MIT News

Beacon Biosignals is mapping the brain during sleep

Zach Winn | MIT News — Fri, 01 May 2026 00:00:00 -0400

The human brain remains one of the most fascinating and perplexing mysteries in medicine. Scientists still struggle to match neurological activity with brain function and detect problems early, slowing efforts to treat neurological disorders and other diseases.

Beacon Biosignals is working to make sense of the brain by monitoring its activity while people sleep. The company, which was founded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, developed a lightweight headband that uses electroencephalogram (EEG) technology to measure brain activity while people enjoy their normal sleep routines at home. Those data are processed by machine-learning algorithms to monitor the effects of novel treatments, find new signs of disease progression, and create patient cohorts for clinical trials.

“There’s a step-change in what becomes possible when you remove the sleep lab and bring clinical-grade EEG into the home,” says Donoghue, who serves as Beacon’s CEO. “It turns sleep from a constrained, facility-based test into a scalable source of high-quality data for diagnostics, drug development, and longitudinal brain health.”

Beacon partners with pharmaceutical companies to accelerate its path to patients. The company’s FDA 510(k)-cleared medical device has already been used in over 40 clinical trials across the globe as part of studies aimed at treating conditions including major depressive disorder, schizophrenia, narcolepsy, idiopathic hypersomnia, Alzheimer’s disease, and Parkinson’s disease.

With each deployment, Beacon learns more about how the brain works — insights it is using to create a “foundation model” of the brain.

“It’s our belief that the dataset that’s going to transform brain health doesn’t exist yet — but we are rapidly creating it,” Donoghue says. “Our platform can characterize the heterogeneity of disease progression, generating dynamic insights that are impossible to fully capture through static modalities like sequencing or imaging. The brain is an electric organ and changes through synaptic plasticity, so tracking brain function across many diseases at scale will allow us to discover novel subgroups of diseases and map them over time.”

Illuminating the brain

Donoghue trained in the Harvard-MIT Program in Health Sciences and Technology, conducting clinical training for an MD while completing his PhD in neuroscience at MIT under the guidance of Earl Miller, MIT's Picower Professor in Brain and Cognitive Sciences and The Picower Institute for Learning and Memory. While in the program, Donoghue trained at Massachusetts General Hospital and Boston Children’s Hospital, where he helped care for patients, including in oncology, during the rise of genomic sequencing to guide precision cancer therapies. He later worked in neurology and psychiatry, where care often relied on more iterative approaches — highlighting an opportunity to bring similarly data-driven precision to brain health.

“What struck me most was the inability to measure brain function in the ways that cardiologists can longitudinally monitor cardiac function in patients from home,” Donoghue says. “At MIT, I built this conviction that processing a lot of brain data and working to correlate that with brain function would be transformative to how these neurological diseases are identified and treated.”

Toward the end of his training, Donoghue began developing his ideas further, engaging with mentors including HST and Harvard Medical School professors Sydney Cash and Brandon Westover. He had met Revels, who was working as a research software engineer in MIT’s Julia Lab, during his PhD, and convinced him to co-found Beacon with him in 2019.

“We decided building a business to understand the organ of interest — the brain — would be a great start to understanding heterogeneous neuropsychiatric diseases and building better treatments,” Donoghue recalls.

Beacon began as a computation and analytics company building wearable devices to expand clinical impact and reach. From its early days, Beacon has been partnering with large pharmaceutical companies running clinical trials, offering a less invasive way to watch brain activity and learn how their drugs are impacting the brain as well as how patients sleep.

“It was clear sleep was the right window to understand the brain,” Donoghue says. “Neural activity during sleep can be an order of magnitude higher and more structured, almost like a language. It’s a great surface area for understanding brain function and how different drugs affect the brain.”

Donoghue says Beacon’s devices can collect lab-grade data on each patient for multiple sequential nights, resulting in higher quality assessment. The company uses machine learning to extract insights, such as the time patients spend in different sleep stages and the number of small awakenings that occur throughout the night. It can also detect subtle sleep architecture changes that might lead to cognitive decline.

“We’re starting to take features of sleep activity and link them to outcomes in a way that’s never been done with this level of precision,” Donoghue says.

To date, Beacon has taken part in clinical trials for sleep and psychiatric disorders as well as neurodegenerative diseases, where sleep changes can emerge years before the presentation of symptoms.

“We do a lot of work in areas like Alzheimer’s disease and Parkinson’s, which affected my grandfather,” Donoghue says. “We’re analyzing features of rapid-eye-movement and slow-wave sleep to detect early changes that precede clinical symptoms. It’s an opportunity to move these diseases from late recognition to much earlier, data-driven detection.”

Improving brain treatments for millions

Last year, Beacon acquired an at-home sleep apnea testing company that serves more than 100,000 patients each year across the U.S., accelerating access to high-quality, comprehensive testing in the home and expanding the reach of its platform. Then in November, the company raised $97 million to accelerate that expansion.

“The vision has always been to reach patients and help people at scale,” Donoghue says. “What’s powerful is that we’re building a longitudinal record of brain function over time,” Donoghue says. “A patient might come in for sleep apnea screening, but if they develop Parkinson’s years later, that earlier data becomes a window into the disease before symptoms emerged. That turns routine testing into a foundation for entirely new prognostic biomarkers — and a path to detecting and intervening in brain disease earlier, potentially before symptoms ever begin.”

Unlocking mysteries of the universe through math

Lyn Nanticha Ocharoenchai | School of Science — Thu, 30 Apr 2026 16:30:00 -0400

GPS navigation, cryptography, quantum computing — while some of humankind’s greatest advancements have been invented by pioneers from various cultures, they were founded upon one common grammar: mathematics.

“Mathematics is the language with which God wrote the universe,” said the famous Italian astronomer, physicist, and philosopher Galileo Galilei, who, among his various scientific contributions, helped provide evidence for the idea that the sun is at the center of the solar system.

Although mostly conveyed through combinations of numbers, letters, and signs that may seem enigmatic to many, math equations hold within them countless stories — playbooks that generations of wonderers and inventors have crafted, refined, and shared in an attempt to make sense of a world full of unknown variables.

“I have faith in mathematics that, when there seems to be something special happening, when there’s some coincidence, that it’s not just a coincidence,” says mathematician Amanda Burcroff, “but that there’s actually some really deep, interesting, and involved reason for why that should be true.”

Burcroff’s research is focused on algebraic combinatorics, an area that provides discrete frameworks for understanding algebraic and geometric spaces that ubiquitously arise across science. This year, she joins MIT’s Department of Mathematics as a postdoc as part of the School of Science Dean’s Fellowship. Working with Professor Alexander Postnikov, Burcroff is building upon her techniques with the goal of applying them to other areas such as theoretical physics — a field that seeks to uncover the fundamental laws governing everything from subatomic particles to the cosmos itself.

“I have trust that if you keep following the path, eventually you’ll find the treasure — that is, whatever theorem or proof — that you’re looking for,” she says.

Exploring possibilities and redefining rules

Like many children, Burcroff once saw math as a subject that entailed lots of memorizing. Although she felt that it came naturally to her, she didn’t always find math very interesting.

In high school, as she came to learn about areas like calculus and geometry, Burcroff started to see the discipline in a different light — a creative approach to exploring what’s possible.

