robot allows us to take a biopsy or remove it,” says Giovanni Traverso, professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and an associate member of the Broad Institute of MIT and Harvard.

Traverso is the senior author of the study, which appears today in Science Robotics. The paper’s lead authors are former MIT postdoc Binbin Ying, who is now an assistant professor at University of Texas Southwestern Medical Center; MIT graduate student Ahmad Mujtaba Jebran; and former MIT research scientist Sahab Babaee.

Snaking through the intestine

The most common problems that doctors need to evaluate within the small intestine include bleeding, lesions, and blockages.

Currently, the only way to diagnose those intestinal complications is with a procedure known as double balloon enteroscopy. During the procedure, an enteroscope advances through the small intestine as a health care worker alternately inflates and deflates two balloons that help propel it through the tract.

This process is slow and laborious, so the MIT team wanted to design an alternative enteroscope that would be easier to control and could move faster through the intestine.

“The fundamental challenge that we’re trying to overcome is how to evaluate that 20-to-25-foot segment of the small intestine in a way that can be more automated,” Traverso says.

The new tube-like robot moves through the narrow small intestine using a locomotion strategy similar to that of the snake. When slithering along the ground, a snake can steer itself by first contracting muscles on one side of the body, then the other.

To mimic this, the researchers designed multifunctional actuators that control the robot’s movement. Each actuator contains four chambers, which can be inflated in different combinations, prompting the tube to extend or bend in any direction.

“By tuning the amount of air that goes into these chambers, you can tune the direction that the robot is moving in,” Traverso says.

The head and tail segments of the robot contain additional expandable anchoring balloons that take turns inflating and deflating, helping the robot move forward. These head and tail segments are coated with special patterned sleeves inspired by kirigami — a variation of origami that involves cutting paper as well as folding it.

Using this technique, the researchers created coatings that lie flat until they are stretched by the expansion of the balloon, which causes three-dimensional kirigami needles to pop out. Similar to the scales of a snake, these needles give the robot more traction within the slippery lining of the intestine.

In previous work, Traverso’s lab has used this approach to create coatings for shoe soles that give them more traction, as well as stents that can temporarily lodge in the esophagus and release drugs.

“By using kirigami sleeves to enhance surface friction, the robot can generate localized tissue engagement traction for locomotion without relying on excessive stretching of the delicate small intestinal tissue,” Jebran says.

Diagnosis and treatment

The system also includes electronics that allow for navigation, with a controller that the person operating the device can use to guide the robot through narrow passages and around tight corners. The soft robot is tethered to the external controllers by a cable and is designed so that it could enter the body through either the mouth or the rectum.

The robot contains tools including a camera for imaging, a sprayer for drug delivery, and clips that can take tissue biopsies or remove polyps.

In animal studies, the researchers showed that the robot could travel about six and a half feet through the small intestine. They also successfully demonstrated the drug sprayer, tissue clips, and camera. The team is now working on improving the navigation systems and testing this approach for additional applications in the small intestine.

“We hope we can continue to move it forward to eventually develop systems that can help achieve more automated evaluation of the small intestine,” Traverso says.

The research was funded, in part, by the Karl van Tassel Career Development Professorship, the MIT Department of Mechanical Engineering, a Banting Postdoctoral Fellowship, an NSERC Postdoctoral Fellowship, the UT Southwestern Medical Center Startup Fund, Samsung Semiconductor and Eurnekian Nanotechnology Graduate Student Fellowships, the Cooke Undergraduate Transfer Scholarship, and the MIT Undergraduate Research Opportunities Program.

3 Questions: Why infrastructure is entering a defining era

Stephanie Martinovich | Department of Civil and Environmental Engineering — Wed, 28 Oct 2026 12:00:00 -0400

For centuries, infrastructure has evolved alongside technology. New materials and engineering techniques enabled taller buildings, longer bridges, and larger cities. Computing and digital technologies later transformed how infrastructure was designed, monitored, and operated.

Now, the pace of change is accelerating, and challenges are arriving simultaneously. Climate change is increasing pressure on water, energy, and other resources. Artificial intelligence is beginning to transform engineering and design, and its energy needs, along with aging grid infrastructure, is creating significant challenges, as well as new opportunities. In addition, geopolitical complexity is exposing vulnerabilities in global supply chains, and demand for infrastructure is rising, particularly in the developing world.

The result is a fundamental challenge: How can the world build the infrastructure it needs without simply repeating the extractive, carbon- and resource-intensive model of the 20th century?

In this interview, Ali Jadbabaie, JR East Professor and head of the MIT Department of Civil and Environmental Engineering, says the answer will require rethinking not only how infrastructure is designed and built, but how it is financed and operated, and bringing together the many stakeholders who shape those decisions. On Oct. 28, Jadbabaie will join experts from around the world in Madrid to explore these topics at the World Infrastructure Forum, which was co-founded by MIT’s Department of Civil and Environmental Engineering, UPM, the World Bank Group, and the University of Texas at Austin.

Q: Infrastructure has always evolved in response to technological advances. What makes this era different?

A: In the 18th century, civil engineering emerged as the civilian counterpart to military engineering, building the dams, bridges, and canals of its time. Industrialization in the 19th century brought railways and sewage systems. In the early20th century, the focus scaled up to buildings, highways, tunnels, and metro systems, and by mid-century the infrastructure needed to support megacities and skyscrapers.

Every time technology has changed, with information technologies, computing, sensing, and, more recently, digital transformation, it has changed the shape of infrastructure. It has even changed the definition of what infrastructure is. Today, we consider the power grid, semiconductor manufacturing plants, and data centers part of infrastructure, along with wireless infrastructure like cellphone towers and the infrastructure for high-speed internet, just as much a part of our critical infrastructure as roads, water systems, and power grids.

What is different now is the combination and speed of the changes we're facing. Climate change, resource constraints, and geopolitical instability are putting pressure on the way we designed infrastructure in the 20th century. At the same time, many people are moving out of poverty because of recent economic growth in the developing world. In fact, according to models, by 2028, for the first time in the history of humanity, the median human inhabitant of the planet will live off $10 a day. As more people move out of abject poverty, there will also be an increase in the demand for infrastructure.

If we try to meet that demand using the same resource-intensive model we used in the 20th century, we simply won't have the resources to do it. The question then becomes: How do we use the remaining resources more efficiently? We need to find a way to provide more infrastructure while using less water, energy, and materials, and produce less carbon.

This is where digital transformation and AI become potentially transformative. Faster and cheaper sensing, greater access to data, and better sensing, actuation, and control can help us make infrastructure decisions more efficiently. AI is slowly permeating the physical world, and we expect it to revolutionize the engineering design process, allowing us to explore many more possibilities, much faster and at a lower cost.

But the story of that hasn't been written yet, and the outcomes could still be very positive, depending on what we do.

Q: Can you give an example of infrastructure systems becoming more interconnected in a way that changes how we build or manage them?

A: The revolution in information technology and digital transformation over the past 30 years has made infrastructure systems more interconnected. You see this in cities. If the power goes out, water and gas systems can stop functioning, internet fails, and transportation system comes to a halt. If communications networks fail, many other services are affected. We saw this during Covid, when disruptions in supply chains affected energy networks, critical materials, and manufacturing. We see it when geopolitical conflicts disrupt oil and energy markets and those disruptions propagate through other parts of the global economy.

Infrastructure systems that were once managed separately are increasingly dependent on one another. As we connect more things together for more efficiency, we also increase systemic risk and expose the systems to rare but catastrophic failures. They have become rare, but when they happen, they're much bigger and more consequential.

The really tricky thing is that developing infrastructure involves much more than designing and building it. The whole value chain includes financing and operating it, as well as understanding who will use it and what those users need and prefer.

So, designing infrastructure is a highly transdisciplinary, sociotechnical endeavor, involving social, technical, and economic considerations. Understanding how these systems are designed is increasingly important because they are becoming more complex, making decision-making particularly difficult.

There’s also so much uncertainty in large infrastructure projects. They’re very capital-intensive, the business is lower margin than many other industries, and projects take a long time. Combine all of that, and it has historically made the industry very conservative.

But the transformations coming from AI and digital transformation are potentially offering a new way forward. What that new way looks like, no one has quite delineated it yet.

Q: How are you and your colleagues working to change how these decisions get made?

A: We realized that if we're going to develop a new approach to infrastructure, no single discipline, company, or country can do it alone. Infrastructure value chain is too interconnected, and the challenges are too large.

That's why MIT’s Department of Civil and Environmental Engineering co-founded the World Infrastructure Forum with the World Bank Group, the Universidad Politécnica de Madrid, and University of Texas at Austin. The goal is to bring together academia, industry, governments, and financial institutions across the infrastructure value chain and create an ongoing dialogue about how infrastructure should be designed, financed, built, and operated in the decades ahead.

This is particularly important because the biggest opportunities for new infrastructure development are not necessarily in the United States or other countries with mature infrastructure systems. Those countries still face significant needs for modernization and new investment. But over the coming decades, much of the world's population growth, and much of the infrastructure that will need to be built, will be in countries where infrastructure systems are still developing.

Those countries have an opportunity to benefit from modern infrastructure without simply repeating the model of the 20th century. But capturing that opportunity will require new approaches to technology, financing, engineering, and governance.

We're trying to create an ecosystem rather than just a forum, a mechanism for continuing the conversation among the different stakeholders across the infrastructure value chain. We've established five working groups and an advisory group of more than 30 leaders from across the infrastructure value chain, including engineering and construction, materials, finance, multilateral institutions, and academia.

Ultimately, the question is not whether we need a new way of developing infrastructure. The question is how we create it and who is going to execute it. There is no single person, company, or country that can solve this problem, because infrastructure itself is interconnected. We need to build the new model together.

Making machines that can listen

Adam Zewe | MIT News — Wed, 28 Oct 2026 00:00:00 -0400

The crack of a bat accompanied by a roar from the crowd can capture a baseball fan’s attention, even if they miss seeing the home run. Humans may be visual creatures, but we also perceive the world around us by listening to it.

To improve artificial perception, MIT Professor Paris Smaragdis SM ’97, PhD ’01 seeks to make machines that can hear and understand sound even better than humans.

A machine with super-human hearing could rapidly pinpoint the location of a traffic accident by listening for squealing tires and crunching metal, or identify when an industrial drill might break while it is boring through dense bedrock to dig a geothermal well.

“I like to say that I do the same thing the computer vision folks do, but instead I do it for hearing,” says Smaragdis, a professor in the Music and Theatre Arts program of the School of Humanities, Arts, and Social Sciences holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science.

His research group explores areas like machine learning for sound source separation, smart devices that can augment the audio surroundings of humans, and better tools to help people understand, manipulate, analyze, and create music.

“Having had a musical background, I’ve been interested in examining these ideas. What does it take to make machines understand sound? There is a huge domain of applications for machines that can listen,” says Smaragdis, who joined MIT in 2025 and is also part of the Music Technology program and a member of the Research Laboratory of Electronics.

A musical prelude

Smaragdis, who grew up in Greece, began playing classical guitar at a young age. He enjoyed playing traditional pieces but reached a turning point in his musical journey when his father brought home a small synthesizer.

“That little synthesizer had a function that allowed you to make your own sounds, and I became completely captivated by the possibilities,” he says.

Synthesizers were taking a more prominent place in pop music during the 1980s, adding to his fascination with the technology.

As he advanced through school, Smaragdis wanted to study music made with synthesizers. But this was not an educational path offered by the more traditional European conservatories at the time.

Instead, he enrolled at Berklee College of Music in Boston, into a program where he could explore the use of computers and synthesizers to shape and manipulate sound.

“With computers and synthesizers, I could make new sounds that have never been heard before,” he says.

At the same time, Smaragdis became immersed in jazz fusion. He declared bass as his major at Berklee since it would allow him more opportunities to play with jazz ensembles.

“I found the bass to be sonically more interesting than the guitar. It was a more unexplored domain, and I’ve always gravitated toward unexplored domains,” he says.

That interest in the unexplored carried over into his coursework. He and mentor Richard Boulanger, a professor of electronic production and design, created new tools to help people use computers to write, produce, and enhance music.

Through his studies at Berklee, Smaragdis embarked on a collaborative project with Professor Barry Vercoe (who passed away in 2025) at the MIT Media Lab to create software synthesizers for music creation. Building on that project, Smaragdis pursued graduate studies in Vercoe’s group, where he completed his master’s degree and PhD, followed by a postdoc.

“In the Media Lab, I got exposed more to the computer part of computer music. That’s where my attention shifted a bit. Now I really wanted to make computers that could understand sound,” he says.

Tuning in to unexplored domains

At that time, many students in the Media Lab were using signal processing techniques to help machines handle sound. Smaragdis chose a less explored approach — machine learning — to separate himself from the pack.

He found that training machine-learning models to understand sound offered advantages that couldn’t be attained with traditional signal processing techniques.

“Over time, it became really clear to me that we need to teach computers how to listen. We can’t just tell them how to listen. If anything, we don’t even know how we humans listen,” he says. “We don’t have a vocabulary to explain what it means to listen, or how to tell the difference between a cat’s meow and the sound of a violin.”

After finishing his postdoc, Smaragdis began a career at Mitsubishi Electric Research Labs, where he worked on artificial intelligence systems that could scan video soundtracks and understand what is happening on screen.

“What if you record an entire soccer game, but you only want to watch the highlights? In computer vision, it is very difficult to identify what a highlight is. But on the other hand, if you only listen to the sounds, then when the crowd goes crazy you would know that something interesting is happening,” he says.

That system was eventually incorporated into electronic devices, including a personal video recording system Mitsubishi was developing at the time, and into traffic monitoring systems and cell phones.

Smaragdis later joined Adobe, where he created the audio research team that built user-friendly tools to help artists, musicians, and producers manipulate sound without needing a lot of expertise.

“As we did that work, I kept coming up with deeper technical problems that needed to be resolved. I jumped into academia to address that itch,” he says.

Smaragdis joined the faculty at the University of Illinois at Urbana-Champaign, where he and his group incorporated the latest machine-learning techniques into audio-processing systems.

One of the technologies they developed utilized neural networks for separating individual sounds from audio mixtures. That project caught the attention of the team working on the 2021 documentary mini-series “The Beatles: Get Back.”

The production team enlisted Smaragdis to help clean up some archival Beatles recordings.

“That was a very different way of audio processing than what I was doing in academia. Now, I had to do something that sounded good but didn’t necessarily have to look good on paper. We didn’t have a formula we could optimize,” he says.

Every recording involved different challenges. For instance, in one clip John Lennon was playing music while Paul McCartney was horsing around, reading headlines from newspapers and making fun of George Harrison.

“We were trying to come up with a technological solution that could help us do everything, as opposed to addressing each problem on its own,” he says.

His contributions helped the team win an Emmy Award for sound editing for a nonfiction or reality program, in 2022.

A concerted effort

After 15 years at the University of Illinois, Smaragdis decided to join the faculty at MIT to tackle the thorny, interdisciplinary problems that were emerging in the field.

His group now focuses on two main areas. For one, they develop systems that make computers more useful tools for musicians.

One project in this area is enabling machines to understand how to listen to music, so they can provide constructive feedback to musicians.

“We want to enhance the toolbox a musician has. We don’t want to use computers to replace musicians,” he says.

On the other side, his group focuses on the fundamental tools scientists and artists alike can use to process, analyze, and manipulate audio.

For instance, his team has recently been exploring algorithms that allow sophisticated signal processing where traditional frames of reference, such as sampling rates, frame sizes, or context-windows, are not fixed — or even defined.

That implies different computational structures that are yet unexplored, despite their potential for more efficient processing, Smaragdis says.

“That requires rethinking a lot of the basics, but once you have that ability, you can start getting much more flexible and more powerful systems,” he adds.

They also explore ways to incorporate the latest techniques, like large language models, into speech recognition and audio enhancement.

“AI is an immensely powerful tool, but I don’t think we have found the ideal way to use it for sound and music yet,” he says.

He also enjoys mentoring students and helping them materialize their visions for research projects.

Since few of his students have experience using computation in sound, he emphasizes hands-on lessons so they see and hear how to transform equations into something tangible.

Smaragdis still feels a thrill from making music, whether with a computer or a more traditional instrument. Off campus, he tries to play jazz bass whenever he can find the time, and enjoys exploring the rich musical community of Boston with his wife and 15-year-old son.

Outside the musical realm, he plays and coaches underwater hockey — a sport where players wear diving masks and use sticks to push a heavy puck across the bottom of a pool toward their opponent’s goal. He coached the underwater hockey team at the University of Illinois, and is hoping to revive the MIT underwater hockey club soon and start hosting tournaments on campus.

While underwater hockey may be a lesser-known sport, it fits with Smaragdis’ lifelong interest in pursuing the underexplored.

“I’ve always like doing things that were different from what everyone else was doing. If you can create a new sound that no one has heard before, what does that mean about your ability to create new things? I still find these questions completely fascinating,” he says.

Origami-based construction method yields lightweight, load-bearing structures on architectural scales

Adam Zewe | MIT News — Tue, 27 Oct 2026 00:00:00 -0400

MIT researchers developed a new type of origami-based construction method that could be used to build complex structures that are cheaper and easier to assemble, and use less material than those built with some conventional techniques.

This scalable system simplifies the process of designing lightweight, rigid structural shells from modular components that can be fabricated from flat sheets of material and then folded into shape.

The units are connected to build a larger lattice structure using simple tools and a foolproof assembly process. They form a strong, load-bearing structural shell that doesn’t need interior support columns.

This method could be particularly well-suited for disaster relief shelters that can be rapidly fabricated and shipped efficiently as flat panels, then set up quickly by workers at the site of an earthquake or flood. It could also be used to develop strong, lightweight components for airplanes or modular robots, or architectural elements like sound-dampening panels and building facades.

The researchers’ technique can generate structures that are many times stiffer than those made using some other forms of structural origami, while requiring only about two-thirds as much material. This could make it a more sustainable form of construction.

In addition to developing small prototypes, the researchers demonstrated the scalability of this approach by fabricating a room-sized folded aluminum sculpture that was recently hung at the Boston Public Library as an exhibition inaugurated for the annual Fab Lab Conference and Symposium.