“[In] most other fields, the rules are imposed on you by the world,” she says, “but in math, you get full freedom to lay down those rules and then figure out what the implications of those rules are by using logical consequence.”

In 2015, Burcroff began her bachelor’s degree at the University of Michigan with a major in math and a minor in computer science. There, she entered the world of combinatorics — a branch of math dealing with counting, arranging, and combining objects that forms a crucial basis for understanding the complexity of problems, as well as the limits of computer algorithms.

“When I was starting out, I was just happy to have any mystery that anyone gave me,” she says.

Math was, to Burcroff, like a fun game with levels to complete. But during a study abroad program in Budapest, Hungary — the hometown of Paul Erdős, who is considered to be one of the most prolific mathematicians of the 20th century — it became more exciting to play when she was handed puzzles no one has yet solved.

“It turns out that if you put down the right set of rules, there’s an infinite number of beautiful things that you can do with it,” she says.

A journey of endless mysteries to unlock

In 2019, Burcroff embarked on a journey to pursue further research in England, later completing a master’s degree in pure mathematics at the University of Cambridge, then a research master’s degree at Durham University. In 2021, she returned to the United States and began her PhD at Harvard University, with the guidance of Professor Lauren Williams.

Among several riddles she has unraveled over the years, Burcroff helped unify different mathematical approaches to understand why systems work so reliably. Think of it as finding out that two seemingly different set of instructions actually lead the same way. By demonstrating their connections, her work has revealed an underlying, overarching mathematical architecture — a finding that later helped Burcroff and her collaborators tackle one of the many enduring riddles in her field.

Generalized cluster algebras form the basis for describing geometries that appear throughout physics. For more than a decade, mathematicians suspected these building blocks were created only by adding up ingredients and never subtracting, although no one was able to prove it. In 2024, Burcroff and her collaborators published a paper demonstrating that these spaces have nice positivity properties by developing a new way to count and organize patterns — helping untangle a long-standing conjecture, whose potential implications span from predicting particle collision outcomes to describing the spaces appearing in string theory.

These findings have earned Burcroff numerous prestigious awards including a National Science Foundation Graduate Research Fellowship, a British Marshall Scholarship, and a Jack Kent Cooke Graduate Fellowship.

Despite the tremendous number of problems she has answered, new ones keep arising.

“Every time you unlock one of them, it gives you a bunch of paths to new connected mysteries,” Burcroff says.

At MIT, she is working with Postnikov, whose research on combinatorics and positivity-type problems has presented a radically different way to calculate fundamental quantities in quantum field theory.

“Burcroff is conducting research across disciplinary boundaries,” says Postnikov.

He adds: “I am sure that she will have a lot of fruitful interactions with researchers in other MIT departments.”

Burcroff’s goal is to apply combinatorial techniques to broader physical contexts and direct applications, especially those with implications to topics like mirror symmetry, a principle in string theory suggesting that very different-looking geometric spaces can be mathematically equivalent.

While “doing math is 99 percent trying something and failing,” Burcroff says it is this same challenge that keeps her motivated. To her, it is not about reaching a destination, but rather about the continuous “process of discovery,” one she hopes to share beyond the typical classroom.

To make math more accessible, especially among underrepresented groups, Burcroff has worked with mentorship programs including Harvard’s Real Representations and Math Includes, Cambridge Girls’ Angle, and MIT PRIMES. During her time as a postdoc, she hopes to continue this outreach and explore ways to get involved with other support groups at MIT’s Department of Mathematics.

Study: Gene circuits reshape DNA folding and affect how genes are expressed

Anne Trafton | MIT News — Thu, 30 Apr 2026 14:00:00 -0400

When a gene is turned on in a cell, it creates a ripple effect along the DNA strand, changing the physical structure of the strand. A new study by MIT researchers shows that these ripples can stimulate or suppress neighboring genes.

These effects, which result from the winding or unwinding of neighboring DNA, are determined by the order of genes along a strand of DNA. Genes upstream of the active gene are usually turned up, while those downstream are inhibited.

The new findings offer guidance that could make it easier to control the output of synthetic gene circuits. By altering the relative ordering and arrangement of genes, or “gene syntax,” researchers could create circuits that synergize to maximize their output, or that alternate the output of two different genes.

“This is really exciting because we can coordinate gene expression in ways that just weren’t possible before,” says Katie Galloway, an assistant professor of chemical engineering at MIT. “Syntax will be really useful for dynamic circuits. Now we have the ability to select not only the biochemistry of circuits, but also the physical design to support dynamics.”

Galloway is the senior author of the study, which appears today in Science. MIT postdoc Christopher Johnstone PhD ’26 is the paper’s lead author. Other authors include MIT graduate student Kasey Love, members of the lab of Brandon DeKosky, an MIT associate professor of chemical engineering, and researchers from Peter Zandsta’s lab at the University of British Columbia and the labs of Christine Mummery and Richard Davis at Leiden University Medical Center in the Netherlands.

Gene syntax

When a gene is copied into messenger RNA, or “transcribed,” the double-stranded DNA helix must be unwound so that an enzyme called RNA polymerase can access the DNA and start copying it. That unwinding leads to physical changes in the structure of DNA strand.

Upstream of the gene, DNA becomes looser, while downstream, it becomes more tightly wound. These changes affect RNA polymerase’s ability to access the DNA: Upstream of an active gene, it’s easier for the enzyme to attach; downstream, it’s more difficult.

In a study published in 2022, Galloway and Johnstone performed computational modeling that explored how these biophysical changes might influence gene expression. They studied three different arrangements, or types of syntax: tandem, divergent, and convergent.

Most synthetic gene circuits are designed in a tandem arrangement, with one gene followed by another downstream. In a divergent arrangement, neighboring genes are transcribed in opposite directions (away from each other), and in convergent syntax, they are transcribed toward each other.

The modeling suggested that the divergent arrangement was most likely to produce circuits where both genes are expressed at a high level. Tandem arrangements were predicted to result in the downstream gene being suppressed by the upstream gene.In the new study, the researchers wanted to see if they could observe these predicted phenomena in human cells.

“Normally, we think about gene circuits and pieces of DNA as these lines that we draw, but they’re polymers that have physical characteristics,” Galloway says. “The thing that we were trying to solve in this paper was: When you put two genes on the same piece of DNA, how does their physical interaction become coupled?”

The researchers engineered circuits that each contained two genes, in either a tandem, divergent, or convergent configuration, into human cell lines and human induced pluripotent stem cells.

The results confirmed what their modeling had predicted: In divergent circuits, expression of both genes was amplified. In tandem circuits, turning on the upstream gene suppressed the expression of the downstream gene.

These effects produced as much as a 25-fold increase or decrease in gene expression, and they could be seen at distances of up to 2,000 base pairs between genes.

Using a high-resolution genome mapping technique called Region Capture Micro-C, the researchers were also able to analyze how the DNA structure changed when nearby genes were being transcribed.

As predicted, they found that the DNA regions downstream from an active gene formed tightly twisted structures known as plectonemes, similar to the tangles seen in a twisted telephone cord. These structures make it harder for RNA polymerase to bind to DNA.

To engineer these cells, the researchers used a new system they developed with the LUMC team called STRAIGHT-IN Dual, which allows them to efficiently insert two genes into the same DNA strand at both alleles. This system is being reported in a second paper published today, in Nature Biomedical Engineering.

Precise control

The new findings could help guide the design of synthetic gene circuits, which are usually designed to be controlled by biochemical interactions with activator or repressor molecules. Now, circuit designers can also perform biophysical manipulations to enhance or repress genes expression.

“Everyone thinks about the components they need, and the biochemical properties they need to build a circuit,” Galloway says. “Now, we have added the physical construction of those components, which is going to change how those biochemical units are interpreted.”

As a demonstration of one potential application, the researchers built synthetic circuits containing the genes for two segments of a novel antibody discovered by the Dekosky lab, used to treat yellow fever, and incorporated them into human cells. As they expected, the divergent syntax produced larger quantities of the yellow fever antibody.

Galloway’s lab has also used this approach to optimize the output of synthetic gene circuits they previously reported that could be used to deliver gene therapy or to reprogram adult cells into other cell types.

This strategy could also be used to build a variety of other types of dynamic synthetic circuits, such as toggle switches, oscillators, or pulse generators, for any application that requires precise control over gene expression.

“If you want coordinated expression, a divergent circuit is great. If you want something that’s either/or, you can imagine using a convergent or tandem circuit, so when one turns on, the other turns off, and you can alternate pulses,” Galloway says. “Now that we understand the syntax, I think this will pave the way for us to program dynamic behaviors.”

The research was funded, in part, by the National Institutes of Health, the National Institute for General Medical Sciences, a National Science Foundation CAREER Award, the Pershing Square Foundation, the Air Force Research Laboratory, and the Koch Institute Support (core) Grant from the National Cancer Institute.

The hidden structure behind a widely used class of materials

Zach Winn | MIT News — Thu, 30 Apr 2026 14:00:00 -0400

Materials called relaxor ferroelectrics have been used for decades in technologies like ultrasounds, microphones, and sonar systems. Their unique properties come from their atomic structure, but that structure has stubbornly eluded direct measurement.

Now a team of researchers from MIT and elsewhere has directly characterized the three-dimensional atomic structure of a relaxor ferroelectric for the first time. The findings, reported today in Science, provide a framework for refining models used to design next-generation computing, energy, and sensing devices.

“Now that we have a better understanding of exactly what’s going on, we can better predict and engineer the properties we want materials to achieve,” says corresponding author James LeBeau, MIT’s Kyocera Professor of Materials Science and Engineering. “The research community is still developing methods to engineer these materials, but in order to predict the properties those materials will have, you have to know if your model is right.”

In their paper, the researchers describe how they used an emerging technique to reveal the distribution of electric charges in the material, with a surprising result.

“We realized the chemical disorder we observed in our experiments was not fully considered previously,” says co-first authors Michael Xu PhD ’25 and Menglin Zhu, who are both postdocs at MIT. “Working with our collaborators, we were able to merge the experimental observations with simulations to refine the models and better predict what we see in experiments.”

Joining Zhu, Xu, and LeBeau on the paper are Colin Gilgenbach and Bridget R. Denzer, MIT PhD students in materials science and engineering; Yubo Qi, an assistant professor at the University of Alabama at Birmingham; Jieun Kim, an assistant professor at the Korea Advanced Institute of Science and Technology; Jiahao Zhang, a former PhD student at the University of Pennsylvania; Lane W. Martin, a professor at Rice University; and Andrew M. Rappe, a professor at the University of Pennsylvania.

Probing disordered materials

Leading simulations of relaxor ferroelectrics suggest that when an electric field is applied, the interactions of positively and negatively charged atoms in different nanoregions of the material help give rise to exceptional energy storage and sensing capabilities. The details of those nanoregions have been impossible to directly measure to date.

For their Science paper, the researchers studied a relaxor ferroelectric material used in sensors, actuators, and defense systems that is a lead magnesium niobate-lead titanate alloy. They used an emerging measurement technique, called multi-slice electron ptychography (MEP), in which researchers move a nanoscale-sized probe of high-energy electrons over a material and measure the resulting electron diffraction patterns.

“We do this in a sequential way, and at each position, we acquire a diffraction pattern,” Zhu explains. “That creates regions of overlap, and that overlap has enough information to use an algorithm to iteratively reconstruct three-dimensional information about the object and the electron wave function.”

The technique revealed a hierarchy of chemical and polar structures that spanned from atomic to mesoscopic scales. The researchers also found that many regions of differing polarization in the material were much smaller than predicted by the leading simulations. The researchers then fed their new data back into those computer simulations and refined the models to better reflect their findings under different conditions.

“Previously, these models basically had random regions of polarization, but they didn’t tell you how those regions correlate with each other,” Xu says. “Now we can tell you that information, and we can see how individual chemical species modulate polarization depending on the charge state of atoms.”

Toward better materials

Zhu says the paper demonstrates the potential of electron ptychography to study complex materials and opens up new avenues of research into complex, disordered materials.

“This study is the first time in the electron microscope that we’ve been able to directly connect the three-dimensional polar structure of relaxor ferroelectrics with molecular dynamics calculations,” Xu says. “It further proves you can get three-dimensional information out of the sample using this technique.”

The researchers also believe the approach could one day help engineer materials with advanced electronic behaviors for a range of improved memory storage, sensing, and energy technologies.

“Materials science is incorporating more complexity into the material design process — whether that’s for metal alloys or semiconductors — as AI has improved and our computational tools have become more advanced,” LeBeau says. “But if our models aren’t accurate enough and we have no way to validate them, it’s garbage in garbage out. This technique helps us understand why the material behaves the way it does and validate our models.”

The work was supported, in part, by the U.S. Army Research Laboratory, the U.S. Office of Naval Research, the U.S. Department of War, and a National Science Graduate Fellowship. The researchers also used MIT.nano facilities.

How neurons sense bacteria in the gut

David Orenstein | The Picower Institute for Learning and Memory — Thu, 30 Apr 2026 13:30:00 -0400

Recent studies suggest animals and people alike have close and complex relationships with the bacteria around and within them. The human gut microbiome, for instance, has been associated with both depression and Parkinson’s disease. To go beyond association toward understanding of the actual mechanisms that enable the bacterial microbiome to influence brain function, a new study by neuroscientists in The Picower Institute for Learning and Memory at MIT examines the mechanisms at work in a model “bacterial specialist,” the nematode Caenorhabditis elegans.

In the new open-access study in Current Biology, the team, led by Picower Fellow Cassi Estrem in the Picower Institute for Learning and Memory lab of Associate Professor Steven Flavell, identifies the specific chemicals that a key neuron in C. elegans senses, both in the bacteria that it eats and in the bacteria that it needs to avoid ingesting.

“In our bodies, our own cells are outnumbered by the bacterial cells living in and on us. There’s an increasing recognition that this has a profound impact on human health,” says Flavell, an investigator of the Howard Hughes Medical Institute and faculty member of MIT’s Department of Brain and Cognitive Sciences. “It’s been clear that there are links for some time. Our study aimed to identify the hard mechanisms of how a host nervous system is affected by bacteria in the alimentary canal.”