“This work shows that the power of digital manufacturing can go beyond personal fabrication projects and start touching the real world, enhancing the way we design houses and large structures. We are demonstrating structural origami on building scales,” says Alfonso Parra Rubio, a graduate student in the Center for Bits and Atoms (CBA) at MIT and lead author of a paper on this method.

He is joined on the paper by Riccardo Foschi, a researcher at the University of Bologna in Italy; Rupert Maleczek, a senior scientist at the University of Innsbruck in Austria; Tomohiro Tachi, a professor at the University of Tokyo; and senior author Neil Gershenfeld, an MIT professor and the director of the CBA. The research appears in the International Journal of Solids and Structures.

Lightweight, rigid components

The researchers drew inspiration from the folded tessellations developed by the artist and scientist Ron Resch in the 1960s. Resch employed repeating patterns of polygonal unit cells to create complex 3D structures (like a honeycomb).

But rather than creating a shape from one folded piece of material, like Resch’s tessellations, the researchers fabricate unit cells that are folded individually and connected to form the 3D structure.

By designing individual building blocks, they can simplify the fabrication process and create more complex shapes.

While working in the Tachi lab, Parra Rubio developed a method for combining two tripod-shaped unit cells from a Resch tessellation into a building block that has zero degrees of freedom, which means it is completely rigid and locked in place.

These rigid building blocks can be connected into a network of interwoven beams that form a 3D structure with extremely high stiffness and stability, but very light weight.

“This helps us build strong structures while minimizing the amount of material we are using,” Parra Rubio explains.

But it would be enormously challenging to manually design polygonal building blocks that will form a structure that meets a user’s constraints.

To simplify the process, Parra Rubio joined forces with CBA graduate student Erik Strand to develop an automated solver that designs a series of manufacturable unit cells that will form the user’s desired structure. These design files can be fed to a CNC milling machine that automatically cuts the building blocks from flat sheets of material, like aluminum.

“On the computational part, we break this big problem down into many small subproblems. Now, we solve each cell independently to make sure that it is foldable, and then we globally assemble them after that,” Strand says.

Simplified assembly

Builders fold the individual units into shape and assemble them into a finished structure using only hand tools, like screwdrivers.

The assembly process does not require any large-scale welding equipment, which could make it simpler, faster, and cheaper than some other forms of construction. In addition, the unit cells are designed so they adjust naturally into their correct shape as the structure is assembled.

“The way the unit cells are designed, the structure can only exist in one configuration. The connection is the same everywhere, even though each unit cell is different,” Parra Rubio says.

Simulations indicated that this method yields structures about 3.5 times stiffer than those made with some other origami-based techniques, while using less material. In addition, the resulting structures could be strong enough for some architectural applications, Parra Rubio adds.

To demonstrate the versatility of this approach, the researchers assembled a 23-foot sculpture from pieces of folded aluminum that was hung from the ceiling of the atrium in the Boston Public Library. A team with no professional construction experience built the entire structure in a matter of days, Parra Rubio says.

“It was really fun to work on something at such a quick pace where the theory was really meeting a practical use for it. We had to think a lot about how we could tweak the design to achieve each objective,” Strand says.

In the future, the researchers want to test this approach in larger, architectural structures. They also want to explore the use of unit cells with more complex geometries, which could offer greater strength or stiffness, and develop a user-friendly design interface.

“This work shows that folding metal is a method that is ready to be used for building structures. We can do it big enough and strong enough, now that we can bring it out of the lab and to the construction site,” Gershenfeld says.

“Within the architecture, engineering, and construction industry, complex structures are often delivered through a fragmented sequence of handoffs between design, engineering, fabrication, and construction teams, with each transition creating opportunities for miscoordination, rework, and loss of design intent. By embedding geometric, structural performance, and assembly intelligence directly into each module, Rubio’s approach maintains a seamless digital thread from design through erection, enabling unprecedented customization while ensuring precise execution,” says James Coleman, SM ’14, MArch ’14, cofounder of SumPoint, a project installation specialty firm.

Coleman, the former Vice President of R&D at A. Zahner Company, was not involved with this research. His recent work on the design, fabrication, and erection of complex architectural projects includes the Lucas Museum of Narrative Art, Air Force Academy Cadet Chapel, Google Headquarters, and SoFi Stadium.

This research was funded, in part, by the Center for Bits and Atoms Research Consortia, the University of Bologna in Italy, and the Austrian Research Agency.

J-PAL North America launches Homelessness Evaluation Resource Hub to drive evidence-informed approaches to housing stability

Kate Christie | J-PAL North America — Mon, 26 Oct 2026 19:00:00 -0400

J-PAL North America, a research and policy center in the MIT Department of Economics, is launching a free Homelessness Evaluation Resource Hub.

The hub — a public repository of evergreen multimedia content developed with a grant from Google.org — aims to equip decision-makers with foundational tools on how to leverage evidence to reduce homelessness and improve housing stability.

Homelessness represents both a cause and a consequence of poverty and inequality. Rigorous evidence is critical in identifying effective solutions to eliminating homelessness, but rising constraints on time, funds, and other resources can create difficult barriers to developing and accessing high-quality evidence.

J-PAL North America aims to increase access to evidence-informed paths to housing stability by providing essential tools for researchers, policymakers, and practitioners to evaluate the impact of their programs, expand the body of literature on what works to reduce homelessness, and leverage existing evidence to inform programs and decision-making.

To launch the hub, J-PAL North America is hosting a virtual event on Oct. 27.

David Phillips, director of research at the Wilson Sheehan Lab for Economic Opportunities (LEO) and research professor of economics at the University of Notre Dame, served as the scientific advisor on the hub.

“I’m thrilled to have been able to contribute to J-PAL North America’s Homelessness Evaluation Resource Hub,” Phillips says. “In my 15 years working with nonprofit and government leaders on program evaluations, I've seen how much value comes from practitioners and academics entering each others’ world, learning to speak each others’ languages, and, most of all, learning from each other. This resource hub builds shared language for research design so teams can move forward with clarity and confidence — creating more time to focus on the shared goals of continuous learning and improving outcomes for communities.”

The hub includes resources focused on two primary activities: using evidence, and generating evidence. The Using Evidence section provides newly developed two-page policy briefs that synthesize experimental and quasi-experimental research results on five established homelessness and housing interventions — “Housing First,” emergency financial assistance, case management, legal assistance in housing court, and housing vouchers — with more on the way.

The section also features J-PAL North America’s 2019 Evidence Review, “Reducing and Preventing Homelessness: Lessons from Randomized Evaluations,” which synthesizes results from 40 rigorous evaluations of 18 distinct programs related to homelessness prevention and reduction in North America.

The Generating Evidence section houses seven modules on foundational concepts of rigorous impact evaluations such as ethics, data and measurement, and opportunities for randomization, and how to apply them to evaluations in the homelessness sector. These resources build upon J-PAL’s experience training over 30,000 learners over two decades to become better users and producers of evidence.

“We are proud to be launching the Homelessness Evaluation Resource Hub,” says Laina Sonterblum, a J-PAL North America policy and communications manager who oversees the organization’s housing and homelessness portfolio. “These resources are informed by over a decade of experience working with partners and researchers to understand the current literature and build the base of rigorous research on the best ways to address homelessness. We’re hoping the hub can provide a free, accessible entry point for anyone curious about how evidence can be a tool for learning and maximizing the effectiveness of programs and policies.”

The resources in the hub are also informed by nearly 20 informational interviews with prominent researchers, intermediaries, government leaders, and service providers in the region and build off a decade of experience working with partners and researchers to build the base of rigorous evidence on the best ways to address homelessness. J-PAL North America developed these resources in close collaboration with partners at LEO at Notre Dame and is especially grateful to David Phillips, who provided meticulous guidance and subject matter expertise.

Explore the Resource Hub.

Hashim Sarkis to step down as dean of the School of Architecture and Planning

Peter Dizikes | MIT News — Mon, 26 Oct 2026 16:30:00 -0400

Hashim Sarkis will step down as dean of the MIT School of Architecture and Planning (SA+P) at the end of June 2027. During his 12 years as dean, Sarkis elevated the school’s role and impact by creating new academic centers, adding more support for students, and, quite visibly, moving into a new home in MIT’s renovated Met Warehouse building.

To an unusual degree, these achievements are interconnected: The Met Warehouse, dedicated this month, features innovative and expanded spaces for education, research, and public engagement. The building enables SA+P to add programs, host interdisciplinary work, and stage events of Institute-wide interest.

Sarkis, the Elizabeth and James Killian (1926) Professor at MIT, and a professor of architecture and urban planning, also led the Institute’s “One Community = One Campus” planning project from 2016 to 2018, which conceptualized MIT as a contiguous urban space. The report helped MIT add sustainable infrastructure as well as new buildings, from dormitories to the theater arts and music buildings — and now the Met Warehouse.

For Sarkis, providing more support for SA+P students is as important as any other accomplishment. The school will provide full tuition support in multiyear graduate programs, starting with new students in the fall of 2027.

“I do believe education is a right for every student,” Sarkis says, when asked to cite the most meaningful changes he has helped enact.

In a letter to the MIT community, Provost Anantha Chandrakasan wrote that Sarkis had crafted “a remarkable tenure as dean,” one that has “advanced a distinctive role for SA+P within MIT: connecting technology to society through design, planning, making, and creative practice.”

Sarkis, a lauded architect who has published widely and runs an international design practice, became dean in January 2015. He will take a sabbatical in the 2027-28 academic year, then return as a professor and head of the MIT-LUMA Lab.

In his letter to the community, Chandrakasan also announced the formation of a search committee to identify the next dean of SA+P. The 17-person committee is being chaired by Justin Steil, professor of law and urban planning in DUSP.

The “transformative power of design”

MIT’s School of Architecture and Planning is a broad-ranging place, encompassing the Department of Architecture; the Department of Urban Studies and Planning; the MIT Media Lab, which is home to the graduate program in Media Arts and Sciences; the Center for Real Estate; the program in Art, Culture, and Technology; the Leventhal Center for Advanced Urbanism; and the Morningside Academy for Design. One of the hallmarks of Sarkis’ tenure has been the growth of existing interdisciplinary programs at SA+P and the development of new ones. In 2016, a gift from the Leventhal family expanded the Center for Advanced Urbanism, established in 2013, and renamed it in honor of Norman B. Leventhal ’38. The center enables research and hands-on projects addressing complex design and planning challenges in cities globally.

During Sarkis’ tenure, MIT also introduced an undergraduate minor in design in 2016 and relaunched its bachelor of science in art and design in 2018, expanding opportunities for students across the Institute. The school also established DesignX, an accelerator supporting entrepreneurship in design and the built environment.

In 2020, MIT launched the Mobility Initiative, bringing together researchers from across the Institute to examine the future of transportation and mobility. Led by Professor Jinhua Zhao, now head of the Department of Urban Studies and Planning, the initiative connects research in transportation, technology, urban planning, and policy.

In 2022, with support from the Morningside Foundation, the Institute founded the Morningside Academy for Design (MAD), an interdisciplinary design center allowing for campus-wide collaboration. And in 2024, with support from the LUMA Foundation, SA+P launched the MIT-LUMA Lab, which works across a wide range of fields, including design, science, technology, and art, to explore solutions for climate-based and other environmental challenges.

In a different vein, SA+P also collaborated with the MIT Schwarzman College of Computing this year to establish the MIT and HPI AI and Creativity Hub (MHACH), which aims to further work in design and computing at a time of rapid expansion for AI applications.

Three of these centers — LCAU, MAD, and MIT-LUMA Lab — are located in the transformed Met Warehouse building, a leading example of adaptive reuse in architecture and engineering. The huge brick edifice, which initially had tiny window slits bringing in minimal light, was used for about a century as a storage facility, until 2015, when MIT began exploring other possibilities for the building.

By 2018, the concept of moving SA+P into the Met Warehouse had gained traction, with Sarkis publicly telling MIT News, “It’s about really creating a design hub for MIT on the campus. ... Moving does not address just the school’s aspirations, but MIT’s aspirations.”

After working with the Cambridge Historical Commission, the project architects, Diller Scofidio + Renfro, replaced large segments of brick wall with glass sheets, added skylights, and reconfigured the interior, which now has multiheight design studios, modular classrooms and common spaces, an auditorium, public galleries, a large entrance lobby with a café, and building-long corridors on all floors. For almost a decade, Sarkis served as the key driver of the project — and was an advocate for adaptive reuse, rather than creating a new building requiring a greater carbon footprint.

“I think it sends a very good message that this vanguard school of architecture, at the Massachusetts Institute of Technology, is moving into a historic building and adapting it for the future,” Sarkis told MIT News this summer.

At the building’s formal dedication ceremony on Oct. 1, MIT President Sally Kornbluth called the Met Warehouse “a testament to the transformative power of design, which has been central to MIT from the very beginning.” She also heralded it as a model for education, noting that future learning in many fields may call for “exactly like the kind of hands-on, in-person, collaborative problem-solving the new Met was made for. … It’s also primed to help the rest of MIT meet the moment.”

The many sources of design and democracy

Sarkis grew up in Beirut, Lebanon, with a childhood interest in architecture. His views about the civic applications of design have long had many sources. He received his BA in architecture from the Rhode Island School of Design in 1987, then earned an MA in 1989 and PhD in 1995, in architecture, from Harvard University. At Harvard, his PhD work included political philosophy, as he studied with legal theorist Roberto Mangabeira Unger and philosophers Hilary Putnam and John Rawls, among others.

Sarkis was a professor at Harvard’s Graduate School of Design from 1998 until he joined MIT in 2015. He has published widely, including the 2003 book “Circa 1958: Lebanon in the Pictures and Plans of Constantinos Doxiadis.” He edited the 2002 volume “CASE: Le Corbusier’s Venice Hospital and the Mat Building Revival,” and co-edited, with Eric Mumford, the 2008 volume “Josep Lluís Sert: The Architect of Urban Design, 1953-1969.” He is also co-author, with Roi Salguiero Barrio and Gabriel Kozlowski, of the 2020 book, “The World as an Architectural Project.”

In 1998, Sarkis also established his practice, HashimSarkis Studios, which has offices in Lebanon and Massachusetts. Among his best-known works are the Byblos Town Hall in Lebanon, completed in 2016, which features three volumes rising above a glass-enclosed ground floor, to represent and help constitute the transparency needed for democratic government. His first major project, Housing for the Fishermen of Tyre, also in Lebanon, was cited as one of the most significant buildings of the century in the “Phaidon Atlas of 21st-Century World Architecture.”

During his upcoming sabbatical, Sarkis will work on a variety of projects in locations including Venice, Egypt, and Armenia.

In addition to his many duties as dean, Sarkis served as the curator of the Venice Biennale’s 2021 International Architecture Exposition. The exhibition’s theme was a question Sarkis has long contemplated: “How Will We Live Together?” Featuring work conceptualized at many scales, the exhibition displayed projects by 112 designers from 46 countries and was critically acclaimed; The New York Times called it “a case study for how to begin to tackle such questions.”

A dose of optimism

Sarkis’ seriousness of purpose as SA+P dean has long meshed with what he has called a “quirky” sense of humor, in the MIT vein. In 2016 he served as a judge for the “Crossing the Charles” competition, in which dozens of inventive MIT-designed watercraft floated across the Charles River to mark the 100th anniversary of MIT’s move from Boston to Cambridge. A parade also unfolded on the bridge carrying Massachusetts Avenue across the river.

In an echo of those events, last month SA+P organized its own procession into the Met Warehouse, complete with costumes gesturing at the Bucentaur, the boat that in 1916 carried MIT’s charter across the river. The procession was conceptualized and directed by MIT Professor Azra Akšamija, and developed in collaboration with many students, faculty, and staff.

Meanwhile, when it comes to the experience of having other architects redesign the Met Warehouse, Sarkis quips, “I learned to be a client.”

More seriously, talking about high points of his tenure as dean, Sarkis quickly calls the organized visioning process for the Met Warehouse “inspiring,” adding, “We needed a common framework, and faculty were able to project forward and identify it.” The building, Sarkis emphasizes, helps reinforce what he believes is central and distinctive about design work at the Institute: “the MIT model of conducting research and education at the same time.”

In the face of current challenges confronting education, Sarkis steadfastly stuck with the Met Warehouse project, helping complete it while continuing to build up the School of Architecure and Planning and solidifying MIT’s place at the leading edge of design. That may be a larger sign that such progress remains possible.

“Optimism is integral to our fields,” Sarkis says.

Implantable device uses ultrasound to improve drug delivery to the brain

Anne Trafton | MIT News — Mon, 26 Oct 2026 11:00:00 -0400

Patients with glioblastoma, an aggressive type of brain tumor, usually undergo surgery to remove the tumor. However, it’s difficult for surgeons to remove all of the cancer cells, and the tumor often recurs.

MIT researchers have now designed an implantable drug-delivery device that they hope could help prevent recurrence of glioblastoma. Their device contains a reservoir that can release drugs over an extended period, along with ultrasound transducers that use sound waves to help drugs spread farther through the brain and bypass the blood-brain barrier.

Such a device could be implanted in the brain during surgery, then gradually release drugs that would help kill the remaining tumor cells. After implantation, a wireless controller can be used to program the exact ultrasound dosages to be delivered, enhancing the reach of the delivered therapy.

“We have 24 transducers in one ultrasound array, which can help a given drug bypass the blood-brain barrier and enhance the drug infusion. The device also gives doctors options in terms of the size, the length, and the number of infusions,” says Canan Dagdeviren, an associate professor of media arts and sciences at MIT and the senior author of the study.

In a paper appearing today in the journal Device, the researchers showed that their ultrasound device allowed molecules to spread further after being released, in tissue models and in a mouse study.

MIT graduate student Sarah Ornellas and Jason Hou PhD ’26 are the lead authors of the study.

Enhanced by ultrasound

Glioblastoma is a rare but deadly cancer, with a little more than 10,000 cases diagnosed in the United States per year. On average, patients survive 12 to 15 months after diagnosis. Patients typically undergo surgery to remove as much of the tumor as possible, followed by radiation and chemotherapy.

Chemotherapy drugs are usually given either as pills or an intravenous infusion, but in recent years, scientists have developed drug-delivery wafers and hydrogels that can be implanted directly in the skull. That can help to eliminate the side effects of chemotherapy and ensure that a higher concentration of the drug reaches the brain.

Those devices work by passive diffusion of drugs, but the MIT team wanted to design a new device that would allow for greater control of the timing of drug release. They also wanted to incorporate ultrasound arrays to enhance the distribution of drug molecules.

Previous research has shown that ultrasound helps drug molecules to disperse more widely, by creating convective flows that help travel them longer distances. The mechanical oscillations generated by ultrasound waves also temporarily increase the permeability of the blood-brain barrier, helping drug molecules get into the brain.

Dagdeviren had previously designed a tiny cannula, about as thin as a human hair, that could be implanted in the brain to deliver drugs. She also built an implant called ImPULS, which uses tiny ultrasound transducers to deliver deep brain stimulation.

In the new study, her lab combined these technologies to create LUCID (Long-term Ultrasound-enhanced Convective Injection Device) — a flexible implant as thin as a sheet of cardstock. A drug-delivery cannula is 3D-printed directly onto an array of 24 miniature ultrasound transducers. These transducers generate ultrasound waves right at the tip of the needle, helping drug molecules to diffuse farther into tissue after they are released.

“Getting a drug into the brain is only part of the challenge. It also needs to spread through the surrounding tissue. We brought a delivery channel and a tiny ultrasound array together in one implant to help molecules reach a larger area, even during slow infusion,” says Hou.

The implant’s reservoir carries about 100 microliters of fluid, and infusion is supplied by a passive osmotic pump, while ultrasound can be controlled wirelessly through Bluetooth. The device also contains a small battery that can be recharged wirelessly.

In tests in a “tissue phantom” — a gel-like material engineered to mimic human tissue — the researchers showed that ultrasound-aided delivery spread drug molecules much farther than when the drugs were delivered without ultrasound.

“By enhancing that distribution locally and programmably with ultrasound, you can reduce systemic toxicity in nontarget organs, while the therapy that you’re using has a better chance of reaching whatever tumor cells are within the affected area,” Ornellas says.

Wider distribution

The researchers also tested their system in mice, using it to deliver an MRI contrast agent. When the ultrasound array was activated, the contrast agent spread further through the brain than when ultrasound was not used.

If developed for use in patients, the drug cannula and ultrasound array would be placed inside the skull, while the part of the device containing the battery and other electronics could sit outside the skull, the researchers say.

In addition to delivering small-molecule drugs, this technology could also be used to deliver immunotherapies or nucleic acids, including gene therapies. It could also be potentially used in organs other than the brain.

“We started with the brain as a follow-up to our ImPULS technology, but eventually this can be used for other soft tissue treatments for different types of drugs, different types of interventions in deep body tissue,” Dagdeviren says. “We are showing that we can make any kind of payload be distributed in a better way.”

Other authors of the paper include Michal Nowicki, a research associate at Massachusetts General Hospital; Jerry Li, a recent graduate of Harvard University; Heloise Leblanc, a graduate student at Boston University; Betul Serra Tulu, an MIT undergraduate; David Sadat, a research specialist at MIT; Marco Mineo, an instructor in neurosurgery at Harvard Medical School; Steve Ramirez, an associate professor of psychological and brain sciences; and E. Antonio Chiocca, a professor of neurosurgery at Harvard Medical School and Massachusetts General Hospital.

The research was funded by the MIT Media Lab Consortium and the National Institutes of Health.

Watch the universe emerge from the dark ages, with Lumina

Jennifer Chu | MIT News — Mon, 26 Oct 2026 00:00:00 -0400

With the Big Bang, the universe burst into existence as a hot and dense soup of electrons and subatomic particles. This primordial ionized plasma, initially smaller than a single atom, rapidly expanded and cooled. In the process, particles in the soup slowed enough to bind with electrons, forming electrically neutral hydrogen.

As this neutral hydrogen accumulated, the effect was like a shroud, preventing light and other forms of radiation from freely passing through. Around 380,000 years after the Big Bang, the universe was plunged into a cosmic “dark age.”

Now, a new computer simulation is giving scientists a detailed view of how the first galaxies, stars, and other structures could have unfolded from that time, to eventually pull the universe out of its dark age.

In a paper appearing today in the Monthly Notices of the Royal Astronomical Society (MNRAS), physicists from MIT, Harvard University, and elsewhere present Lumina — a large-volume, high-resolution simulation that follows the early universe from the dark ages through the next 2 billion years of cosmic evolution.

Two pivotal transformations

Lumina covers a huge patch of the early universe that spans 500 megaparsecs, or about 1.6 billion light-years wide. In that space, the simulation tracks the evolution of dark matter, gas, stars, and radiation (such as photons), across enormous cosmic scales and also zooming down to the detail of an individual developing galaxy.

Over the 2 billion years that Lumina simulates, it captures the emergence of two huge transformations in cosmic history: hydrogen re-ionization and helium re-ionization.

Hydrogen re-ionization is a period when early stars were able to push out enough ultraviolet light to interact with neutral hydrogen and strip it of its electron. This “re-ionizing” prevented the hydrogen from absorbing photons, instead allowing light to travel freely. Hydrogen re-ionization helped to lift the veil of neutral hydrogen — a process that Lumina captures in detail for the first time.

The simulation also tracks helium re-ionization, a process similar to its hydrogen counterpart, in which much higher-energy light breaks apart neutral helium.

In its detailed simulations, Lumina revealed that hydrogen re-ionization was likely driven by light from early galaxies and stars. In contrast, helium re-ionization was fueled by much more energetic early supermassive black holes in their active, accreting phases. Together, this represents the first time a simulation has captured both hydrogen and helium re-ionization.

“Lumina does everything from small-scale star and galaxy formation to large-scale re-ionization of hydrogen and helium, in a volume that is 100 times larger than previous best simulations,” says study author Mark Vogelsberger, a professor of physics at MIT.

He and his colleagues are making Lumina available to the scientific community so that scientists can compare its detailed simulations of the evolution of early cosmic structures with snapshots taken by new and future telescopes. Then, they might begin to piece together the processes that shaped the first galaxies, stars, and structures in the universe.

The study’s MIT co-authors are Xuejian (Jacob) Shen and Teodora-Elena Bluichi. Additional authors include first author Oliver Zier (a former postdoc in the Vogelsberger Lab), Rongrong Liu, Sonja Koehler, and Lars Hernquist of the Harvard and Smithsonian Center for Astrophysics; Aaron Smith of the University of Texas at Dallas; Rahul Kannan of York University in Canada, and Volker Springel and Rüdiger Pakmor of Max Planck Institute for Astrophysics in Germany.

Cosmic computation

Lumina takes its name from the Latin word meaning “lights.” At the heart of Lumina is IllustrisTNG, which was previously developed by scientists at MIT and elsewhere, including members of the Lumina team. IllustrisTNG is a large-scale simulation that tracks the interactions of dark matter, gas, and other processes involved in forming galaxies, from the Big Bang to the present day.

In building Lumina, the team refined IllustrisTNG’s code to enable the simulation to track the formation of galaxies with the same level of detail but throughout a much larger volume. They combined this leveled-up galaxy model with a model of radiation transport, which simulates how different forms of radiation such as ultraviolet light interact at extremely large, cosmic scales to capture processes such as hydrogen re-ionization.

The new and improved simulation resolves extremely fine details across huge scales. In practice, the simulation encompasses half a trillion individual elements, including dark matter particles and gas cells, which is about 30 times higher-resolution than IllustrisTNG. Lumina captures all this detail and evolves it forward in time, across an enormous patch of about 500 megaparsecs, which is 100 times larger in volume than previous simulations.

Running the calculations for every single cell over 2 billions years of cosmic time, and across extremely large and very small scales, involves a significant amount of computing power. The team ran Lumina on Frontier, one of the world’s most powerful supercomputers, based at Oak Ridge National Laboratory in Tennessee.

“At peak scale, the calculation brings together the equivalent of more than 100,000 CPU cores and around 15,000 GPUs,” says Shen. “Making all of those processors communicate efficiently while continuously evolving trillions of interacting pieces of information is itself an extraordinary feat of computer engineering.”

Full zoom

Lumina begins its simulations at around 45 million years after the Big Bang, in the midst of the dark ages, when neutral hydrogen dominated the early universe. The researchers set the simulation with initial conditions approximating what physicists theorize should have existed in such a dark cosmic period.

They then let Lumina run to calculate how matter at those times, in the form of gas and dark matter, interacts across the simulation’s half-a-trillion cells. Simultaneously, the simulation calculates where and how radiation, such as ultraviolet light from stars, emerges and interacts with its surroundings.

On the project website, the team provides a number of videos that depict the various scales, time periods, physical processes, and layers of cosmic structures that Lumina simulates. Examples include a “zoom-in” from the full, large-scale patch of the universe to a single, disk-shaped galaxy; and a time-lapse of the full volume, over 2 billion years, that captures the pattern of hydrogen as it is re-ionized over time.

“On the one hand, it’s evolving dark matter as well as the gas in the universe, and on the other, it’s also tracking how radiation travels,” Zier explains. “We see dark matter and gas start to clump in these over-densities. And when we zoom in, we can see gas is falling into these dark matter haloes. Zooming in more, we see this is where the first galaxies form. And the first stars form in those first galaxies. Those stars then create radiation. The simulation then tracks that radiation, and we can zoom way out to see how that radiation travels out from stars, through the universe, until at some point it ionizes some neutral hydrogen.”

“Other simulations cannot zoom in as far as this,” Vogelsberger notes. “The difference between the smallest and largest scales that we simulate is larger than in any other simulation.”

Looking ahead, the team plans to apply Lumina to map the large-scale structure of the early universe. The simulation can also help to predict the gravitational-wave patterns that early galaxies and black holes might produce. Whatever Lumina simulates could soon be compared with actual observations from new and upcoming telescopes.

“One of Lumina's major advantages is its sheer size,” Shen says. “It produces enormous populations of simulated galaxies, finally allowing us to make comparisons with the very large galaxy samples that upcoming surveys will observe.”

This research was supported, in part, by NASA and the National Science Foundation, and used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Advanced Scientific Computing Research programs in the Office of Science of the U.S. Department of Energy.

At MIT, Martha Minow speaks on sustaining democracy, both by using and overcoming AI

Peter Dizikes | MIT News — Fri, 23 Oct 2026 17:00:00 -0400

Legal scholar Martha Minow delivered this fall’s Compton Lecture at MIT on Thursday, examining the civic foundations of democracy — and describing how artificial intelligence is undermining it, but could be used to help reinforce traditional U.S. governance.

“It’s not a good time for democracy or constitutionalism,” said Minow, who is the 300th Anniversary University Professor at Harvard University, and a prominent expert in constitutional law and human rights. She noted a litany of challenges to democracy in recent years, including violations of laws and norms, the stifling of free speech, a diminution of congressional authority, disregard of legal rulings, the common vilification of others in society, and more.

Recent Gallup polling, Minow observed, shows 77 percent of people saying that the founders of the U.S. would be disappointed in the contemporary condition of the country.

“Many people genuinely wonder whether this nation’s system of government and rule of law, ignited 250 years ago, will survive,” Minow said.

Her remarks assessed the state of the U.S. in terms of what she called four key “ingredients” of democracy: education, reliable and accessible information, security from violence, and fairness. Minow evaluated how AI has, among other things, influenced these areas of life — but also suggested ways people could use contemporary technology to help.

“It’s very familiar to hear blame attributed to digital technologies and experts of all stripes, but a turnaround, I believe, is genuinely possible if people at MIT and elsewhere pour time and talent towards real and much-needed solutions,” Minow said.

A substantial portion of her remarks focused on the pitfalls of AI in education. That issue compounds some other troubling trends: The U.S. already has large local and regional disparities in educational funding, Minow noted, while schools have actually become significantly more segregated in economic and ethnic terms over the last three decades.

In this context, AI threatens to further weaken independent-thinking skills, Minow suggested, just when greater understanding of civic issues is absolutely vital.

“As AI surges, I think we have to learn to be smarter about how to use it, how to regulate it, and how to actually educate people about the costs of mindless using,” Minow said. She added: “Dominant AI platforms supercharge the strategies that are deployed by social media companies, inducing passivity in people … Leading AI models and interfaces and sycophantic elements endanger the development particularly of young people’s capabilities to think, to remember, and to engage in critical thinking.”

That trend away from cognitive effort, she suggested, can be changed — perhaps through AI tools that act more like tutors and are designed to enhance people’s deep thinking.

“Existing LLMs, model training, and interface design can offer nudges and questions and hints rather than replacing student work, but there’s so much more that could be developed, and more effectively, to be trustworthy, and to deepen human activities and curiosity, rather than replacing people,” Minow said.

To the audience, she added, “AI tools could do much more of that … And I believe that you, the people here, could develop better tools than the ones that are out there. And make better assessments, more meaningful and cheating-resistant.”

Minow’s talk, “Constituting Democracy in the Era of AI,” was delivered to a large audience in MIT’s Huntington Hall, Room 10-250 on campus.

MIT President Sally A. Kornbluth delivered introductory remarks to the audience. The Karl Taylor Compton Lecture Series, introduced in 1957, is the highest-profile event of its kind at MIT. Karl Taylor Compton was MIT’s ninth president, serving from 1930 to 1948, and then becoming chair of the MIT Corporation from 1948 to 1954.

“He guided MIT through the Great Depression and World War II,” Kornbluth said, “transforming the Institute from an outstanding school for training job-ready engineers to a great global research university.”

She added: “He was also a deeply committed patriot who helped harness the intellectual power of America’s research universities to serve the nation during World War II.”

Kornbluth called Minow “a legal scholar of extraordinary intellectual scope,” and a “dedicated educator whose work has shaped how we think about the law and our own humanity, as individuals and as a society.”

Minow has been a faculty member at Harvard Law School since 1981, and served as dean of the school from 2009 through 2017. Minow earned her BA in history from the University of Michigan, an MA in education from Harvard, and a JD from Yale University.

Minow’s books include “Making All the Difference” (1990), “Partners Not Rivals” (2002), and “When Should Law Forgive?” (2020).

In her talk, Minow also discussed the civic effects of misinformation, threats of violence, and multiple dimension of societal fairness. Modern media technology has led to a 75 percent reduction in journalists in recent decades, Minow observed, with an attendant rise in corruption in places without local media. Civic-minded technologists, she proposed, should work towards establishing a widely-used “information commons,” or many forms of them, to help people access more reliable information.

Meanwhile, when it comes to basic security in society, Minow warned, “We’re on the brink right now. I know a lot of judges, and every one of them has a credible threat of violence against them. In America. This is not acceptable.”

For all the challenges to democracy and traditional civic norms, however, Minow sounded positive notes throughout her remarks, pointing out that there are many opportunities to create new forms of reliable information, education, and community relations.

“We have to actually invent the world that we want to live in,” she offered.

Minow’s remarks were followed by a Q&A session in which Kornbluth read aloud questions submitted by the audience — and asked her to characterize her concerns about present trends.

“I worry about the loss of the Enlightenment,” Minow said. “I worry about the loss of the belief that reason, and testing hypotheses, and rejecting myths in the face of evidence, and the toleration and rights and expansion of participation that came along with the Enlightenment, that’s what concerns me the most. That’s why the disinformation issue for me is so important, that’s why the loss of critical thinking in schools is so important.”

She added: “I’ve spent much of my own career thinking about mass violence. What happens when people don’t understand each other, and are afraid. And it’s terrible, and we don’t want to be there. And I do view the Enlightenment as a time when people learned that actually having mass wars is a bad thing, and coming up with toleration is a good thing, and testing our ideas in light of evidence other people can share can produce a better life for everybody. I worry we’re abandoning that right now.”

Reinforcing the value of human understanding at MIT

Benjamin Daniel | School of Humanities, Arts, and Social Sciences — Fri, 23 Oct 2026 15:35:00 -0400

At the MIT School of Humanities, Arts, and Social Sciences (SHASS) 75th Anniversary Symposium, speakers celebrated the school’s value and contributions, while exploring how artificial intelligence is transforming many aspects of teaching, research, and society.

The symposium was held Sept. 24-25 in the Linde Music Building’s Thomas Tull Concert Hall. Scholars, students, staff, and alumni from across SHASS and MIT gathered for the event, which kicked off with welcoming remarks from MIT leadership.

“How should we think about MIT’s School of Humanities, Arts, and Social Sciences? With enthusiastic appreciation and gratitude,” said President Sally Kornbluth.

The school’s origins date back to the Lewis Committee Report, commissioned by MIT and published in 1949, which called for stronger integration between scientific and technical topics and humanistic scholarship and teaching.

Kornbluth noted that, much like when the school was founded just after World War II, the world faces uncertainty as it grapples with the broad implications of artificial intelligence.

“AI has suddenly arrived with enormous consequences for both research and education,” she said. “As we face the challenges of our own time, and our own society, it’s clear that MIT has never needed your particular insight and guidance more than we do now.”

In opening remarks, Provost Anantha Chandrakasan described the importance of a human-centered approach to technology, one that considers both its efficacy and its impacts. He lauded many of the new research projects happening through the MIT Human Insight Collaborative, a strategic initiative launched in 2024.

“Today — a time of rapid, AI-driven change — the arts, humanities, and social sciences are as important as ever,” he said. “To be able to contribute effectively to solving the world’s great challenges, it is not enough to gain technical mastery.”

Chancellor Melissa Nobles recalled how, during her tenure as SHASS dean from 2015 to 2021, reading the positive feedback from students in their SHASS course assessments reinforced the value of the school’s teaching.

“MIT students have a range of interests that draw them to our classes that may be invisible to their STEM professors, but that we very well know our students have,” said Nobles, who is also a professor of political science.

Agustín Rayo, the Kenan Sahin Dean of SHASS and professor of philosophy, noted that, while research in the school plays a key role in addressing global issues such as climate change and AI, the value of the work in SHASS should not be measured by practical application alone.