Achieving a fundamental mechanistic understanding of how neurons interact with bacteria could help improve attempts to intervene in or manipulate those interactions with therapeutic drugs or supplements, Flavell says.

Mmm … sugar

Flavell calls C. elegans a “bacterial specialist” because the tiny, transparent worm has evolved to eat bacteria as its diet, while also needing to avoid pathogenic bacteria that can prove to be its undoing. This has led it to develop a nervous system especially well-attuned to sorting out what is food and what is foe. In 2019, the lab discovered that the neuron NSM, which projects into the worm’s alimentary canal, employs two “acid sensing ion channels” (ASICs) to detect when certain bacteria have been ingested. Notably, those ion channels are analogous to ones found in neurons in humans. When NSM detects yummy bacteria, it releases serotonin that causes the worm to increase its feeding rate and slow its slithering so that it can stay to dine on the surrounding meal.

To really understand how this works, Flavell and Estrem realized they needed to know exactly what the ion channels are detecting in the bacteria. To get started, they exposed worms to 20 different kinds of bacteria the worms are known to encounter and found that they all activated NSM activity to varying extents. Then they broke the bacteria down into more and more specific chemical components to see which one or ones triggered NSM. The experiments ruled out many components, including DNA, lipids, proteins, and simple sugars, and instead found that it’s specifically the polysaccharide sugars that coat many bacteria that drive NSM activation. In particular, in gram-positive bacteria, a chemical called peptidoglycan activated NSM. In gram-negative bacteria, a different polysaccharide was apparently in play.

Estrem and Flavell’s team also ran experiments showing that polysaccharides from bacteria in general, and peptidoglycan in particular, not only trigger NSM electrical activity, but actually promote the feeding and slowing behaviors. They also showed that genetically knocking out the ASICs abolished these responses. In all, they demonstrated that polysaccharide and peptidoglycan detection are sufficient to trigger the worm’s behaviors, and requires the ASICs.

Better not eat this

Having shown what exactly triggers the worms to recognize their bacterial food, the researchers wondered whether they could also pinpoint a danger sign the worm finds in harmful bacteria. For these experiments, they carefully used Serratia marcescens, a bacterium that’s also infectious for humans. Some strains of the bacteria have a red color, while others do not. The red ones, which have a pigment called prodigiosin, tend to be much more lethal for worms. In their testing, the researchers found that when NSM detected the non-pigmented bacteria, the neuron still activated and the worms still ingested the bacteria, but when prodigiosin was present, NSM did not activate and the worm did not pump it in or slow down to eat.

Adding prodigiosin to normally yummy bacteria also suppressed NSM’s usual response. In other words, the worms have evolved their digestive behavior (and the detectors within NSM) to avoid ingesting a chemical specifically associated with danger.

Flavell says it’s likely that some of the fundamental mechanisms highlighted in the new paper will inform studies of similar mechanisms in other animals.

“We developed a way of identifying these pathways by studying this organism that specializes in bacterial detection and displays robust responses,” Flavell explains. “But there’s no reason these pathways should be limited to C. elegans. The molecular players we identified are found in many species, including mammals.”

In addition to Estrem and Flavell, the paper’s other authors are Malvika Dua, Colby Fees, Greg Hoeprich, Matthew Au, Bruce Goode, and Lingyi Deng.

The National Institutes of Health, the McKnight Foundation, the Alfred P. Sloan Foundation, the Howard Hughes Medical Institute, and The Freedom Together Foundation provided support for the study.

A materials scientist’s playground

Amanda Stoll DiCristofaro | MIT.nano — Thu, 30 Apr 2026 13:20:00 -0400

Scientists and engineers around the world are working to improve quantum bits, or qubits, the minuscule building blocks of the quantum computer. Qubits are incredibly sensitive, making it easy for errors to be introduced, lowering device yield. But a new cluster tool at MIT.nano introduces capabilities that will allow researchers to continue advancements in qubit performance.

Passersby outside MIT.nano may have recently noticed a complex looking piece of equipment being installed on the first-floor cleanroom. What looks like a sci-fi movie prop is actually a state-of-the-art, custom-built molecular beam epitaxy (MBE): a physical vapor deposition system that operates under ultra-high vacuum to produce high-quality thin films. With the ability to grow different crystalline materials on a wafer, the tool will support quantum researchers and materials scientists by allowing them to study how film growth affects the properties of the materials used in making qubits.

“To realize the full promise of quantum computing, we need to build qubits that are robust, reproducible, and extensible,” says William D. Oliver, the Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science and professor of physics at MIT. “To date, most of the improvements to superconducting qubit performance are traceable to circuit design — essentially, designing qubit circuits that are less sensitive to their environmental noise. However, those improvements have largely run their course. Going forward, we need to address the fundamental materials science and fabrication engineering required to reduce the sources of environmental noise. This multi-chamber, cassette-loaded, 200-millimeter wafer MBE system is exactly the right tool at the right time. And there’s no place better to do this research than at MIT.nano.”

That is because MIT.nano is preconditioned to receive this type of system with physical space, climate controls, policies and procedures for researchers, and expert staff to manage the lab. Through an equipment support plan, Oliver’s Engineering Quantum Systems (EQuS) group is able to install and run the tool inside MIT.nano, a high-performance, safe, and reliable environment.

A controlled environment is essential for the MBE. “Think of this system like an inverted International Space Station (ISS),” explains Patrick Strohbeen, research scientist in the EQuS group. “The ISS is a small chamber of atmosphere surrounded by the vacuum of space. This MBE system is a chamber of space-level vacuum surrounded by atmosphere.” That vacuum of space is kept at a steady negative 90 degrees Celsius, which enables precise growth of thin films on an atomic scale. It is the largest single deposition chamber (1-meter diameter) the manufacturer, DCA, has sold in the United States.

The journey of a wafer

The system, which in total takes up 600 square feet, is made up of six chambers. First is the load lock, where the wafer is placed into the system and brought down from atmospheric pressure to near the vacuum level of space. Then, the wafer enters the distribution center. This space acts like a central hub, transferring the wafers to other chambers. Next is the deposition, or “growth,” chamber. This is where the system’s primary function takes place — depositing materials, specifically atoms of superconducting metal, onto a substrate, typically silicon. From there, it moves to the oxidation chamber, which facilitates the growth of key ceramic materials for qubits. A fifth storage chamber can hold an additional 10 wafers within the vacuum.

A unique aspect of this system is its sixth chamber, designed for X-ray photoelectron spectroscopy (XPS). Using this chamber, researchers can shoot a photon in the form of X-rays at the surface and, when it hits the surface, it will excite the electron inside the material so that the electron jumps out and is picked up by a sensor that then tells the researcher about the environment the electron came from. As individual layers of atoms are put down in the growth chamber, scientists can move the wafer to the XPS chamber to measure changes in the material structure of the film and back again, all while keeping it inside the vacuum space.

Why is this important? “The quantum community has excellent device physicists and device engineers,” said Strohbeen. “The last piece of the puzzle is: We need to understand the materials platform that we’re using for these devices.” The buried interfaces, so far, have been understudied due to the difficulty in probing them, he explained.