“Education should also help us ask what is worth doing, what do we owe one another, and what makes a life worth living,” Rayo said. “Some research matters because it helps us understand the histories, cultures, and social structures that have made us who we are. Some things matter because they enrich human life.”

Scholarship from across SHASS and beyond

Over the symposium’s two days, research presentations, panel discussions, and performances offered attendees opportunities to engage with the role of SHASS in exploring today’s biggest societal questions.

“We gather not so much to look back over the past 75 years, but to showcase where we are now, and share what we urgently aim to tackle together in the years ahead,” said David Kaiser, Germeshausen Professor of the History of Science and professor of physics at MIT, who also served as chair of the symposium planning committee.

The first day included a session on teaching and learning innovations. Faculty presented on the success of educational programs housed within SHASS, including the MIT Indigenous Languages Initiative Master's Program, the Master in Data, Economics, and Design of Policy (DEDP), and MISTI, the hub for global experiences for MIT students.

Stephanie Seed, associate professor of literature, spoke about why she and others are working to reintegrate classic books into their teaching.

“MIT, of course, is an institution unusually oriented towards the future,” she said. “Our students are trained to imagine and create worlds that don’t yet exist. And it’s precisely this future orientation that makes reading the past especially important here.”

Eric Klopfer, professor of Comparative Media Studies/Writing and co-author of MIT’s recent report on AI and instruction, said this is a moment when instructors must reassess how courses are taught.

“I don’t know exactly what that looks like, but I do think it means something that’s more than modifying our curriculum. I think it really means rethinking what the educational experience is,” he said.

During a session on computing and society, panelists described different views and research into technology and how it affects humans.

“We project aspects of ourselves onto computational systems,” said MIT Professor D. Fox Harrell during his talk about seeing “selfness” in machines.

“What makes an AI system is not just the code, not just the programming or the algorithm, it’s the fact that we narrate it as being intentional,” he said.

Program in Science, Technology, and Society Professor Sherry Turkle offered a skeptical view of human-technology interaction, particularly with AI chatbots, which she describes as “intimate machines.”

“If social media came for our attention, I argue that chatbots are coming for our capacity for attachment, which is deeper and much more troubling,” said Turkle.

Professor David Autor, head of the Department of Economics, said it’s an exciting time to be working at MIT, where scholars are collaborating to explore AI’s impact on society.

“I don’t think there’s a close analogy to a moment when everyone felt the world change so quickly,” he said. “The stakes feel incredibly high.”

Celebrating the arts and engaging with the future

The first day concluded with a rousing Music and Theater Arts performance showcase, which began with an excerpt from Stan Lai’s “A Dream Like A Dream,” a play directed and performed by students and instructors in class 21T.100 (Theater Arts Production).

“Variations on the Theme of SHASS 75,” a piece choreographed by Dan Safer, senior lecturer and director of dance programs, combined movement, lighting, and music in a celebration of both the human body and the school’s milestone anniversary.

Student and faculty musicians from a variety of styles and disciplines shared the stage, including Rambax MIT, MIT Concert Choir, and the MIT All-Star Jazz Combo. The showcase included stirring renditions of “Madrigal,” composed by Vice Provost for the Arts Keeril Makan, and “Prayer for Peace.”

On the second day of the symposium, several alumni presented on their current work and the impact of their SHASS experiences.

Jessica Coon PhD ’10, now a professor of linguistics at McGill University, spoke about the leading role MIT Linguistics plays in preserving endangered languages, and how her studies at MIT prepared her to carry on that work.

“SHASS gives us the tools not just to conduct research, but to communicate the impacts of this research across a variety of different audiences and in different ways,” she said.

Ankita Reddy ’19 double-majored in anthropology and biology and is now an MD/PhD student at the University of Pennsylvania. She highlighted her most impactful SHASS courses, including 21A.308 (Global Mental Health).

“This was the first time I was forced to consider how good health care looks different based on social systems and cultural practices,” she said. “SHASS made me more comfortable with, and more curious about, complexity.”

During the symposium’s final session, panelists engaged in a conversation about AI, art, and the implications for creatives in an era where their art can be co-opted and copied.

“There’s so much praise for new and upgraded technologies,” remarked associate professor of literature Sandy Alexandre. “The question I consistently have is: What does it mean to be an upgraded human being? How are we innovating as technology is innovating?”

Philosopher Justin Khoo said AI’s role in art could be considered a “giant paint brush you don’t have much control over,” adding he could see the creation of new sub-genres of art that are human-only, AI-infused, and purely AI, for example.

Khoo closed by saying he encourages students to “think broadly, and really take seriously the humanistic education here at MIT.”

“A broad understanding of a set of concepts which are widely applicable, not just narrowly applicable, I think that’s what we need more than ever now,” he said.

As the symposium came to a close, attendee Kent Sahin praised the event.

“I’ve attended every session, and this is a fantastic program and a great celebration of the 75th anniversary,” he said. “Clearly, whether it’s AI impact, the impact of climate change, or the impact of political and social change, SHASS is in the right place at the right time.”

Gunter Stein, control systems and theory expert, dies at 84

Fri, 23 Oct 2026 14:25:00 -0400

Gunter Stein, an expert on control systems and theory who was a longtime MIT adjunct professor and researcher who also worked at Honeywell Labs, died on July 11 in his home state of Minnesota. He was 84 years old.

Throughout his career, Stein helped to reconcile competing schools of thought surrounding automated control systems, and he worked helped usher in an era of robust multivariable control.

Born in Germany, Stein emigrated with his family to the United States at a young age. The son of a tool and die maker at Fisher Body Works, Stein earned his BS from the General Motors Institute (now Kettering University) in Flint, Michigan, and his MS and PhD in electrical engineering from Purdue University in 1966 and 1969, respectively. He then joined Honeywell Labs, where he spent a more than 50-year career developing systems including aircraft flight controls, spacecraft altitude and orbit controls, and other navigation and control systems for applications as varied as launch boosters, reentry vehicles, ship positioning systems, torpedo engines and autopilots, and precision guided weapons.

As an engineer at Honeywell, Stein’s incisive and rigorous way of approaching problem-solving quickly garnered attention, catching the eye of the then-director of the MIT Laboratory for Information and Decision Systems (LIDS), Michael Athans, when the laboratory and Honeywell began cooperating on a NASA contract to design the controls for the F-8 aircraft. Stein’s submission on behalf of Honeywell was, at the time, labeled the “most conservative” of the potential submissions, but eventually became the only control system successfully flight-tested.

Noting Stein’s talent, Athans approached Stein with a pitch to join MIT in 1977. As Stein’s career and personal life were by that point deeply rooted in Minnesota, he was not interested in relocating full-time, but agreed to an adjunct position with the Department of Electrical Engineering and Computer Science (EECS), where he would spend one week per month giving lectures, helping advise graduate students, reading PhD theses, and acting as a principal investigator in LIDS. This arrangement would continue for the next 30 years, and would deeply shape MIT’s overall approach to control theory.

“Stein immediately struck me as a brilliant engineer,” says Munther Dahleh, the William A. Coolidge Professor in EECS and a member of LIDS, who at the time of Stein’s arrival was a newly hired junior member of the faculty. “As a naive researcher who was more of an applied mathematician than a real engineer, I was struck by how Gunter could see through the mathematics and distill what was truly essential for applications.”

As Dahleh’s career developed — he would eventually become an associate department head of EECS and the founding director of the Institute for Data, Systems, and Society — his regard for Stein only grew: “Through Gunter’s work at Honeywell and his collaborations with students and faculty at MIT and other universities, robust control entered virtually every area of control applications. He shaped the direction of our field for decades, and his ideas remain a driving force behind many of today’s control applications.”

“Gunter came to LIDS at a propitious time,” says Nils R. Sandell Jr. SM ’71, EE ’73, PhD ’74, who co-taught class 6.291 (Classical and Modern Methods for Control System Design) with Stein. “With the experience on the NASA project and on others, the emphasis had begun to shift from optimal control to robust control. Gunter was a key part of this evolution, with his keen insight, based on extensive experience, of how classical frequency domain control techniques for single-input, single-output systems dealt with model uncertainty.”

Over the course of the next decades, Stein would work on systems for the B-2 bomber and F-47 aircraft, and for many years would serve as a member of the U.S. Air Force Scientific Advisory Board. “I can attest that, decades later, the flight control design methodology that Gunter and his colleagues at Honeywell developed is still being applied to our most advanced aircraft,” says Sandell.

Alan Willsky, the Edwin Sibley Webster Professor of EECS (Emeritus), remembered his colleague at LIDS as both a practical and a theoretical force: “Gunter was one of the giants of automatic control. His work at Honeywell on advanced control systems is legendary, but what is equally impressive is his impact on control theory and his influence on generations of control researchers. The period of years when he split time between Minneapolis and MIT had major influences on how control is taught and researched here at LIDS.”

Years of working with uncertainty and multivariable systems led Stein to think carefully and deeply about famous failures of engineering, including the nuclear reactor disaster at Chernobyl that captured the world’s attention in 1986. Stein saw the disaster as a stark warning to all designers of control systems, not only for nuclear power plants but for aircraft, spacecraft, and indeed all systems “with automatic controls that cannot be done manually and that, if done improperly, can have dire consequences for property, the environment, and human life.”

In his highly influential 1989 Bode lecture, later adapted into a 2003 essay for Control Systems Magazine, entitled “Respect the Unstable,” he wrote: “We must place renewed emphasis on stating and teaching the principles of our subject clearly and well.” The applications out there are simply too serious for us to hide from responsibility under a cloak of mathematics.” Of that famous lecture, Dahleh muses, “his Bode Lecture, which marked the first time the award was given, solidified my view of his remarkable ability to understand very different applications from a common fundamental perspective ... He showed how the challenges posed by complex control engineering systems, from agile aircraft to the Chernobyl disaster, could be understood through the same fundamental ideas that explain the difficulty of controlling an inverted pendulum. Thirty years later, his lecture remains one of the most-cited Bode Lectures and one that has had a clear impact on our community.”

Among other honors, Stein was elected fellow of the IEEE in 1985; awarded the IEEE Control System Society's first Hendrik Bode Prize in 1989; elected to the National Academy of Engineering in 1994; and awarded the International Federation of Automatic Control's Nathaniel Nichols Prize in 1999.

His impact on the field of control engineering is also measured by the regard of his colleagues, who uniformly remember him as warm, social, and deeply conscientious.

“Gunter was a great engineer — the greatest I have known — and he was also a great human being,” says Sandell, who notes the great passion with which Stein, in the Bode lecture, enumerated his responsibilities toward society as an engineer.

“On a personal level, Gunter was incredibly humble and unassuming, and always ready to offer help and support whenever asked,” says Dahleh. “He was a giant in our field and played a major role in shaping it. I am deeply grateful for the opportunity to have learned from him and to have had him as a colleague. He will be dearly missed.”

Predeceased by his parents and wife Nancy, Stein was survived by partner Ann Wynia, siblings Gerry (Lynn) Stein and Gisele Davis, and by many nieces and nephews. A skilled sailor and lifelong outdoorsman, Stein spent much of his life exploring the various coastlines and seas of the world, and the forests and lakes of his native Minnesota. Memorial donations can be made in Stein’s name to the Minnesota Nature Conservancy.

Q&A: How AI came to model the “messy” human mind

Adam Zewe | MIT News — Fri, 23 Oct 2026 00:00:00 -0400

Generative artificial intelligence models are rooted in ideas of mid-20th century engineers who thought some amount of irrationality was essential for a machine that could think like a human, contends Benjamin Lindquist, an assistant professor in the MIT History Section, who holds an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science (EECS) and is a principal investigator in the Laboratory for Information and Decision Systems.

To mimic creative thinking, these AI pioneers intentionally injected randomness into algorithms designed to deterministically solve problems, Lindquist writes in a new paper that appears in the journal Past and Present. In the paper, he traces the origins of AI hallucinations back to this early research and suggests there will be inevitable tradeoffs between creativity and predictability when trying to build machines that mimic human behavior. MIT News spoke with Lindquist about these ideas.

Q: Why did early computer engineers believe they needed to inject randomness and irrationality into AI systems designed for logic and calculation?

A: There is a long history of many figures in the world of science and art theorizing that chance plays an important part in discovery and creativity — in the ability to originate ideas. They equated creativity with unexpected leaps of logic, or even a dream-like state that allows you to think about things from a new perspective.

In the early 1950s, one popular idea from John Pierce, who was a prominent electrical engineer at Bell Labs, was about math being valuable in the world of art. The idea was shared by a lot of people working in early computing. When we think of artwork that includes math, we may think of something very orderly and precise, but Pierce came to the opposite conclusion. He concluded that the utility of math is that it could remove order from art. Then in a very calculated way, math could add a burst of the unexpected as a means to deviate from clichés and add an element of irregularity which just might lead to insight.

One popular way to add irregularity into otherwise predictable computers was through artificial (or random) neural networks. Many early neural networks were built by psychologists, who thought that by enabling scientists to build a model of the brain, artificial neural networks allowed scientists to observe what is happening inside, including how brains make productive mistakes. Rather than being black boxes, researchers believed these systems could open this black box of the mind and let scientists peer inside and see all the messy stuff that was really happening.

But there was another camp in the world of AI who thought that creative thinking was just “normal” thinking done better. It is interesting to look at these two very simplistic heuristics for understanding intelligence and creativity, and how they do and don’t mesh. The debate, then, was about trying to determine what human inventiveness boils down to, and then how to reconstitute human inventiveness in machine form.

Q: How did these early computer engineers view AI, and how is it different from the way we think about AI today?

A: When we think of AI systems, we think of ChatGPT or Claude. We value and use these systems because they give answers that, most of the time, are correct. One of the things that was most interesting to me in this research was finding out that a lot of the early advocates of these systems valued them for exactly the opposite reason. They wanted a system that, on occasion, gave answers that deviated from their expectations. They already had systems that could deliver reasonable and expected answers. They saw neural networks as valuable because they could give unexpected, perhaps even strange and bizarre answers.

One reason we might view these systems differently today could be because of how they operate. Early on, these systems were confined to laboratories, so there weren’t really any real-world consequences of a system that gives a strange, or wrong, output. But now there are legal and monetary reasons why OpenAI or Anthropic doesn’t want a chatbot that gives unexpected or creative answers. There has been a “sanding down” of strangeness in these systems. They still potentially have that at their core, but it is something that is now largely suppressed.

Q: How did early neural networks that incorporated randomness to simulate human creativity evolve to become the dominant paradigm in AI?

A: For a long time, a specific kind of AI called “symbolic AI” was the dominant approach. Essentially, symbolic AI says the world is based on rules. The role of the AI researcher is to try and reconstruct the knowledge of the world in facts and propositions and feed it into a computer.

Neural networks, with their use of randomness to solve problems, started to gain traction in the 1950s but fell out favor early on. Frank Rosenblatt, a psychologist who was a very important early figure in neural networks, died prematurely; he died accidentally on his 43rd birthday in 1971. He was quite a charismatic and forceful personality, who made a lot of grand promises about what systems based on neural networks could do. By the time he died, a shift toward symbolic AI was already underway. It is possible if, had he lived, things might have turned out differently.

But neural networks never went away. By the 1980s they had shifted more into the world of cognitive science. To become more powerful, and more useful, neural networks needed a lot of data and computational power. That didn’t really arrive until the advent of the internet, when a lot of data became available and computing started to power up. Around 2012, neural networks started to outperform and outpace a statistical form of machine learning, and they kept growing from there.

Q: What lessons can we learn from studying AI’s history that could inform its development?

A: I think there is a cautionary lesson here. The ideas and experiences of these early engineers tell us that we can work toward quashing hallucinations — we can work toward making AI perform a certain way so it doesn’t produce unexpected or unwanted answers — but there are going to be other consequences of that. These historical figures were onto something. There is a connection between the surprising and creative conclusions AI can offer and errors or hallucinations. There are tradeoffs when we build our systems to behave one way or the other. Maybe it is impossible to be completely at either end of the spectrum, but when we work toward one thing robustly, we often sacrifice something else unintentionally.

Persistence, teamwork, and some healthy competition

Gitana Savage | MIT News correspondent — Fri, 23 Oct 2026 00:00:00 -0400

When Veronica Calzadilla Sosa transferred to MIT from Miami Dade College in Florida, she was an incoming second-year student with an interest in chemical engineering. She didn’t know how involved she would soon become as a student leader in the department.

She would also go on to discover her competitive side.

At the encouragement of a friend who was then a team captain, Calzadilla Sosa joined MIT’s chapter of American Institute of Chemical Engineers, and the two worked with the Department of Chemical Engineering to build up the team of students representing MIT in the organization’s lively chemical engineering competitions. As the organization’s competition chair last year and the vice president of external relations this year, she has fallen in love with the club and found joy in bringing students together to work toward a common goal and have fun along the way.

When she first took on the role, MIT was new to the competition, and students were participating in only one of several events. Now, they participate in three categories: ChemE Cube (where teams build a miniature chemical plant that fits inside a 1-cubic-foot cube), ChemE Jeopardy (a “Jeopardy!”-style trivia competition), and ChemE Sports (where teams operate and optimize a virtual chemical plan).

Calzadilla Sosa also captained MIT’s first ChemE Sports team, working alongside two first-year students. At their first competition, their challenge involved optimizing a distillation column, drawing on concepts Calzadilla Sosa was learning in her separation processes class. The team placed second at regionals.

“The competition was challenging, technical, and at times stressful, but also incredibly fun and rewarding,” she said after the event. “Experiences like this remind me how much engineering extends beyond technical knowledge alone.”

For Calzadilla Sosa, the experience was about more than the result. It gave her the chance to take on a leadership role, work through unfamiliar problems with her teammates, and help create an opportunity for students who might not otherwise have participated.

Building community has been particularly important to Calzadilla Sosa, who began her undergraduate education in Cuba before moving to the U.S. When she later transferred to the Institute after earning her associate degree from Miami Dade, where she was part of the Honors College, “I was a sophomore for the third time,” she jokes. At that point, many of her new classmates had already spent a year getting to know one another and finding their footing.