For those of us who are not MBE experts, think of the snow that fell in Massachusetts this winter. How can you tell how much ice is on the pavement without removing all of the snow on top of it? And without changing the natural setting where the snow, ice, and pavement meet? With this system, specifically the XPS chamber, scientists can study the interfaces of buried materials without disturbing the physical or chemical environments. “It is a materials scientist’s playground,” jokes Strohbeen — a controlled space where researchers can learn about and explore materials’ interactions within layers of atoms.

Why MIT.nano?

When Oliver, who is also the director of the MIT Center for Quantum Engineering, secured the MBE Quantum, the next question was where to put it. Enter MIT.nano. Housing 45,000 square feet of cleanroom, this facility exists at MIT to support complex, sensitive equipment with both the infrastructure and the staff needed to maintain it.

“MIT.nano’s ultra-stable building utilities and lab environment are exactly what is needed to support a system that demands extreme repeatability and purity,” says Nick Menounos, MIT.nano associate director of infrastructure. “The success of this installation grew from the early collaboration. Professor Oliver engaged the MIT.nano team in the procurement process almost two years in advance. That foresight, combined with the infrastructure momentum we gained from the recent CHIPS Act project, meant that we could prepare the cleanroom perfectly. We compressed the installation process that normally takes several months and had this extraordinary machine running in under three weeks.”

“From the very beginning, the MIT.nano staff were helpful, knowledgeable, and willing to go above and beyond to make this happen,” says Oliver. “While the MIT.nano facility is certainly an infrastructural crown jewel at MIT, it’s the MIT.nano staff who make it the national treasure it is today.”

Positioning the MBE Quantum in the cleanroom helps the team focus on scalability and device yield. Humidity and particle count, two things carefully measured and maintained at MIT.nano, can affect the output of the device. Minimizing as many variables as possible is key to improving qubit performance. The cleanroom also allows for new device research because an array of fabrication and metrology tools are available without having to leave the clean environment.

“We’re really excited to see what we can do with it,” says Strohbeen. “We bought it as a materials science tool, and it will also be a device development tool due to the flexibility of having it in the cleanroom.”

The MBE system was purchased through a combination of grants from the Army Research Office (ARO) and from the Laboratory for Physical Sciences (LPS). The ARO grant, a Defense University Research Instrumentation Program grant, is the premier grant from ARO for funding large capital equipment purchases that should prove disruptive in technologically relevant areas. It arrives at an important time on campus, as one of MIT’s strategic initiatives — the MIT Quantum Initiative — aims to apply quantum breakthroughs to the most consequential challenges in science, technology, industry, and national security.

Making the case for curiosity-driven science

Thu, 30 Apr 2026 00:00:00 -0400

“The thing that really struck me when I came to MIT and strikes me every single day is the stuff that’s going on here is amazing. The science, the engineering… every day I hear something that makes my jaw drop,” remarked President Sally Kornbluth during a live discussion with Lizzie O’Leary of Slate’s “What Next: TBD” podcast.

Kornbluth spoke about everything from the importance of curiosity-driven science and why basic science is critical to our nation’s future, to AI and education, and even bravely joined O’Leary in a rendition of the Williams College song, “The Mountains,” in honor of their shared alma mater.

“We are in this time of incredible uncertainty,” said Kornbluth of the current state of higher education and funding for scientific research. “What we are trying to do is keep the science robust.”

Bouncing back to her time at Duke and her love of college basketball, she noted it’s a combination of zone coverage and man-to-man defense when trying to address skepticism about higher education in Washington, D.C. She emphasized: “As one of the top institutions in the world it’s part of our responsibility to articulate the importance of science. Behind the scenes, I am – along with many other [university] presidents – I am in D.C. all the time now. I want to speak to Congressmen and women, Senators, people in the executive branch to explain the importance of what we are doing.”

Kornbluth emphasized that the pipeline of basic science that flows from U.S. universities is a critical asset for our country, cautioning that to keep straining this pipeline could have enormous negative ramifications for the U.S. down the line.

“If you think about research done in this country, it’s done in in universities, it’s done in national labs, and it’s done in industry,” said Kornbluth. Universities are where most of the science with a long pathway to impact, requiring patience, starts. She pointed to immunotherapy for cancer, which began 30-40 years ago in basic immunotherapy research, as an example. With that pipeline being drained, what does the future hold for new cancer therapies or new AI and quantum technologies?

Kornbluth also underscored that uncertainty and lost funding are having a “huge impact on the talent pipeline,” delving into the unique role universities play in training graduate students, who are the next generation of scientific researchers. “We hear, ‘Oh it would be okay if research was more in industry.’ I say, ‘Would you fly on a plane with a pilot who had never flown?’ How do they think people learn how to do research? We are training the next generation… and we are losing funding for them.” She added: “I think we are going to see reverberations for many decades if we don’t rectify that issue.”

When asked how she and her colleagues are working to keep research moving forward, Kornbluth explained that at MIT, “we have tried to find alternative ways to elevate the science. We have a series of presidential initiatives that cut across the whole campus in things like health and life sciences, quantum, humanities and social sciences. The notion is that we are trying to create new opportunities.”

Still, she acknowledged that losses from the endowment tax and diminished federal funding are painful. “There are only four schools right now that are subject to the 8% endowment tax, which is a tax on our earnings. For us, that means $240 million dollars a year plus other losses in grants. So, let’s say the whole thing is, we budgeted for a loss of $300 million a year on a $1.7 billion budget… That has definitely had an impact on us. No question about it.

“The other thing about it is again there’s all this uncertainty. Our investigators are writing a ton of grants. They don’t know if they’re going off into the void or they really have the sort of competitive opportunities they’ve always had in the past.”

Asked why universities did not see this moment coming, Kornbluth offered a few thoughts. “Look at MIT – 30,000 companies have come from MIT. When you look at something like that, why would you think any government that wants economic flourishing in their country would come after MIT?” she reflected. “It just never would have occurred to us.”

Turning towards the rapid advances in AI, and how the field is impacting education, Kornbluth noted that at MIT and other universities, “we have to focus on the human element, we have to educate our students, they need to know how to write and do mathematics…they have to view AI as a tool to augment their capabilities. That is how we are thinking about it.”

In the course of the conversation, Kornbluth also expressed her unwavering support for international students, noting that most want the opportunity to stay and contribute to research in the U.S. after graduation. “The talent brought to us through our international community is unbelievable. We can attract the very best in the world. You can bet when they talk about competitiveness with China, for example, in AI, quantum, etc., they are not sitting around in China saying, ‘Oh it’s great America is taking all our students.’ They’re thinking, ‘It’s great that America doesn’t want to take as many of our students anymore because we can train them.’ It’s a competitive issue that we really should lean into.”

Study: Immigrants help address the US eldercare shortage

Peter Dizikes | MIT News — Thu, 30 Apr 2026 00:00:00 -0400

Good caregivers are often in short supply, but after the Covid-19 pandemic hit the U.S. in early 2020, staff levels at nursing homes dropped by 10 percent. What was a simple personnel shortage has moved closer to being a nursing-care crisis.

“We have an aging population, care for them is labor-intensive, and there are shortages everywhere in that supply chain,” says MIT economist Jonathan Gruber.

As it happens, about one-fifth of health care support workers in the U.S. are immigrants. And as a newly published study of the nation’s metro areas shows, changes in immigration levels can affect how much nursing care the elderly receive.

“When immigration rises in a city, it significantly increases the health care workforce,” says Gruber, co-author of the study and a paper detailing its findings.

Overall, Gruber and his colleagues determined that when there is more immigration, registered nurses and other aides work more hours at nursing homes, without displacing already-employed caregivers, while patient outcomes improve. Essentially, a 10 percent increase in female immigrants in a given metro area leads to a 1.1 percent increase in hours that registered nurses spend with elderly patients, while hospitalizations for those patients drop, among other things.

“Even if immigration actually increases labor supply to the medical sector, it was an open question if that would improve outcomes, and it does,” adds Gruber, the Ford Professor of Economics and head of the MIT Department of Economics.

The paper, “Immigration, the Long-Term Care Workforce, and Elder Outcomes in the U.S.,” appears in the American Journal of Health Economics. The authors are Gruber; David C. Grabowski, a professor in the Department of Health Care Policy at Harvard Medical School; and Brian E. McGarry, an assistant professor in the Department of Medicine and the Department of Public Health Sciences at the University of Rochester.

More care, fewer hospitalizations

To conduct the study, the researchers tapped into multiple data sources, including immigration information from 2000 to 2018 appearing in the U.S. Census Bureau’s American Community Survey. Extensive nursing home data came from different types of reports that facilities are required to file in order to maintain Medicare and Medicaid eligibility, allowing the scholars to examine care staffing levels and patient outcomes.

All told, the study encompasses 16 million Medicare beneficiaries in over 13,000 nursing homes in metropolitan statistical areas of the U.S., and evaluates immigrations flows for two decades.

“One of the key groups that’s taking care of our nation’s elders is immigrants,” Gruber says. “So I thought it would be fascinating to understand how much does immigration actually matter for elder care.”

More specifically, the scholars find that for every 10 percent increase in immigration above the norm in metro areas, in addition to the 1.1 percent increase in registered nurse hours, there is a 0.7 percent increase in hours of care provided by certified nurse assistants. There is a 0.6 percent decline in hospitalizations for patients making short-term stays, of up to a month, in nursing homes.

Beyond that, the study yielded other markers showing that patient outcomes improve in these situations. The roughly 1 percent increase in hours of care was accompanied by a decline in the use of physical restraints needed for patients, who also needed less psychiatric medication prescriptions and had fewer urinary tract infections, among other things.

The fact that those outcomes improved in more immigrant-staffed situations is among the new insights provided by the research.

“There’s a lot of evidence that providing more labor supply to the elderly sector improves patient outcomes,” Gruber says. “But it wasn’t clear whether more immigrants would work the same way, because of language issues or other factors.”

A new lens

The study comes as immigration policy has become a major issue in the U.S., something that Gruber says helped spur his curiosity about its health care implications — although he did not know what the study would reveal, one way or another. In this case, he notes, the impact of immigration on eldercare may be another factor to be considered in the larger debates about the subject.

“I think it provides a new lens on the debate over immigration,” Gruber says. “The debate over immigration has been solely about what will it do to native workers, what will it do to the crime rate, what will it do to tax collection. This adds a new element, which is: What will it do to our citizens’ care? By having more immigration, we provide more care.”

Gruber, Grabowski, and McGarry are continuing to study this issue. In a new working paper, released in February, they found that increases in immigration are consistent with a reduction in the mortality rate, in part by allowing more elderly people the opportunity to receive care at home.

Gruber recognizes that there will continue to be sharp policy disagreements over immigration. Still, as the just-published paper states, to this point, when it comes to nursing care, the “results paint a consistent picture of improved quality of care resulting from increased immigration.”

Solving the “Whac-a-mole dilemma”: A smarter way to debias AI vision models

Alex Ouyang | Abdul Latif Jameel Clinic for Machine Learning in Health — Wed, 29 Apr 2026 17:40:00 -0400

In today’s hospitals and clinics, a dermatologist may use an artificial intelligence model for classifying skin lesions to assess if the lesion is at risk of developing into a cancer or if it is benign. But if the model is biased toward certain skin tones, it could fail to identify a high-risk patient.

Perhaps one of the best known and most persistent challenges that AI research continues to reckon with is bias. Bias is often discussed in relation to training data, but model architecture can also contain and amplify bias, negatively influencing model performance in real-world settings. In high-stakes medical scenarios, the very real consequences of poor performance have made bias into a quintessential safety issue.

A new paper from researchers at MIT, Worcester Polytechnic Institute, and Google that was accepted to the 2026 International Conference for Learning Representations proposes a novel debiasing approach called “Weighted Rotational DebiasING” (i.e., WRING) that can be applied to vision language models (VLMs), like OpenAI’s OpenCLIP.

VLMs are multi-modal models that can understand and interpret different data modalities like video, image, and text simultaneously. While debiasing approaches for VLMs do exist, the most commonly used approach is known as “projection debiasing,” which leads to what has been termed the “Whac-A-Mole dilemma”, an empirical observation that was formally introduced to AI research in 2023.

Projection debiasing is a post-processing approach that removes the undesirable, biased information from model embeddings by “projecting” the subspace out of a representation space of relationships, thereby cutting out the bias. But this approach has its drawbacks.

“When you do that, you inadvertently squish everything around,” says Walter Gerych, the paper’s first author, who conducted this research last year as a postdoc at MIT. “All the other relationships that the model learns change when you do that.”

Gerych, who is now an assistant professor of computer science at Worcester Polytechnic Institute, is joined on the paper by MIT graduate students Cassandra Parent and Quinn Perian; Google’s Rafiya Javed; and MIT associate professors of electrical engineering Justin Solomon and Marzyeh Ghassemi, who is an affiliate of the Abdul Latif Jameel Clinic for Machine Learning and Health and the Laboratory for Information and Decision Systems.

While projection debiasing stops the model from acting upon the bias that’s been projected out of the subspace, it can end up amplifying and creating other biases, hence the Whac-A-Mole dilemma. According to Ghassemi, the unintended amplification of model biases is “both a technical and practical challenge. For instance, when debiasing a VLM that retrieves images of clinical staff — if racial bias is removed — it could have the unintended consequence of amplifying gender bias.”

WRING works by moving certain coordinates within the high-dimensional space of a model — the ones that appear to be responsible for bias — to a different angle, so the model can no longer distinguish between different groups within a certain concept. This changes the representation within a specific space while leaving the model’s other relationships intact. And like projection debiasing, WRING is a post-processing approach, which means it can be applied “on the fly” to a pre-trained VLM.

“People already spent a lot of resources, a lot of money, training these huge models, and we don’t really want to go in and modify something during training because then you have to start from scratch,” Gerych explains. “[WRING is] very efficient. It doesn’t require more training of the model and it’s minimally invasive.”

In their results, the researchers found that WRING significantly reduced bias for a target concept without increasing bias in other areas. But for now, the approach is somewhat limited to Contrastive Language-Image Pre-training (CLIP) models, a type of VLM that connects images to language for search or classification.

“Extending this for ChatGPT-style, generative language models, is the reasonable next step for us,” says Gerych.