She began to find her place in an unexpected setting: office hours.

During her first chemical engineering class at MIT, Calzadilla Sosa regularly went to office hours for help with difficult problems. She soon noticed that she was not the only one struggling.

“I was there because I was struggling with a problem, but then I would see more people also struggling with the same exact things,” she says. “And that, for some reason, gave me this idea that I wasn’t alone in that sense.”

Those shared struggles became a starting point for conversations, friendships, and a support system. She learned that finding a community did not necessarily mean having everything figured out.

“Sometimes you find your community simply by allowing yourself to struggle alongside other people,” she says.

Throughout her undergraduate experience, Calzadilla Sosa has also explored the many ways chemical engineering can be applied beyond the classroom.

While studying in Miami, she interned with the U.S. Food and Drug Administration, analyzing data on toxic elements in food, and with the engineering consulting firm Jacobs, where she learned about water and wastewater infrastructure projects in the county. As an MIT student, she has been a materials intern at the medical device company Convatec, and a materials chemistry/chemical engineering intern at the startup Folia Materials.

In the process, she has gotten a closer look at different industries and begun to figure out which ones interest her most. At MIT, she has become particularly interested in energy systems and how existing resources and infrastructure can be used more efficiently.

“I think exploring different areas is very valuable,” she says. “I find myself thinking ‘Maybe I could do this or I could do that. Maybe I don’t like this. Maybe I prefer doing another thing.’”

Outside of her studies, Calzadilla Sosa enjoys cooking and baking for family and friends, traveling, and going to concerts and dance performances. She also likes bringing people together, especially when she returns to her first home in Cuba and has the chance to reconnect with loved ones.

As graduation approaches, Calzadilla Sosa is excited to leave student life behind and see where her degree takes her. She has not settled on one career path, but she is comfortable with that uncertainty. After studying at multiple institutions and exploring different corners of chemical engineering, she knows there is still plenty left to discover.

“I spent so many years as an undergrad, so I’m really excited to go to industry and take that next step and see the results of my education,” she says.

Startup plans to rejuvenate the nuclear power industry with a new manufacturing approach

Zach Winn | MIT News — Thu, 22 Oct 2026 00:00:00 -0400

Building a nuclear power plant is one of the most complex construction projects in the world. It requires thousands of trucks to ship parts, often to remote regions, where huge teams of skilled workers like electricians and welders come together to assemble everything over many years.

Financing such projects is difficult, in part because no one wants to risk loaning money to a project that might never generate power and revenue, whether that’s due to technical failures, the inability to recruit enough talent, or regulatory hurdles.

Stated this way, it’s no surprise the average nuclear plant costs twice as much as originally expected and faces delays of many years. Those cost overruns and delays have ground the nuclear industry to a near halt: When Georgia finally turned on its Vogtle Unit 3 reactor in 2023 (seven years late), it was the first new reactor constructed in the U.S. in over 30 years.

Blue Energy wants to make deploying nuclear power plants more like wind, solar, and gas projects. The company plans to prefabricate huge blocks of its nuclear plants at shipyards, where workers and equipment already produce huge steel structures for the oil and gas industry every day, then ship the blocks on barges to the site of the plant for final assembly.

“The nuclear problem is a construction problem,” says Blue Energy founder and CEO Jake Jurewicz ’14, SM ’15, who grew up in the construction industry and studied the differences between nuclear and oil and gas projects at MIT. “On paper, nuclear technology is almost perfect: It operates above 90 percent capacity, it’s zero carbon, dispatchable, extremely high reliability, and has predictable cash flow. We don’t have enough nuclear power because we’re really bad at building it. But there’s already a huge manufacturing supply base for offshore oil and gas and offshore wind. If we can point that toward building nuclear plants, and finance it the same way, we can reinvigorate the industry.”

Blue Energy’s prefabricated blocks will weigh around 1,500 tons and be designed to be easily assembled at their destination to complete a nuclear power unit over the course of a single year. The company hopes to further reduce barriers to financing and speeding up construction timelines by designing its early plants to run on gas initially before adding nuclear reactors, which it hopes will generate revenue within three years from the start of construction.

The company has already gained approval from the Nuclear Regulatory Commission for its gas-to-nuclear approach and has also secured an agreement with GE Vernova Hitachi Nuclear Energy for its first project, a 2.5 gigawatt hybrid nuclear power plant in Victoria, Texas, that is scheduled to begin producing power in 2030. This September, Blue Energy submitted the first part of its application to the NRC to begin construction on its Texas project, making it just one of five companies to do so in the current nuclear development era.

“Our first site is a great power generation project for that community because it's going to generate a lot of excess power,” Jurewicz says. “It’s going to show a new model for the world: This is a way the U.S. and its allies can deliver a turnkey nuclear power plant and turnkey energy sovereignty.”

A new blueprint for nuclear

Jurewicz grew up in Chicago, where he worked as a construction draftsman through high school. When he arrived at MIT, he double majored in nuclear engineering and physics, then stayed at MIT for a masters in the Department of Nuclear Science and Engineering. He also minored in energy studies through the MIT Energy Initiative.

Jurewicz’s master’s thesis explored ways nuclear power plant construction could apply lessons from the offshore oil and gas industry.

“If you’re trying to solve the construction problem, find parallel industries where people have solved it before,” Jurewicz says. “That’s why I looked at offshore oil and gas as well as nuclear shipyards in the U.S. and South Korea. We build oil and gas plants all the time, so what looks different about the way we build and execute an oil and gas plant relative to how we build a nuclear plant? They’re both mostly steel welding.”

Shipyards have several advantages over other sites for building nuclear power plants. For one, they already have thousands of skilled workers and loads of specialized equipment. They can also manufacture things in controlled environments, enabling increased automation and reducing uncertainty.

Following graduation, Jurewicz worked in corporate strategy at the utility company Exelon Energy, where he sponsored research at MIT. He originally considered starting Blue Energy as a graduate student with an approach that included creating a new passive safety system and going through a new regulatory insurance framework.

When he founded Blue Energy in 2023, he wanted to minimize risks with a more conventional reactor design. That includes planning to prefabricate functional chunks of the reactor — what the company calls “Blue Blocks” — at a shipyard using standard manufacturing approaches that meet nuclear licensing and quality assurance requirements.

“The 1,500-ton Blue Block prefabricated modules are fully functional mechanically and electrically, they have plumbing — everything is delivered ready to run directly to the site,” Jurewicz says. “Then we have this gas-to-nuclear technology that allows us to get things going earlier with gas by building the whole nuclear steam turbine system right away and energizing it with steam from gas combustion turbines, which means we can finance it as a gas plant, so the cost of capital goes down. It dramatically lowers the cost of the project, and it reduces risk.”

Blue Energy’s nuclear reactor will be a small modular light water reactor that has already been approved by the Nuclear Regulatory Commission and is made by GE Vernova Hitachi Nuclear Energy.

Blue Energy’s first power plant in Texas will provide 1 gigawatt of power using two GE Vernova gas turbines in 2030 and then add another 1.5 gigawatts of power from up to five small modular reactors beginning in 2032.

“It will be pretty easy to add more modular reactors to the site, so we can lower the cost to that community to provide things like water desalination, which is a big issue in south Texas,” Jurewicz says. “This power infrastructure is clean, reliable, and resilient through hurricanes or any other weather scenario that can happen in that community. It creates hundreds of high-paying permanent jobs and the foundation for a lot of local economic growth.”

Faster, cheaper nuclear

Historically, nuclear power plants have taken 10-plus years of construction. Blue Energy believes it can shorten that to about 36 months. The company is projecting power from later projects to reach about $5,000 per kilowatt. For context, the Vogtle nuclear plant delivers power at more than twice that cost.

Jurewicz believes Blue Energy’s lower prices could finally inject much-needed life into the U.S. nuclear industry.

“Despite all of the excitement around nuclear, you haven’t seen real power purchase agreements get signed because there’s a stalemate on who’s going to take the construction overrun risk,” Jurewicz says. “The government doesn’t want to take it. The construction companies don’t have the balance sheet to take it. The utilities can’t take it. Even the hyperscalers won’t take a two-or-three-times cost overrun risk. Blue Energy is introducing a fifth party to break the stalemate, which is the shipyards and fab yards, which are best positioned to take the risk because they can make things in a controlled fabrication environment, so they'll take the risk on construction in their own yards.”

Editor's note: This article was updated on Oct. 26 to more accurately reflect the early stage of the company and its planned work.

Tod Machover’s new symphony plays with AI while reflecting on democracy

Peter Dizikes | MIT News — Wed, 21 Oct 2026 00:00:00 -0400

Innovation, reflection, and celebration will blend at the Boston Symphony Orchestra this week, where MIT composer Tod Machover’s inventive new work “already and not yet” will see its world premiere on Thursday.

Machover, one of the world’s most prominent composers, has incorporated multiple ongoing interests into the new symphony, including AI tools interacting with the musicians, and a piece that is structured as a meditation on the crisis and promise of democracy.

“An orchestra is an interesting ensemble to use for thinking about how different groups of people can relate to each other,” says Machover, who is the Muriel R. Cooper Professor of Music and Media and faculty director at the MIT Media Lab, where he also serves as director of the Opera of the Future group.

He adds: “John Cage used to say one way to think of a piece of music is that it should be like the society you’d like to live in. I was always inspired by this idea, and thought of the orchestra — and constructed this new piece — in this way.”

If the innovation in “already and not yet” comes from its use of artificial intelligence, and the reflection pertains to democracy, the celebration involved in the event partly relates to MIT: The performances are coordinated with the MIT Media Lab’s 40th anniversary celebrations occurring this week. Machover has been a member of the Media Lab since it opened.

“In many ways, the piece is also an homage to the lab and to its spirit of invention and reinvention,” Machover says.

The acclaimed conductor Kent Nagano — a longtime collaborator and advocate for Machover’s music — will lead the Boston Symphony Orchestra’s performances, which are taking place Oct. 22-24 at Boston’s Symphony Hall.

Inspired by the possibilities

Machover’s extensive career has often featured the invention of new technologies within his compositions. Among many other technologically related projects, he has led the development of hyperinstruments, which can be responsive to the playing techniques of musicians and expand their range of expression; renowned cellist Yo-Yo Ma is among those who have performed Machover’s work using them.

Working across diverse musical forms, Machover’s 2010 opera “Death and the Powers” was a Pulitzer Prize finalist. He has also used locally sourced and community-created sounds as additional aural layers in his “City Symphonies,” which have been created around the world. The next project in the series, underway in Seoul, South Korea, is scheduled for premiere in September 2027.

“I’ve always been inspired by the possibilities of technology and the expressive potential it opens up,” Machover says. “It is kind of the core language of our time and something we have to grapple with so that it evolves in the most positive human-enhancing ways. I feel so fortunate that I have been able to pursue this vision at MIT, and particularly at the Media Lab, for all these years.”

Machover’s “already and not yet” reflects on democracy in multiple ways. One is through the form of the composition, which has three movements relating to different stages of democracy. In the first movement, “Smash Up,” strings and winds eventually get crowded out by what Machover calls the “oppressive, aggressive” sounds of brass, percussion, and electronics. The second movement, “Wake Up,” presents a moment to reflect and regroup, but little by little weaves together a thriving melodic and textural presence. The third movement, “Stand Up,” involves more vigorous interplay between all the musicians, inviting the players to participate in establishing a new kind of balance between individuality and community.

In this way, “already and not yet” conceptualizes the orchestra as a way of representing the stress and revival of democracy, and uses a “Live AI” system created at the Media Lab — along with layers of electronics crafted in Machover’s studio — to emphasize this struggle and progression. In the second movement of “already and not yet,” the AI tools aim to underscore the distinctiveness of each instrumentalist, establishing an ensemble of soloists; in the third movement, more broadly representing the regrowth of a polity, the AI system aims to find common ground, building new sonic bridges across the orchestra and extending it out into the hall using extra, specially installed loudspeakers.

“I’ve used the way the orchestra plays, how much is scripted and how much freedom they have, and how they negotiate their relationship with the conductor, as part of the story,” Machover says. “It’s not said explicitly, but I think the players and the audience will feel it. It’s a whole society up there on stage, and the liveliness and ever-changing nature of the AI systems for this piece both dramatize and encourage a new way of playing and listening.”

Machover credits two PhD candidates in his group at the Media Lab for their extensive work on the project: Manaswi Mishra, who designed and built the AI systems for the performances, and Ana Schon, who will be doing the complex sound mixing in Symphony Hall during each performance.

Reflections of an AI veteran

For Machover, the application of machine learning and AI is hardly a newfound idea; he has been exploring the concept for decades, giving him a distinctive perspective on this topic du jour.

“The current generation of AI, especially generative AI, has capabilities that are different than AI from 20 or 30 years ago,” Machover says. “It’s good at certain things that were hard to do 20 years ago, but less good at some things that were easier to do back then.”

When he designed a hypercello for Yo-Yo Ma, for instance, those systems would analyze Ma’s sound, playing techniques, and expressive intent in order to shape the extensions of the instrument. Today’s AI tools “are less good at analysis, but very good at detecting significant features and patterns in the audio itself, information that can be leveraged to powerful musical effect,” Machover says.

“Current systems aren’t so good at what we did [for Yo-Yo Ma], like trying to understand the performance or the music itself,” Machover says. “But evolving AI models do an incredible job — especially in Manaswi’s magical systems — at establishing an immediate, creative dialogue with musicians, producing sophisticated and surprising augmentations that in turn inspire performers or composers to discover truly new ideas.”

Ultimately, Machover says, “I think this piece — through its technology, its ‘story,’ and its sounds — is meant to take people on a journey of recognition about where we are at this moment in time and where we might be going. It’s meant to be an optimistic and inspiring piece.” He adds, “The idea, expressed in the work’s title, is that we’re so lucky to be at this place in this moment, even with everything that’s wrong and in critical need of repair. We really are. At the same time, it’s so urgent to realize that the vision of a just and flourishing society, for which the foundation exists, isn’t finished at all. Not only that, it’s in crisis. So this is the last time to be complacent, or discouraged.”

For all of that, Machover also expresses hope that the audience at Symphony Hall can simply engross themselves in the music itself, and find it compelling on its own terms.

“My biggest wish is that people listen to this piece as music — forget about the AI or the story and just get swept along,” Machover says. “There’s one melody in this piece that’s trying to express itself in the first movement, and appears in fragmented form in the second movement. In the third movement, it’s the center of everything, a kind of vision of resolve, hope, and purpose. If people come out of the performance and don’t have some part of that melody in their heads, I’d be surprised. I hope that audiences listen for that, try to hold on to it, and see where it might lead.”

The piece was commissioned by the Boston Symphony Orchestra and supported by the New Works Fund of the Massachusetts Cultural Council.

MIT spinout makes surgical stitching more like applying a sticky note

Zach Winn | MIT News — Tue, 20 Oct 2026 00:00:00 -0400

Dozens of surgical procedures require doctors to connect two pieces of tissue together in a delicate stitching process. It happens in triple heart bypass, C-sections, and surgeries to repair hernias and tendons. Today, those procedures are mostly done by surgeon-controlled robots or by hand, and the margin for error is small. Even small errors in suturing or stapling can lead to leaks with devastating consequences to patients.

SanaHeal wants to make connecting tissues more like using tape. The company has developed a bioadhesive technology that can rapidly stick to wet and uneven surfaces and form air- and fluid-tight seals to simplify surgical procedures. The company’s product, first developed at MIT, was inspired by sticky materials in nature like barnacles and spider webs.

SanaHeal’s technology has already gained regulatory approval to stop trauma bleeding, and next year it will enter human trials to help control bleeding during surgeries and for repair of various injured tissues.

The startup, which collaborates with larger companies for clinical trials and commercialization, believes those applications only scratch the surface of the technology’s potential.

“If someone is struggling to put something on biologic tissues, we can help,” SanaHeal founder and chief technology officer Hyunwoo Yuk SM ’16, PhD ’21 says. “The current way of doing this is by suturing through tissues, which is painful and difficult to do perfectly during surgery. Our solution is more like a Post-it note. It’s much less invasive and easier to use with surgical robots. Also, faster and more efficient surgical procedures have potential to help lower ever-increasing costs of surgeries that are significantly straining the health care system.”

A sticking problem

Yuk conducted his master’s and PhD in the lab of Xuanhe Zhao, MIT’s Uncas (1923) and Helen Whitaker Professor of Mechanical Engineering. Initially, his research focused on the mechanical properties of soft materials called hydrogels, but about halfway through the program, he pivoted to study their biomedical applications, in part motivated by a serious accident his family member was involved in.

“We were looking at how to make these hydrogels better adhesives, and because we are in Boston, we started working with doctors and medical schools in the surrounding area,” Yuk recalls.

Those conversations made him realize how often surgeons have to seal or attach tissues. That process usually involves staples or sutures, which can cause more damage to tissues, trigger immune responses, and lead to scar tissue or infections.

“These are often tiny tissues that sometimes require needles thinner than a human hair to be used during microsurgery,” Yuk explains. “They are very difficult surgeries, and they can lead to all kinds of complications.”

Yuk realized his hydrogels offered a better solution. The materials, which are made from polymers and water, are biocompatible, meaning they minimize immune responses and scar tissue buildup. Early versions of the technology acted like double-sided tape, absorbing water from wet tissues using polyacrylic acid — an absorbent material used in diapers — to adhere to them in a matter of seconds.

Yuk had planned to stay in academia after his PhD, but as he learned more about what it would take for the technology to reach patients, he decided to focus on leading that work.

“It’s very difficult for innovations to make it into the clinic and help patients if you’re in an academic research lab,” Yuk says. “We didn’t know anything about startups, so we used resources like MIT’s Deshpande Center, the Venture Mentoring Service, and MIT’s Technology Licensing Office in hope that there might be external interest in our technology. We quickly learned the regulatory hurdles presented too much risk at the early stages for potential partners, so we had to commercialize it ourselves.”

The founders received funding from the Deshpande Center to start developing the company. SanaHeal was officially formed in 2021.

Yuk and his collaborators initially focused on applying their technology to wounds to stop bleeding. But they soon shifted to focus on surgical applications, which are more numerous.