This work was supported, in part, by a National Science Foundation CAREER Award, AI2050 Award Early Career Fellowship, Sloan Research Fellow Award, the Gordon and Betty Moore Foundation Award, and MIT-Google Computing Innovation Award.

Transforming deep-space signals into cathedral sound

Juliana DiVirgilio | MIT Kavli Institute for Astrophysics and Space Research — Wed, 29 Apr 2026 14:30:00 -0400

A new immersive sound installation at Oulu Cathedral, Finland, brings the research of MIT astrophysicist and associate professor of physics Kiyoshi Masui into a striking sensory form, transforming more than 4,000 cosmic signals into spatial audio.

With its grand opening on April 4, “The Logos” project invites visitors to experience deep-space phenomena not as distant abstractions, but as something immediate and resonant. The work is led by artist and creative technologist Andrew Melchior in collaboration with Masui, philosopher Timothy Morton, and cathedral dean Satu Saarinen. Together, they treat the cathedral, built in 1832, not just as a setting but as part of the instrument itself. Its stone surfaces and reverberant acoustics give physical presence to signals that have traveled from distant galaxies.

At the heart of the installation are data gathered by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) radio telescope, which detects fast radio bursts (FRBs). FRBs are immensely energetic flashes lasting only milliseconds and originating in distant galaxies across the observable universe. The Logos represents one of the most extensive artistic sonifications of FRB data to date. Each day at noon, the cathedral is filled with a one-hour procedural composition derived from these bursts. Some bursts are singular events, never repeating, while others pulse again and again from unknown sources. These patterns remain one of astrophysics’ most compelling mysteries.

“The fast flashes will echo as snare-like beats bouncing through the cathedral,” says Masui. “The sweeping dispersion of the signal — where different radio frequencies arrive at slightly different times — creates harmonies between high and low tones. It should feel rich and layered, while also revealing something real about how these signals travel across billions of years of cosmic space before reaching Earth.”

By converting FRB data into a shared listening experience, the collaboration suggests a different way of understanding the universe: not only through analysis, but through attention.

Running through April 2027 to mark the cathedral’s 250th anniversary, The Logos will feature as part of Oulu2026 European Capital of Culture and the Lumo Art and Tech Festival.

The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing

MIT Schwarzman College of Computing — Wed, 29 Apr 2026 06:00:00 -0400

The following is a joint announcement by the MIT Schwarzman College of Computing and IBM.

IBM and MIT today announced the launch of the MIT-IBM Computing Research Lab, advancing their long-standing collaboration to shape the next era of computing. The new lab expands its scope to include quantum computing, alongside foundational artificial intelligence research, with the goal of unlocking new computational approaches that go beyond the limits of today’s classical systems.

The MIT-IBM Computing Research Lab builds on a distinguished history of scientific excellence at the intersection of research and academia. Evolving from the MIT-IBM Watson AI Lab, which originated in 2017 on MIT’s campus, the new lab reflects a transformed technology landscape — one which AI has entered mainstream deployment, and quantum computing is rapidly advancing toward practical impact. Together, MIT and IBM aim to help lead research in AI and quantum and to redefine mathematical foundations across both domains.

“We expect the MIT-IBM Computing Research Lab to emerge as one of the world’s premier academic and industrial hubs accelerating the future of computing,” says Jay Gambetta, director of IBM Research and IBM Fellow, and IBM chair of the MIT-IBM Computing Research Lab. “Together, the brightest minds at MIT and IBM will rethink how models, algorithms, and systems are designed for an era that will be defined by the sum of what’s possible when AI and quantum computing come together.”

“For a decade, the collaboration between MIT and IBM has produced leading-edge research and innovation, and provided mentorship and supported the professional growth of researchers both at MIT and IBM,” says Anantha Chandrakasan, MIT’s provost, who, as then-dean of the School of Engineering, spearheaded the creation of the MIT-IBM Watson AI Lab and will continue as MIT chair of the lab. “The incredible technical achievements sets the bar high for our work together over the next 10 years. I look forward to another decade of impact.”

Addressing the next frontiers in computation

The MIT-IBM Computing Research Lab will serve as a focal point for joint research between MIT and IBM in AI, algorithms, and quantum computing, as well as the integration of these technologies into hybrid computing systems. The lab is designed to accelerate progress toward powerful new computational approaches that take advantage of rapid advances in AI and quantum-centric supercomputing, including those that combine maturing quantum hardware with classical systems and advanced AI methods.

This research initiative will include improving capabilities and integrating AI with traditional computing, alongside pursuing advances in small, efficient, modular language model architectures, novel AI computing paradigms, and enterprise-focused AI systems designed for deployment in real-world environments, where reliability, transparency, and trust are essential.

In parallel, the lab will rethink the mathematical and algorithmic foundations that underpin the next era of computing by accelerating the development of novel quantum algorithms for complex problems, with impacts in areas such as materials science, chemistry, and biology.

Additionally, the lab will investigate mathematical and algorithmic foundations of machine learning, optimization, Hamiltonian simulations, and partial differential equations, which are used to approximate the behaviors of dynamical systems that currently stump classical systems beyond limited scales and accuracy. Innovations from the lab could have wide implications for global industries, from more accurate weather and air turbulence prediction to better forecasts of financial market performance. Similarly, with improved optimization approaches, research from the lab could help lower risks in areas like finance, predict protein structures for more targeted medicine, and streamline global supply chains.

With its focus on AI, algorithms, and quantum, the MIT-IBM Computing Research Lab will complement and enhance the work of two of MIT’s strategic initiatives, the MIT Generative AI Impact Consortium and the MIT Quantum Initiative. MIT President Sally Kornbluth launched these strategic initiatives to broaden and deepen MIT’s impact in developing solutions to serious global challenges. The MIT-IBM Computing Research Lab will also leverage IBM’s longtime leadership and expertise in quantum computing. As part of its ambitious roadmap, IBM has laid out a clear path to delivering the world’s first fault-tolerant quantum computer by 2029, and is working across industries to drive value from quantum-centric supercomputing, tightly integrating quantum computers with high-performance computing and AI accelerators to solve the world’s toughest problems.

Deep integration with scientific domains

The MIT-IBM Computing Research Lab will also continue to serve as a foundation for training the next generation of computational scientists and innovators. It will do so by engaging faculty and students across MIT departments, enabling new computational approaches to accelerate discoveries in the physical and life sciences.

The lab will continue to be co-directed by Aude Oliva, senior research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory, and David Cox, vice president of AI Foundations at IBM Research. MIT and IBM have appointed leads for each of the lab’s three focus areas — AI, algorithms, and quantum. Jacob Andreas, associate professor in the Department of Electrical Engineering and Computer Science (EECS), and Kenney Ng, principal research scientist at IBM Research and the MIT-IBM science program manager, will co-lead AI; Vinod Vaikuntanathan, the Ford Foundation Professor of Engineering in EECS, and Vasileios Kalantzis, IBM Research senior research scientist, will co-lead algorithms; and Aram Harrow, professor of physics, and Hanhee Paik, IBM director of Quantum Algorithm Centers, will co-lead quantum.

“The MIT-IBM Computing Research Lab reflects an important expansion of the collaboration between MIT and IBM and the increasing connections across AI, algorithms, and quantum. This deepened focus also underscores a strong alignment with the MIT Schwarzman College of Computing’s mission to advance the forefront of computing and its integration across disciplines,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and MIT co-chair of the lab. “I’m excited about what this next chapter will enable in these three areas, and their impact broadly.”