“It’s a major unmet need,” Yuk says. “We are giving surgeons more options for a host of complex procedures. They are really excited about this.”

Partnering for impact

SanaHeal provides its technology to larger medical device company partners, who are developing products for a wide range of applications including in open, laparoscopic, and endoscopic surgeries as well as in combination with existing devices for sutureless implantation in applications like hernia and nerve repairs.

“We have envisioned ourselves as a platform company from the beginning,” Yuk says. “We don’t have the resources to develop 10 products at once, so we partner with bigger companies to commercialize solutions.”

Yuk, who has been working on the technology for nearly a decade, admits the commercialization process has taken longer than he expected. But he believes the payoff — seeing a technology he first developed in a lab have an impact on patients — will make the journey worth it.

“My life goal as a researcher is to see the full lifecycle of a new technology, from the scientific discovery, to research, to understanding how it works, to engineering it, to working with subject matter experts, and finally developing a product that gets approved to help humans,” Yuk explains. “It’s rare, but it would be incredibly rewarding and would be a great story to tell to inspire my research colleagues.”

Giving AI a “crystal clear” view of the materials world

Zach Winn | MIT News — Sun, 18 Oct 2026 00:00:00 -0400

In principle, artificial intelligence could make finding the right material for an application as simple as ordering dinner: Scientists would tell a large language model what they want, and it would serve them the perfect material for the job.

Unfortunately, that approach doesn’t work well yet, and the problem comes down to data: Most materials consist of 3D arrangements of crystals, not the text streams that today’s large language models are trained on. The experimental literature is also heavily biased toward a few popular classes of crystals, making AI outputs skew toward those materials.

Now MIT researchers have created a multimodal AI model that tackles these problems. The model combines 3D atomic structures with natural language descriptions to create rich linguistic descriptions about the structures from experimental research papers and first-principles computational databases. The unified description outlines not just the chemical composition and structure, but also its properties, processing considerations, and potential applications, helping AI learn more about what makes materials impactful.

In a new paper in Nature Communications, the researchers showed their system could identify better material designs than models trained on text streams alone by allowing the model to “see” the atomic arrangements and bonding patterns. For example, when the researchers asked the model to identify materials suited for solid-state lithium-ion battery electrolytes, its top results included materials that had been independently identified as promising candidates. The researchers also showed their model learned geometric representations of material structures in ways text-only models could not.

“Structure is really at the heart of the materials science field,” says Ju Li, the Carl Richard Soderberg Professor in Power Engineering in the departments of Materials Science and Engineering and Nuclear Science and Engineering. “If you understand the structure, you can infer quite a lot about the properties and performance of materials. We wanted to build the connection between a material’s atomic structure and its properties and processing for the model.”

Joining Li on a paper about the model are Yang Jeong Park, first author and former MIT postdoc; Mayank Kumaran, a recent University of Illinois Urbana-Champaign computer engineering graduate; Chia-Wei Hsu SM ’26; and Elsa Olivetti, the Jerry McAfee (1940) Professor in Engineering in DMSE.

Getting models to see in 3D

Materials scientists are always on the hunt for new crystals that will lead to the products of the future. Over the last decade, AI has reshaped that process, proposing and designing new crystalline structures, which have highly ordered atomic structures and make up more than 90 percent of solid materials on Earth. Most of those models treat solid crystalline materials as 3D graphs.

The rise of large language models has given researchers another tool for innovation. But existing large language models rely heavily on text from published papers and books, limiting their knowledge of the physical world, since many potential crystal structures are stored in computational databases. Text-based models also lack understanding of visual geometry.

To overcome those challenges, the researchers decided to create a vision-language model for crystalline materials.

They trained a language model to create textual descriptions of crystal structures using an approach known as contrastive learning, in which models compare similar and dissimilar examples of structures and textual descriptions to achieve a unified understanding. This is similar to aligning the visual and language regions of the brain.

Following training, the researchers compared the predictions of two versions of the model: one trained on a dataset containing the AI-generated descriptions and the other trained on a dataset containing text from papers only. They found that the model trained on AI-generated descriptions successfully learned how to align geometric structures with text descriptions while the other did not.

“We wanted to create an AI model that works broadly across materials,” Li says. “That’s why we decided to synthesize narratives rather than rely solely on existing papers. We found when we used that narrative dataset, the models are better at predicting the function.”

Accelerating materials science

The researchers then showed their model could be given a natural language query describing a property and identify crystalline materials with that property, outperforming a baseline.

“There are sometimes these step changes within materials science around algorithmic integration toward useful tools, which the community uses to further advance the field,” Olivetti says. “This approach helps us see more deeply into three-dimensional structures and link structures to property and processing. I hope this can be one of those step changes.”

Li compares the model to AlphaFold, an AI model that links protein structures with function that has helped advance biological research and drug development.

The researchers believe their new approach can work with many crystalline materials, and they hope other material scientists will use it to accelerate their work.

“Being able to see a three-dimensional crystal and how its atoms are connected is super foundational,” Li says. “I think this will be useful to anyone who practices materials science.”

The work was supported, in part, by the U.S. Defense Threat Reduction Agency through the University Research Alliance; the U.S. Defense Advanced Research Projects Agency; and the National Research Foundation of Korea.

Fostering community amid Kendall Square’s innovation

Zach Winn | MIT News — Fri, 16 Oct 2026 12:00:00 -0400

Kendall Square in Cambridge, Massachusetts, is often cited as the most innovative square mile in the world. But when the Kendall Square Association gathered local stakeholders for its 18th Annual Meeting Thursday, the conversation focused on innovation primarily in the context of community.

The event, which took place at the nonprofit startup incubator The Engine, attracted people from across Cambridge, including business leaders, elected officials, local residents, and students, for a night of local food and conversation about the advance of technologies like artificial intelligence and what they mean for society.

“How can we make AI not an enemy of work, but a tool for workers to expand their capabilities and build new expertise or do new things?” asked MIT Institute Professor Daron Acemoglu in a keynote talk. “The world is at a point in time where we need new innovations and new adaptations to growing challenges.”

Acemoglu’s keynote was one of several presentations at the event, which also featured music from the Kendall Square Orchestra, a vote on new KSA board members, and awards for organizations making a positive impact through innovation.

KSA is a nonprofit made up of over 150 organizations across Kendall Square, from major companies to universities, research organizations, and the independent shops and restaurants that give Kendall Square its distinct character.

“People come to Kendall Square to make the world better,” said KSA Executive Director Beth O’Neill Maloney in her opening remarks. “People come here to improve the human condition, people come here to solve big problems, and people come here to innovate for good.”

Acemoglu, who shared the Nobel Prize in economics in 2024 for his work studying how institutions affect prosperity, spoke on the subject of his new book, “What Happened to Liberal Democracy?”

Acemoglu says liberal democracy is under siege as trust in liberal institutions decline, political polarization rises, and people lose faith in elected officials. He acknowledged that the shared prosperity liberal democracy promised has not always been delivered, especially over the last 50 years, but insisted it is the best path forward and in need of renewed support.

“I believe liberal democratic institutions, together with the norms that support them, are one of the greatest human achievements,” Acemoglu said. “It would be unthinkable for people who lived 300 years ago that we could live in societies consisting of hundreds of millions of people with diverse interests, different views, doing different things, and people still participate in politics and reach compromises, and we have state institutions that, by and large, actually provide services and do things for the people, and that we build opportunities for prosperity from which most people benefit while social freedoms are largely upheld.”

Cambridge Mayor Sumbul Siddiqui also spoke at the event. Siddiqui began her talk by telling the story of Cambridge residents working with biotech labs in the 1970s to create a groundbreaking policy on recombinant DNA research, which helped turn Kendall Square into the life science hub it is today.

“All these years later I think that story captures what makes Kendall Square so special: It’s our willingness to work together even when we face big challenges,” Siddiqui said. “It isn’t just the universities or labs, it’s also the culture of collaboration among nonprofits, governments, and residents.”

Broad Institute Chief Communications Officer Lee McGuire started his talk by reviewing a 2009 report on what made Kendall Square such a powerful driver of innovation, noting that factors like talent density, education, and a collective voice remain strong in the area today.

“This report was brilliant in that it captures so many of the special things that matter for a place like Kendall,” McGuire said. “It is the job of the Kendall Square Association and all of us to help protect those things, make sure we name them, understand them, and see them as essential elements of making sure this community stays what it is no matter what happens. As long as there’s a good idea, this is the place that’s going to get that idea moving and scale it up to change the world.”

Satellite data reveal how Antarctica’s ice cliffs behave over time

Jennifer Chu | MIT News — Thu, 15 Oct 2026 14:00:00 -0400

With rising temperatures, the buttressing ice shelves at the edges the Antarctic ice sheet are melting and calving off at record rates, leaving behind sheer cliffs of solid ice. The stability of these exposed ice cliffs could influence how Antarctica further breaks apart and contributes to sea level rise around the world.

Scientists have assumed that an ice cliff’s stability depends on its height: Ice cliffs that are taller than 100 meters — about the height of the Statue of Liberty — should collapse under their own weight. And once a cliff sloughs off, it should expose even taller, inland cliffs that collapse in turn. At least, that’s been the idea.

But researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) have found that the stability of an ice cliff depends on more than its height. And even when a cliff topples, it doesn’t necessarily set off a runaway collapse.

In a study appearing today in Science, the team presents an analysis of over 15,000 ice cliffs in Antarctica that were monitored by satellites over a four-year period. They observed how the ice cliffs and the larger ice sheet evolved over time, and found that those ice cliffs that collapsed did so at a much faster rate than scientists had predicted. But these collapsing cliffs also quickly stabilized, instead of continuing to erode the larger ice sheet.

“We found that when the cliffs were retreating, they were doing so faster than what has been modeled, which would mean more ice loss and higher contributions of sea level rise,” says study author Caroline Needell, a graduate student in the MIT-WHOI Joint Program. “But because we found that the retreat was short-lived, and all the glaciers eventually stabilized, that would suggest that things won’t be so bad. We don’t see this runaway effect.”

The study’s co-authors are Catherine Walker, assistant scientist at WHOI, and Jeremy Bassis, associate professor at the University of Michigan.

Ice height

In glacial science, ice cliff collapse is described by a process known as marine ice cliff instability (MICI), which assumes that if an ice cliff exceeds a critical height, it will collapse. If then a taller cliff is exposed, it too should collapse, triggering a cascade of instabilities that shrink and erode the ice sheet over time.

In previous studies, scientists have characterized MICI mainly by modeling the physical properties of ice, scaling these relationships up to the size of an ice cliff and combining these predictions with a few observations of actual ice cliffs in Antarctica. Through these efforts, scientists had determined that ice cliffs become unstable and collapse when they are taller about 100 meters.

“Predictive ice-sheet models that have been used so far to represent ice cliff instability just take into account cliff height,” Needell notes. “That’s the only variable that matters. And once the cliff height reaches the critical threshold, usually 100 meters, it should initiate this runaway effect of ice sheet retreat.”

But she and her colleagues wondered: How tall do ice cliffs really get? And when they collapse, do they really set off a runaway retreat?

To answer these questions, they looked to data collected by NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) — a satellite that passes over the Earth’s polar regions and uses a laser altimeter to measure the height of ice sheets, glaciers, sea ice, and the level of the ocean in and around Antarctica.

The team collected the satellite’s data from 2019 to 2023, which included measurements every three months of over 15,000 ice cliffs. From this dataset, they observed that the average ice cliff was about 30 meters tall — much shorter than the proposed critical height. But they did see a few rare ice cliffs that exceeded 100 meters.

They also looked for other features that might influence a cliff’s stability. Interestingly, they found that ice cliffs that more or less stayed in place over time also had thicker mélange — a term for the slushy mix of broken icebergs and sea ice that gathers at the front of an ice cliff.

“We measured mélange height and found taller cliffs have thicker mélange, which could be a built-in stabilizing effect,” Needell explains.

The satellite data also showed that the height of the surrounding water can stabilize a cliff: The deeper the water, the more it can buoy and push back against the cliff, keeping it from collapsing.

Glacial speed bump

“After we did that global analysis, we thought, ok, let’s zoom in on the tallest cliffs and see how they’re behaving,” Needell says.

The team focused in on four regions around the Antarctic ice sheet where the satellite captured the tallest ice cliffs, each about 100 meters tall, with one that was 127 meters tall. They compiled measurements of these four cliffs and how they evolved every three months, over four years.

From these measurements, they saw that indeed, the tallest cliffs collapsed, and the ice sheet in those regions retreated, at a surprisingly faster rate than MICI models had predicted.

“But that retreat was short-lived,” Needell notes.

After that, the ice spread back out toward the ocean.

“It’s this rapid retreat, punctuated by periods of stabilization,” Needell says. “That was puzzling to us. Then, we thought: We need to look at all the things that affect cliff height.”

The first thing they considered was the topography of the land underneath the ice. If that topography is relatively flat, then the slope of the overlying ice sheet reflects that actual height of the ice itself. But if there’s a bump or rise in the land, the ice sitting directly over that bump is in fact shorter than it appears. The topography then could act as a literal speed bump, thinning the overlying ice, and making it less likely to collapse.

When the researchers checked topographic measurements of Antarctic, they found that indeed, ice cliffs stabilized around regions where the land happened to rise up.

They also found that the taller a cliff is, the faster they “thin out” — as they collapse, once tall cliffs settle into shorter, more stable cliffs that prevents further collapse.

“Now we know what to look for,” Needell says. “We really need to know the bed topography, and we need to know the ice thinning rate, in order to get ice cliff instability right, because this value really matters in the context of projecting sea level rise.”

This research was supported, in part, by NASA, the U.S. National Science Foundation, and the DOMINOS project, a component of the International Thwaites Glacier Collaboration.

Q&A: The “collision” between AI, your brain, and society

Peter Dizikes | MIT News — Thu, 15 Oct 2026 00:00:00 -0400

As artificial intelligence use becomes more widespread, scholars are working to understand its effects on the people who use it. MIT Professor Eric So offers one of the most complete syntheses yet, in his new book, “The Collision: What AI Does to Us,” published this month by W.W. Norton. Recognizing that AI offers opportunities to complete work faster, and in some cases has been turned into useful domain-specific tools, So nonetheless looks at the costs of relying on it too broadly. He contends that heavy use of AI can lead to a kind of cognitive atrophy, in which people fail to develop and retain essential thinking skills. In general, he says, evidence shows that our ability to learn gets dramatically better when we wrestle with material, work through initial failures, keep iterating, and in the process acquire better thinking skills while retaining more knowledge.

These conclusions are drawn from recent research by So and others, in academia and industry, including analysis from AI firms themselves, such as an Anthropic study showing that about half of queries from college students were low-effort queries for simple answers. So also weaves personal reflections into his narrative — having long studied financial markets and behavior, he is familiar with analyzing how new tools and information affect our decisions. MIT News talked to So, who is the Sloan Distinguished Professor of Global Economics and Behavioral Science in the MIT Sloan School of Management, about the book.

Q: What prompted you to write “The Collision”?

A: I wrote this book in response to patterns I was seeing in myself and other individuals. I open the book with this story where I was instinctively reaching for AI to help me think through problems I normally would have handled myself. A colleague had reached out for assistance and I had essentially taken her question and written half a sentence, then said, “ChatGPT, help me finish this professionally.” I made a few light edits and sent it off. And I sat back and really felt incredibly concerned about how I had removed my own thinking from the process.

I have found students are outsourcing their thinking to AI, rather than thinking through things themselves, turning to it to answer homework that would normally help them build mastery in a subject. We need to start understanding what this technology is doing to us and how it interacts with human cognition and our psychology, and give people some practical insights.

Q: Part of that means understanding the incentives people have to use AI, or how the allure of AI takes hold, even when there are long-term costs, in your view.

A: There are converging factors drawing us to AI. For example we have a real biological instinct to conserve our mental energy, and socially, we face situations where AI is used by other people to race ahead, and it rachets up the pressure on everyone else to use AI. Combine that with the problems detecting AI use in a practical way, and all these factors create what I call “AI gravity,” this constant pull to use AI. It’s not simply a matter of telling people to think for themselves. At this moment we’re in, AI is colliding with society.

Q: So we need to look at these systemic factors, not just individual decisions, when it comes to AI use, like businesses urging employees to use it.

A: Many organizations are not just suggesting use of AI: It is now the basis of your evaluation, meaning if you want a bonus or promotion, it is incumbent upon you to use these tools. I think a subtle process is taking hold in academia, where people say, “My future employer will want me to use AI, so I’d better start using it now.” But the problem is that as you start to use AI now, we’re losing touch with the practices and capabilities we’re seeking to build, and in turn that makes us more dependent on using AI in the future, and it becomes a self-fulfilling prophecy.

Q: What is AI doing to our minds?

A: AI is not just changing what we can do, it’s changing who we are as thinkers. AI removes a lot of the friction necessary to build foundational mastery. It is causing many people to lose their capacity for pushing through problems. AI offers instant relief, and we become accustomed to that. Many people are turning to it as an access point for information, so it’s affecting our ability to remember. Also, AI is changing our interpersonal relationships. A key point in the book is that these models are simultaneously sycophantic, meaning they want to make us happy because that’s how they drive engagement and get positive feedback, but they are also really persuasive. We lose the ability to connect with other people because we’re so accustomed to this frictionless environment where the models always agree with us.

Q: Your book emphasizes the value of “productive struggle.” You write, “Struggle doesn’t mean you’re suffering.” Instead, we get a handle on things by struggling to learn them.

A: We need to recast how we think about struggle, as something that is formative rather than a problem to be solved. I draw a lot of parallels in the book between some of the societal changes that occurred post-industrialization, when suddenly people no longer needed to be physically active to be economically productive. We saw more people suddenly develop physical exercise as something we do intentionally, that required reframing of what physical struggle meant. And I think we need that sort of shift about what we think about cognitive struggle.

Q: Given all this, what can we do to maintain some reasonable level of cognitive effort today?

A: At the personal level, I emphasize four principles for navigating this age of AI. One is embracing the struggle, as we’ve talked about. The second rule is: Value who you are without AI. I’ve seen so many presentations over the last couple of years where someone has put together this very polished-looking presentation with Claude, but when they present it, it’s clear they didn’t put in the work necessary to understand the material. The formative struggle creates what I refer to as an “inseparable task bundle,” since making the slides allows me to engage in substantive discussion. In the absence of valuing who you are without AI, we get the product, but not the ability to capitalize on it.

The third rule is about reinvesting your cognitive surplus. There are a lot of tasks for which AI can save you time — rote activities — but it matters what you do with those time savings. If you’re just funneling the same energy into the same tasks, you’re missing out on the potential transformational benefits of AI. The most innovative changes are not the ones that simply allow us to automate, but stretch the capabilities of humanity in a productive way and build expertise. Or, people can channel that freed-up time to focus on family, learn a new language, cultivate skills.

The last rule is about using AI as your cognitive trainer. AI is this dual force, where it is very bad for thinking if you use it as an answer machine, but can be great for learning if you use it as a tutor. AI can be tailored to you. However, it requires that you seek out the friction of having a tutor, relative to having an answer machine. And that requires motivation.

This is a very challenging moment, and the problem is one we need to take seriously.

Researchers improve weather forecasts looking two to six weeks out

Zach Winn | MIT News — Wed, 14 Oct 2026 11:00:00 -0400

In 2021, Texas suffered the worst energy infrastructure failure in state history, following three severe winter storms in February. At least 246 people were killed as a result of the disruptions, which led to food, water, heat and electricity shortages.

If energy officials had had more time to prepare for the storms, for example by bringing in more trucks of natural gas to disrupted areas, damage from the event could have been mitigated. Unfortunately, weather forecasts beyond two weeks are notoriously unreliable, making for a tighter window for energy operators, farmers, and emergency response officials around the globe.

A new technique by a team including researchers at MIT significantly improves weather forecasting beyond two weeks. The researchers developed a machine-learning model that learns to correct errors in existing forecasting models to improve their output over the two-to-six-week time frame. When applied to a gold-standard forecasting model, it doubled forecasting accuracy over that time frame, placing first in a prominent international forecasting competition.

In a new paper in Nature, the researchers describe their approach and show how it enables forecasts that for the first time beat climatologic averages between two and six weeks.

“Not only does this do better on time averaged forecasts for this range, which is already very useful for farmers and members of the energy industry trying to match supply with demand, but it also improves predictions of extreme weather events, whether it’s flooding or cold spells or something else,” co-author and MIT Research Scientist Judah Cohen says. “It’s really important to predict both, but extreme events might be the most impactful part of this for society.”

Joining Cohen on the paper are lead authors Hannah Guan, a Harvard College undergraduate; Soukayna Mouatadid, a University of Toronto postdoc; Genevieve Flaspohler SM ’18, PhD ’22; Paulo Orenstein, an associate professor at Brazil’s National Institute for Pure and Applied Mathematics; Jeremy Berman of the data company DTN; Jakob Schloer and Joshua Talib, researchers with the European Center for Medium-Range Weather Forecasts; and Alex Lu, Haiyu Dong, Jonathan Weyn, Zekun Ni, and Lester Mackey, all researchers at Microsoft.

Longer forecasts

In the 1970s, longtime MIT Professor Edward Lorenz came up with the idea of chaos theory to describe how tiny changes in initial weather conditions lead to completely different outcomes.

“He came up with this theoretical two-week limit where beyond that, we just can’t predict weather because the noise in the system grows so much that it overwhelms any signal you start with,” Cohen says. “That had a strong influence on the field that may have held people back from exploring forecasting beyond the two-week range. I never really believed that completely. In theory the atmosphere can create an infinite number of patterns, but it likes to fall into these favorite patterns over and over again. Once one thing happens, I think you can predict the domino that will fall after that, and the domino that will fall after that.”

Today, the best weather forecasts come from either dynamical models, which numerically simulate how the atmosphere and oceans evolve over time, or artificial intelligence models, which learn to make predictions from historical weather data. AI models have shown improvements in predicting weather at short ranges, but they’re even worse than dynamical models at longer time ranges.

Cohen, who is based in the MIT Department of Civil and Environmental Engineering, focuses on forecasting weather two to six weeks in advance, which is known as subseasonal forecasting. “That range is referred to as the ‘predictability desert,’ because in the short range you can capitalize on the initial conditions in the climate system and the atmosphere, and on the longer term you have these slow, varying boundary forces, like the super El Niño everyone’s focused on this winter,” Cohen says. “The subseasonal range can’t take advantage of either of those. In most cases forecasting in the subseasonal ranges is no different than going to a casino and rolling the dice.”

Cohen credits his co-author Lester Mackey, whom he has been collaborating with for more than a decade, with the initial idea of using machine learning to reduce errors in forecasting models.

“We built this wrapper — Lester calls it postprocessing,” Cohen says. “We know AI models have a lot of errors, so we thought maybe we can quantify those errors and remove them.”

While other forecasting systems do more simplistic error correction, the research team’s postprocessing layer uses AI to learn from a forecasting system’s mistakes based on historic data and shifts its forecasts. In that time range, forecasters use so-called probabilistic forecasts, where the likelihood of a range of possibilities if given.

“As more forecasts and data become available, our model adaptively improves its correction,” Cohen says.

To build the model, the researchers used MIT Engaging, a supercomputing cluster hosted by MIT’s Office of Research Computing and Data, but the resulting model does not require a supercomputer to run.

To put their model to the test, last year the research team submitted three models that included their post-processing layer to the European Center for Medium-Range Weather Forecasts (ECMWF), which is considered the best forecasting center in the world. Each season ECMWF holds forecasting competitions between models.

The submission that won first place combined ECMWF’s forecasting model with another, and then applied the postprocessing correction on top. That approach ended up doubling the original model’s predictive power in the subseasonal range. For the first time, the model offered an improvement over relying on climatologic averages.

“Up until now, no one had been able to show a model predicts weather better than long term averages for these subseasonal forecasts,” Cohen says. “They’ve pretty much all been worse than that — you could literally flip a coin and do better. People have questioned if it’s even worth investing in models at all for this time range. We consistently beat climatology averages with our models.”

The model also showed a statistically significant improvement in the ability to predict extreme weather.

“That’s one of the more important findings, because it’s so difficult to accomplish,” Cohen says. “When the ECMWF developed their own, non-AI, error-correction to their model, it got worse at predicting extreme weather. I think that’s because traditional error, or bias, correction is always trying to get to the mean, whereas extreme weather is on the fringe of probabilities.”

Preparing for disaster

In the Nature paper, the team showed their technique could be used to improve land forecasts in most regions across the globe. They also applied their bias correction technique to other models and showed it improved those as well, putting even lesser models on par with the ECMWF’s forecasts.

“What’s nice about our model is it’s an add-on wrapper,” Cohen explains. “We used it with the ECMWF’s dynamical model because that was considered the best, but it can be used with any model. It even improves models that are already bias corrected using another AI algorithm. So it shows a robust improvement. And it’s open source and low cost.”

Cohen says the meteorological office for the Republic of Kenya has already reached out about implementing the model to improve forecasting of the annual monsoon season to improve agricultural yields this winter. He hopes other groups will also use the model.

“This could help you decide when to fertilize around rain, when there’s a freeze coming to protect crops during winter harvests, or help you avoid droughts,” Cohen says. “It could also be useful for energy and emergency management. I’ve talked to energy officials who say, ‘If I’m told two days before that an ice storm’s coming, I don’t have enough time, but if I had two weeks, that could be enough to make adequate preparations.”

The work was supported, in part, by the U.S. National Science Foundation, the Carlos Chagas Filho Foundation for Research Support, and the European Union.

Reprogrammed NK immune cells target cancer and autoimmune disorders

Anne Trafton | MIT News — Tue, 13 Oct 2026 05:00:00 -0400

MIT engineers have developed a new way to program natural killer (NK) immune cells to target cancerous T cells or T cells that have become autoreactive, leading to diseases such as lymphoma and autoimmune hepatitis.

Using their new technique, the researchers can reprogram NK cells to target diseased T cells without ever removing the NK cells from the patient’s body. Instead, they use lipid nanoparticles to deliver messenger RNA directly to NK cells. These mRNA molecules encode proteins that allow the NK cells to seek and destroy the dysfunctional T cells.

Known as CAR NK cells, these cells could be deployed against a variety of diseases. The MIT team’s new approach could bypass one of the major obstacles to using these cells, which is the time and expense of manufacturing them outside the body.

“The manufacturing challenges with CAR NK cells are significant. The idea here is you can deliver a nanoparticle that will modify your own NK cells with a CAR construct and teach them to attack tumor cells,” says Daniel Anderson, a professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science.

Anderson is the senior author of the study, which appears today in Nature Biotechnology. The paper’s lead authors are MIT postdoc Yuang Chen and MIT graduate students Julia Monkovic and Haseeb Mughal. Robert Langer, the David H. Koch Institute Professor at MIT, is also an author.

Easier reprogramming

Over the past decade, the U.S. Food and Drug Administration has approved seven new cancer treatments based on engineered T cells called chimeric antigen receptor (CAR) T cells. Chimeric antigen receptors are proteins that can be embedded into T cells, allowing them to detect a target antigen, such as a cancer protein. When the target is detected, the T cell becomes activated and destroys the target cell.

This approach has had great success in treating blood cancers, primarily those that originate from B cells, including several types of leukemia, lymphoma, and multiple myeloma.

To make CAR T cells, doctors take a blood sample from the patient. The sample is sent to a lab where the T cells are isolated and then engineered to express the CAR protein. When enough CAR T cells have been generated, they are shipped back to the hospital and infused into the patient.

In recent years, scientists have begun using a similar strategy to create CAR NK cells. One potential advantage of CAR NK cells over CAR T cells is that they could be used to kill diseased T cells. Using CAR T cells to treat T cell diseases often doesn’t work well because T cells that are engineered to target T cell proteins may end up killing each other, in addition to the diseased T cells. That issue doesn’t arise when using NK cells to target T cells.

“NK cells can treat these T cell malignancies — not only T cell cancer, but also T cell autoimmune diseases,” Chen says.

However, manufacturing large quantities of CAR NK cells poses many of the same difficulties as generating CAR T cells. To simplify the process, the MIT team wanted to come up with a way to generate CAR NK cells “in vivo” — that is, in the patient’s own body, without having to remove any cells.

“If a patient can just get an infusion of nanoparticles, it would dramatically simplify the process and reduce costs,” Anderson says.

Currently, there are no FDA-approved CAR NK cell therapies, but many researchers are working on developing them to treat cancer or autoimmune disorders.

To generate these cells, the MIT team designed lipid nanoparticles (LNPs) that can deliver an mRNA payload. These strands of mRNA encode a CAR that can be targeted to specific cell types. For example, they could deliver a CAR that targets CD19, a protein found on B cells, or CD3, a protein found on autoreactive T cells.

The particles are coated with antibody fragments that bind to receptors found on the surface of NK cells, to ensure they reach the correct targets.

Targeting dysfunctional T cells

After these particles were infused into mice, the researchers found that they could successfully reprogram about 80 percent of the targeted NK cells. The researchers then tested the particles’ potential to treat cancer and autoimmune hepatitis, which is caused by autoreactive T cells.

In a mouse model of an aggressive form of melanoma, three of the five mice that received LNPs that reprogrammed their NK cells to target the cancer cells went into complete remission.

The researchers also saw significant tumor destruction when they used the LNPs to treat T cell lymphoma in a humanized mouse model, created when mice receive transfusions of human blood cells, allowing them to transiently generate a human-like immune system.

In these mice, tumors were eradicated after five injections of the reprogramming LNPs. This result was similar to what the researchers saw when they treated mice with CAR NK cells that were generated using the conventional approach — removing NK cells, reprogramming them, and returning them to the mice.

Lastly, the researchers demonstrated that their LNPs could generate CAR NK cells that target autoreactive T cells. For this application, they studied mice with autoimmune hepatitis, in which T cells attack hepatocytes in the liver. They found that in the treated mice, the autoreactive T cells were destroyed, and liver damage healed. These effects were seen after just one injection of the LNPs.

“This study is an excellent example of the power of nano delivery systems and their ability to potentially treat cancer and other diseases,” Langer says.

The researchers hope that these kinds of engineered NK cells could be used to treat a variety of autoimmune disorders caused by dysfunctional T cells, including multiple sclerosis and rheumatoid arthritis.

The research was funded by Sanofi, the Marble Center for Cancer Nanomedicine, and a Koch Institute Support (core) Grant from the National Cancer Institute.

Q&A: Building accountability behind our words — and AI’s — in civic conversations

Peter Dizikes | MIT News — Tue, 13 Oct 2026 00:00:00 -0400

When government officials craft policies, there is often a comment period for public feedback. Lawmakers have long had to sort through fake feedback that is mass-produced in an effort to sway policy, but they’ve generally gotten used to detecting it. With, artificial intelligence, however, people can submit realistic-seeming comments, which are harder to dismiss as being fabrications with no person behind them. That’s just one way AI can be used to generate text and statements, in civic conversations, for which no one can be found to be answerable.

This issue, among others, has caught the attention of Deb Roy, professor of media arts and sciences at MIT and director of the MIT Center for Constructive Communication (CCC). In a new essay published in the Bulletin of the Atomic Scientists, titled, “Who stands behind the words?,” Roy calls for a renewed emphasis on responsibility in communication, asserting that even when AI is used to craft text, there needs to be accountability and “answerability.”

Building better group dialogues to produce more public trust is a core part of the work of CCC. The group, along with Cortico, the closely affiliated nonprofit Roy co-founded, has helped facilitate group discussions on important issues in many situations — including civic meetings, schools, deliberative assemblies, and private organizations — and used tech tools, including AI, to help find themes among comments, and more. But the center also designs this process so that participants can check and override the tech-based output at each key step. Ultimately, a person is responsible for every public comment. MIT News spoke to Roy about the essay and the work of the center.

Q: You’ve identified this problem about the lack of accountability behind words that are generated by AI. When did you really first notice this?

A: It came into focus when Chat-GPT [which is powered by a large language model, or LLM] launched. As for my own history with language models, my PhD work explored what we might now call PLMs — Puny Language Models — so I’m quite familiar with language models and have tracked the field’s rapid evolution. But Chat-GPT turned a corner. Two things changed at the same time: the depth of the model and its knowledge, and the way you could interact with it.

I became obsessed with trying to find the limits of what it could do. And I felt that no matter how hard I tried to stretch my arms around it, I couldn’t. I built my tenure case at MIT on language grounding — the idea that it’s not enough for an AI system to just process lots of words and other symbols. I believed that to understand what words mean, you actually need a physical body and life experiences as part of the process. So I revisited the philosophy-of-AI literature I had once been steeped in, which asks what fundamentally distinguishes the kind of system that large language models exemplify, in extraordinarily advanced form, from the kind of processing we humans engage in. I gave several presentations to my own graduate students trying to make the case that, even though AI seems so intelligent, it’s actually different in kind — and I could not convince them.

And I thought, I know the difference, but I’m having trouble convincing even myself of what it is. And that led me to ask, “Wait a minute: Is there someone or something that stands behind the words, a human responsible for what is being said?” That’s the thing this extraordinary new language machine doesn’t have.

Whether these systems genuinely understand language is actively debated. But even if you grant them all the comprehension you like, they still lack standing. The buck can’t stop at a thing, a software system, with nothing to lose.

Q: The Bulletin piece has these interesting examples of civic feedback forums where it looks like there are real people saying things, and instead you have a barrage of comments from AI. How did you find some of these other examples in society where things go off the rails in this way?

A: After the release of ChatGPT, I started hearing a concern from people inside government who review public comments on proposed rules: a suspicion that not all of the comments were written by humans.

My reaction was: That’s going to delegitimize this channel for public feedback. That was several years ago, but everyone now knows this is happening. Public comment is a pillar of democracy. You don’t just put a new rule in place. People need to have a voice and feel like they’ve been heard before a rule is imposed on them. If you skip that step, people don’t accept rules as legitimate. That’s why that process is baked into how America works. And that pillar just cracked, and by now, in my mind, it’s fallen over. Has anyone noticed? My sense is the people on the receiving end of these comments have quietly adjusted the weight they give to public comments. It was never perfect, but boy, now much of its value is being swept away.

I open my essay with this example: In 2017, the Federal Communications Commission received more than 22 million public comments on its proposal to repeal net neutrality rules, and the New York attorney general later found that nearly 18 million of them were fake. As I write in the essay, “That fraud was caught because it left fingerprints: templated text, duplicated phrasing.” But large language models erase the fingerprints. Now every comment can be fluent and different, and reviewers can no longer tell which ones express views that real people formed and stand behind.

If you have enough of the stuff that’s fake — words that no one with a stake actually stands behind — it desensitizes you to what’s real. That filters back to the public, and people realize: If I take the time to express myself, they’re not even listening any more.

Q: Could you say a little bit more about your point that we need people with “standing,” who will take “accountability” for all pieces of text, even when those bits of text are generated by AI?

A: In the legal system, standing is a formal idea: sometimes a case isn’t heard because the people bringing it don’t have standing. But informally we use that term all the time. I’m not trying to formalize standing in everyday life. Instead I’m asking: What is the informal sense of the term, borrowed from the legal one, that we need to pay more attention to now? Here in New England, consider the open town meeting: The person who stands up to speak is generally expected to have standing, as someone from that town with a stake in what gets decided. And if it’s a stranger who drove from across the state, or from out of state, everyone’s like: Wait a minute, who is that? They’re not from around here. In general, there’s just enough friction and it’s costly enough to show up somewhere you don’t actually have standing, that it doesn’t happen much.

Now LLMs and the current digital networked environment collapse those boundaries. We always took for granted that if someone has a voice, it’s because they have standing. They have some basis to be heard. We need to pay attention to this, and really consider how to design systems so that the people with standing are the ones who get heard.

The other idea I tried to develop is “answerability.” It isn’t formalized in law, but it’s baked into the legal process. If you have standing to give testimony, there you are, ready for cross-examination. You have to be able to answer for what you’re saying. And if you’re making a specific accusation about someone, you have to be willing to back it with facts. Standing and answerability have to travel together: The right to be heard comes with the obligation to answer for what you say.

That doesn’t mean everyone has to attach their name to everything. I write in the Bulletin essay, “Privacy and answerability aren’t opposites. … An institution can verify a person and stand behind their words without exposing them.” To voice a view, you only need to show that a distinct, eligible person is speaking. An accusation asks more: Someone trusted, like a reporter, has to be able to know who is speaking. As I say: “Disclosure obligations should scale with power, and identity protections with vulnerability.”

None of this means AI can’t help. AI can do a lot of the work inside the chain of accountability — drafting, summarizing, finding patterns. If someone with a stake in a rule uses AI to draft a comment, adopts it as their own, and is ready to answer for it, that’s legitimate, in this context. What AI can’t do is anchor the chain. There has to be a person at the end who stands behind the words.

In the essay I lay out four design principles for building that in: “attributable authorship,” a “decomposed and inspectable process,” the use of “provenance as infrastructure,” and the need to “widen standing rather than concentrating it.” As I write, the “animating rule is that autonomy is permitted while unaccountability is not.” And the metric that matters most isn’t the efficiency gained by removing humans, but the number of people AI places into roles where they can answer for what they do.

Q: There is a phrase in the documents of the Center for Constructive Communication, that we need things to move “at the speed of trust,” not at the “speed of tech.” And that implies really thinking about building up these systems. Could you just say a little more about what that phrase means to you?

A: I learned that phrase when I was in Appleton, Wisconsin, with Katherine Cramer [of the University of Wisconsin], a longtime collaborator, talking with community organizers who were running “Dialogue across Divides” conversations there. They had hundreds of people involved. I thought I was developing technology tools with exactly that same purpose, and was so excited to see it. And I asked one of the people who ran that organization, “How fast did you scale this?” And he paused, and then he said, “At the speed of trust.”

And I took that to mean “slow.” Then I realized he was saying: For you, slow, because you haven’t yet earned our trust. And I’ve since learned that when you do have trust, things can move fast. So bringing in technology is really a process of engagement. It doesn’t work to say, “Trust us, we’re doing this because we think it’s for your own good.” People will turn to someone they already trust and ask, “Is this good for us?” And if that person says “Yes,” things can move fast.

Look at what’s happening at the intersection of AI and society: The speed of AI and the speed of democracy are running on different clocks. Talk to people who work in democratic practice and they say, “AI must slow down, because democracy can’t move that fast.” Talk to people in AI and they say, “AI cannot slow down, democracy needs to speed up.” If we can’t get these two time scales in sync, we’re headed for a breakway situation, where AI gets deployed faster than democratic processes can respond, and decisions get made without the public. That’s starting to happen right now.

Q: We need people actively doing things to build dialogue and figure out how tech tools can interact with that. At the Center for Constructive Communication, you’ve done a few iterations of the center’s Real Talk initiatives to build civic dialogues, as well as the Youth Voices Initiative in school settings, and currently you’re working on the New Social Forms project. Could you say a few words about them?

A: The Real Talk effort represents the longest-standing thread in this work. A huge emphasis of our work has been developing better ways to listen to one another. We create a context that is always small — small is beautiful here — where people are willing to share authentically. And we have the consent architecture and the technology to reveal the larger patterns, and then share them. We’ve done this with hundreds of communities around the country. And closer to home, we’ve brought the same approach to our campus here at MIT through the realtalk@MIT program, with support from the Office of the President and the Office of the Provost, to help our community build a culture around dialogue and listening.

The second thing I’d emphasize is that we’ve been doing more and more work on deliberative processes and citizen assemblies, and on how to make them more accessible. What if a group of high schoolers says, “We have a real problem with our cellphone policy?” Suppose you want to have an assembly that brings students, teachers, and administrators together, not just to listen to people’s concerns but to make policy recommendations. So we did that. The center worked with a high school in New York City, where technology helped people reveal their preferences and differences and listen to each other.

We’re also building answerability into the technology. A prototype called VoiceTrace, developed by PhD student Cassandra Overney in our group, links every output statement — a summary of what was heard in a listening process, or a recommendation coming out of a deliberation — to the specific voices behind it, so any claim can be traced back to the conversations it came from.

The New Social Forms project pertains to a very interesting pattern emerging in workplaces around the world, certainly the ones we’re connected to. AI is an extraordinary tool for individual empowerment, but in the words of one of our collaborators at the Google Experience Institute, people are tunneling. They go deep, using AI to develop refined ideas, but when they surface, their ideas have diverged from their teammates’. So groups and teams trying to work together with AI, there’s a human coordination problem.

We’re going to expand the initial work we’ve started with Google because there’s a lot of interest in collective communication, trust, and interaction among people trying to work together.

So is that a civic context? It’s a workplace, but workplaces are civic spaces, too. They govern people’s daily lives, they’re where many of us build civic skills and learn to cooperate across difference, and there’s a long tradition of civic life rooted in shared work. We’re all humans trying to interact with each other. These are the same issues. Justice Louis Brandeis once used the phrase “industrial democracy” to capture this. When workplaces lacked certain kinds of civic processes and civility — two interestingly related terms — labor disputes used to turn violent. Procedural justice, mediators, and people hearing one another are just as necessary in the workplace. In the end, it’s all about human beings. Hearing the humanity in one another is what democracy depends on, and it’s just as necessary for communities and organizations to function.

Exploring how plants thrive in a changing environment

Zach Winn | MIT News — Tue, 13 Oct 2026 00:00:00 -0400

The Sierra Nevada mountain range in California is as stunning as it is diverse, with storms from the Pacific Ocean dumping rain and snow on the range’s western slopes while arid conditions mark much of its eastern face. David Des Marais, MIT’s Amgen Career Development Associate Professor, knows the mountains well. His childhood in San Francisco was filled with hiking and camping excursions throughout the range.

At some point during those trips, he started wondering about one of the few constants of the landscape: the presence of plants. How were plants able to thrive in all those conditions? He turned the question into a career.

Des Marais, who directs the Parsons Laboratory in the Department of Civil and Environmental Engineering, studies how plants interact with their environment. The research spans molecules and mountain ranges, exploring everything from how individual genes change a plant’s ability to grow to how climate change will impact populations of red oak trees across the eastern U.S. His work also considers how evolutionary forces act on plants to change populations over time.

Plants are central to his lab, but Des Marais sees them as a window into all living things. He’s even quick to credit the health of his houseplants and garden to his wife, an evolutionary biologist at Harvard University.

“Understanding why life is so diverse is fascinating to me,” Des Marais says. “Understanding how different organisms, from bacteria to dinosaurs, have figured out how to interact with their challenging environment and optimize their fitness is inherently interesting. Why plants in particular? As a geneticist and a physiologist, plants are compelling experimental systems. If I have a theory about gene X causing function Y, I can go into the lab and delete gene X from this species and put it into that species and test function directly. It’s a powerful approach.”

Des Marais does not spend all his days in a lab. In addition to directing the Parsons Laboratory, he teaches four classes at MIT and has two sons who are active in baseball and theater. He welcomes these responsibilities.

At MIT especially, he embraces service.

“The only reason MIT works is because there are people willing to sacrifice some of their research to keep the place running,” Des Marais says. “One of the things I like about academia is that there’s sort of an institutional memory, and you try to honor the legacy of where you’ve been while you shape the future.”

From California to MIT

Des Marais grew up near Stanford University as well as near facilities for NASA and the U.S. Geological Survey, which meant there were always plenty of scientists and engineers around. His mother was a teacher, and his father was a geochemist with a knack for pointing out that the landscape around them was the result of geological processes dating back millions of years — a habit Des Marais admits he didn’t like at the time but that, in hindsight, influenced his thinking.

“I’m pretty sure that got into my brain, especially the idea that what looks static often comes about because of processes, and we can study those processes,” he says.

Des Marais studied integrative biology as an undergraduate at the University of California at Berkeley. He completed his PhD at Duke University around plant genetics.

“There’s a whole series of outdoor events, from the Boy Scouts through college, that made me start to think about the challenges that plants face living in these stressful environments,” he says. “That drove me to plant science and environmental science.”

In a long postdoc at the University of Texas at Austin, Des Marais learned to conduct experiments teasing out the effect of specific genes as he studied how they changed a model plant’s relationship with water.

Des Marais came to Boston primarily for personal reasons: He and his wife, who was also a postdoc at UT Austin, entered the job market at the same time. After some awkward moments competing for the same jobs, his wife landed a tenure-track position at Harvard University. Shortly after, he joined Harvard as a staff scientist.

In 2017, Des Marais discovered MIT’s Department of Civil and Environmental Engineering was hiring a plant ecologist.

“I don’t even think of myself as a plant ecologist, but I applied on a whim and they were interested,” Des Marais says. “I do a lot around plant water relations, and I joke that there are all these hydrologists in the department who study how water moves through the soil and into the atmosphere through these magical pipes. The magical pipes are plants, which play an important role in the water cycle. I realized a ton of people at MIT were studying that phenomenon at regional or global scales, but no one really studied how or why plants move water in the first place.”

Today Des Marais’ lab work can be broken into two broad categories. The first is centered around how plants function, including their relationship with environmental factors like water, temperature, and nutrients. Des Marais says some of his most impactful studies link specific molecules to how a plant functions in a given ecosystem.

The second centers around fundamental questions in plant evolution, what Des Marais describes as how plants “make decisions” about when to grow, reproduce, germinate, and more.

One current research project looks at how climate change will impact red oak trees, an important species and carbon sink. He is also examining climate change’s impact on plants that are important for food systems.

“We know that climate change is impacting plant function, and it’s impacting plant nutrition, and that impacts human nutrition,” Des Marais says. “Climate change is going to affect where we can grow which plants efficiently and how we can get them where they need to be eaten. All that stuff is critically important for humans.”

Understanding plants for the planet

Des Marais says it’s been fascinating working with experts in environmental chemistry and fluid mechanics as part of the Parsons Laboratory. He sees his role as director as being primarily about fostering community.

“The students are great,” Des Marais says. “It’s a funky old building, but we all work well together. We try to make sure everyone feels comfortable and welcome in the space.”

Even if he can’t take credit for his houseplants, Des Marais appreciates the vital role plants play for the planet.

“Plants are the foundation of the food chain, and there’s no way to get around that,” Des Marais says. “The efficiency of plant photosynthesis directly impacts our nutrition, and it impacts what we can feed the animals that we eat. It affects carbon cycling and water cycling — put simply, plant biology matters.”

Economists Robert Hall PhD ’67 and Michael Woodford PhD ’83 share Nobel Prize

Peter Dizikes | MIT News — Mon, 12 Oct 2026 08:00:00 -0400

Robert E. Hall PhD ’67 and Michael Woodford PhD ’83, scholars who both earned their doctorates at MIT, have been awarded the 2026 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel.

Hall, a professor at Stanford University, and Woodford, a professor at Columbia University, were recognized for work that helped revitalize ideas about how government policy can spur growth to help end economic downturns. The Nobel citation describes their research as contributing to “new macroeconomic models that have significantly improved our ability to implement effective stabilisation policy.”

Both scholars helped integrate microeconomic analysis into macroeconomic models. In Hall’s case, the Nobel committee particularly cited research he conducted in the late 1970s shedding new light on economic expectations and household consumption. The committee also spotlighted Woodford’s construction of a comprehensive and influential macroeconomic model that was built on microeconomic foundations and introduced in his 2003 book, “Interest and Prices: Foundations of a Theory of Monetary Policy.”

The work of both scholars helped update models for intervention during economic crises, based on the ideas of John Maynard Keynes, which had worked many times in the 20th century but encountered newfound difficulties adapting to the conditions of the 1970s. So-called New Keynesian models used for economic stabilization today are a result of both economists’ research, the citation noted.

Hall is the Robert and Carole McNeil Joint Senior Fellow at the Hoover Institution and Professor in the Department of Economics at Stanford University. He received his BA in economics from the University of California at Berkeley before earning his PhD from MIT. He also served as an MIT faculty member in the 1970s.

Woodford is the the John Bates Clark Professor of Political Economy at Columbia University. He received a BA from the University of Chicago and a JD from Yale University Law School prior to receiving his PhD from MIT.

Remarkably, Hall becomes the fifth former doctoral student principally advised by MIT economist Robert M. Solow to earn the Nobel Prize in economics. The others are George Akerlof PhD ’66, Peter Diamond PhD ’63, William Nordhaus PhD ’67, and Joseph Stiglitz PhD ’67, all of whom had Solow as their principal advisor in MIT’s graduate program. Additionally, MIT professor of biology H. Robert Horvitz ’68, a Nobel laureate in 2002 in the category of physiology or medicine, wrote his undergraduate thesis in economics under the direction of Solow. In 1987, Solow received the Nobel Prize himself.

Woodford did his doctoral work under the primary supervision of Timothy Kehoe, now of the University of Minnesota — although Woodford does thank Solow in the first paragraph of his doctoral thesis, for helping him “distinguish important problems from exercises of merely technical interest.”

There are now 17 MIT alumni who have won the Nobel Prize in economics. Previously, 10 people have won the award while serving on the MIT faculty, and 10 former faculty have also been named recipients.

3 Questions: What is the best path forward for AI in academia?

Stefanie Koperniak | Institute for Data, Systems, and Society — Thu, 08 Oct 2026 17:25:00 -0400

With artificial intelligence capabilities continuing to expand, universities now face complex, urgent questions about how to navigate increasing challenges, as well as new opportunities. Much attention is now directed toward this, including an MIT report on AI and education published this summer.

A new essay from MIT Statistics and Data Science Center Director Sasha Rakhlin, the Distinguished Professor in Data, Systems, and Society, IDSS and Brain and Cognitive Sciences, synthesizes recent discussions and readings about how AI is changing academia — particularly mathematics, statistics, machine learning, and engineering. Here, focusing primarily on graduate research and education, he describes key questions that departments and universities need to think about, and how they can best work with AI going forward.

Q: What is changing in research, and why is it happening so quickly?

A: Mathematics illustrates how quickly AI capabilities are advancing. Last year, a model reached gold-medal level at the International Mathematical Olympiad. Only a year later, models are producing new research results, including a recently proposed solution to one of the Millennium Prize Problems. Tasks that once demonstrated advanced mathematical ability can increasingly be performed by AI.

A central factor determining the pace of AI progress in a particular discipline is the speed and reliability of verification. Formalized proofs can be checked automatically; programs can be run and tested. Where evaluation is fast and reliable, systems can generate candidates, learn from outcomes, and improve. This also applies to AI research itself: improving models, training procedures, and their supporting tools. Better models can contribute to the next round of development, creating a compounding process that accelerates progress.

For experienced researchers, this creates opportunities to pursue questions whose technical demands previously put them out of reach. It also sharpens the distinction between obtaining a solution and understanding why it works, what generalizes, and what to ask next. However, we should not assume that abstraction, judgment, or problem formulation will remain beyond AI. Universities should prepare for a near future in which AI is smarter than all of us in many, perhaps most, aspects of our intellectual work.

Q: How should departments rethink academic credit and graduate training?

A: In a growing number of fields, a polished paper is becoming a weaker signal of individual expertise. Departments must begin now to reconsider what they reward, without simply defining valuable work as whatever AI cannot yet do. Asking good questions, replication, synthesis of ideas, informative negative results, and shared datasets may deserve greater recognition. More broadly, evaluation should establish what a researcher contributed and takes intellectual responsibility for, including when substantial parts of the work were performed by AI. These expectations should guide hiring, promotion, and funding — and be made explicit for current and incoming PhD students.

Training poses a harder problem. Intellectual muscles develop through exercise. Routine calculations, coding, failed approaches, and small discoveries have long helped students acquire intuition and judgment. Delegating this work can remove formative experiences, even as AI allows students to attempt more ambitious projects. We need to distinguish unnecessary friction from work through which expertise develops. Students should learn to formulate problems, audit model outputs, reproduce results, and defend their choices. Fundamentals may become more valuable as the basis for using these tools well.

Q: What should MIT and other universities build now?

A: This is where I see a real opportunity for universities. Let's first be clear about the goal: Universities should be able to pursue questions over long horizons, share results openly, and evaluate claims independently. Partnerships with industry will be essential, but we should not assume that commercial priorities will cover the breadth of science or remain aligned with it over time. Some degree of technological independence will therefore be necessary.

Academia's most important strategic asset may be the accumulated knowledge and experience within its laboratories. Published papers present a selective record: Failed experiments, abandoned directions, and the reasons an approach did not work often remain unpublished. Scientists and engineers hold tacit knowledge, acquired through experience, about which interventions are likely to fail and why. This missing context may help explain why models currently struggle in some domains, especially the empirical sciences, to anticipate consequences that are clear to an expert. These limitations may be temporary, and capturing negative results and scientists' interpretations could make models much better at scientific exploration.

We can imagine a future in which MIT's laboratories function more like a single, living, scientific organism, connected through shared AI research infrastructure. For example, suppose a neuroscience laboratory needs better methods for segmenting neurons in microscopy images. An AI agent could recognize a relevant advance from a computer vision group, connect the researchers, propose benchmarks, and help them iterate. It could surface unresolved questions to students and faculty, and make lessons from one laboratory available to others. To make this possible, we should build workflows that capture hypotheses, interventions, outcomes, failures, and interpretations. Shared systems should connect these workflows, allowing agents to use tools and information across laboratories with appropriate permissions. This will require substantial public and institutional investment now in compute, secure data systems, and expertise in adapting and post-training AI models.

Tracing this process could also preserve the lineage of ideas and make contributions, including those of graduate students, easier to recognize. With agreed rules for consent and credit, researchers could collaborate more openly, with greater confidence that their work would be acknowledged. This could also help answer the earlier question of how to recognize and reward intellectual contributions.

AI systems can already synthesize and reason about information from more sources than any individual researcher could absorb, and their ability to make useful connections will continue to improve. We should use this capacity to bring us together around hard scientific and engineering problems, helping us build on one another's strengths and insights. To me, this is a promising path forward, but universities need to invest now to make it possible.