Building on nearly a decade of collaboration

The MIT-IBM Watson AI Lab helped pioneer a model for academic-industry research collaboration, aligning long-term scientific inquiry with real-world impact. Since its inception, the lab has funded over 210 research projects involving over 150 MIT faculty members and over 200 IBM researchers. Collectively, the projects have led to over 1,500 peer-reviewed articles. The lab also helped shape the career growth of a number of MIT students and junior researchers, funding more than 500 students and postdocs.

“The true measure of this lab is not just innovation, but transformation of a field. Hundreds of students have contributed to thousands of publications in top conferences and journals, demonstrating their capabilities to address meaningful problems,” says Oliva. “The MIT-IBM Computing Research Lab builds on an extraordinary legacy of impact to advance a trusted collaboration that will redefine the future of AI and quantum computing in a way never seen before.”

“By coupling academic rigor with industrial scale, the lab aims to define the computational foundations that will power the next generation of AI, quantum, and scientific breakthroughs,” says Cox. “By bringing together advances in AI, algorithms, and quantum computing under one integrated research effort, we’re creating the conditions to rethink the mathematical and computational foundations of science and engineering.”

The MIT-IBM Computing Research Lab will capitalize on this foundation, expanding both the scientific scope and the ecosystem of collaborators across the Cambridge-Boston region and beyond.

MIT engineers’ virtual violin produces realistic sounds

Jennifer Chu | MIT News — Wed, 29 Apr 2026 05:00:00 -0400

There is no question that violin-making is an art form. It requires a musician’s ear, a craftsperson’s skill, and an historian’s appreciation of lessons learned over time. Making a violin also takes trust: Violin makers, or luthiers, often must wait until the instrument is finished before they can hear how all their hard work will sound.

But a new tool developed by MIT engineers could help luthiers play around with a violin’s design and tweak its sound even before a single part is carved.

In a study appearing today in the journal npj Acoustics, the MIT team reports on a new “computational violin” — a computer simulation that captures the detailed physics of the instrument and realistically produces the sound of a violin when its strings are plucked.

While there are software programs and plug-ins that enable users to play around with virtual violins, their sounds are typically the result of sampling and averaging over thousands of notes played by actual violins.

In contrast, the new computational violin takes a physics-based approach: It produces sound based on the way the instrument, including its vibrating strings, physically interacts with the surrounding air.

As a demonstration, the researchers applied the computational violin to play two short excerpts: one from “Bach’s Fugue in G Minor,” and another from “Daisy Bell” — a nod to the first song that was ever produced by a computer-synthesized voice.

The computational violin currently simulates the sound of plucked strings — a type of playing that musicians know as “pizzicato.” Violin bowing, the researchers say, is a much more complicated interaction to model. However, the computational violin represents the first physics-based foundation of a strung violin sound that could one day be paired with a model of bowing to produce realistic, bowed violin music.

For now, the team says the new virtual violin could be used in the initial stages of violin design. Luthiers can tweak certain parameters such as a violin’s wood type or the thickness of its body, and then listen to the sound that the instrument would make in response.

“These days, people try to improve designs little by little by building a violin, comparing the sound, then making a change to the next instrument,” says Yuming Liu, senior research scientist at MIT. “It’s very slow and expensive. Now they can make a change virtually and see what the sound would be.”

“We’re not saying that we can reproduce the artisan’s magic,” adds Nicholas Makris, professor of mechanical engineering at MIT. “We’re just trying to understand the physics of violin sound, and perhaps help luthiers in the design process.”

Makris and Liu’s MIT co-authors include Arun Krishnadas PhD ’23 and former postdoc Bryce Campbell, along with Roman Barnas of the North Bennet Street School.

Sound matrix

The quality of a violin’s sound is determined by its dimensions and design. The instrument is made from thoughtfully crafted parts and materials that all work to generate and amplify sound. In recent years, scientists have sought to understand what artisans have intuited for centuries, in terms of what specific parameters shape a violin’s sound.

In one early effort in 2006, scientists, as part of the Strad3D project, put a rare Stradivarius violin through a CT scanner. The violin was crafted in 1715 by the master violinmaker Antonio Stradivari, during what is considered the “Golden Age” of violin making. To better understand the violin’s anatomy and its relation to sound, the scientists scanned the instrument and produced 600 “slices,” or views, of the violin.

The CT scans are available online for people to view and use as data for their own experiments. For their study, Makris and his colleagues first imported the CT scans into a solid modeling software program to generate a detailed three-dimensional model of the violin. They then ran a finite element simulation, essentially dividing the violin into millions of tiny individual cubes, or “elements.”

For each cube, they noted its material type, such as if a cube from the violin’s back plate is made from maple or spruce, or if a string is made from steel or natural fibers. They then applied physics-based equations of stress and motion to predict how each material element would move in relation to every other element across the instrument.

They also carried out a similar process for the air surrounding the violin, dividing up a roughly cubic-meter volume of air and applying acoustic wave equations to predict how each tiny parcel of air would move and contribute to generating sound.

“The entire thing is a matrix of millions of individual elements,” explains Krishnadas. “And ultimately, you see this whole three-dimensional being, which is the violin and the air all connected and interacting with each other.”

A plucky model

The team then simulated how the new computational violin would sound when plucked. When a violinist plucks a string, they pull the string sideways and let it go, causing the string to vibrate. These vibrations travel across the instrument and inside it; the air’s vibrations are amplified as they travel out of the violin and into the surroundings, where a listener hears the vibrations as sound.

For their purposes, the engineers simulated a simple string pluck by directing one of the virtual violin’s strings to stretch out and then rebound. The simulation computed all the resulting motions and vibrations of the millions of elements in the violin, and the sound that the pluck would produce.

For notes that require pressing down on a violin’s fingerboard, they simulated the same plucking, and in addition, included a condition in which the string is held fixed in the section of the fingerboard where a violinist’s finger would press down.

The researchers carried out this computational process to virtually pluck out the notes in several measures of “Daisy Bell” and “Bach’s Fugue in G Minor.”

“If there’s anything that’s sounding mechanical to it, it’s because we’re using the exact same time function, or standard way of plucking, for each note,” says Makris, who is himself a lute player. “A musician will adapt the way they’re plucking, to put a little more feeling on certain notes than others. But there could be subtleties which we could incorporate and refine.”

As it is, the new computational model is the first to generate realistic sound based on the laws of physics and acoustics. The researchers say that violin makers could use the model to test how a violin might sound when certain dimensions or properties are changed. For instance, when the researchers varied the thickness of the virtual violin’s back plate or changed its wood type, they could hear clear differences in the resulting sounds.

“You can tweak the model, to hear the effect on the sound,” Makris says. “Since everything obeys the laws of physics, including a violin and the music it makes, this approach can add an appreciation to what makes violin sound. But ultimately, we get most of our inspiration from the artisans.”

This work was supported, in part, by an MIT Bose Research Fellowship.

Enabling privacy-preserving AI training on everyday devices

Adam Zewe | MIT News — Wed, 29 Apr 2026 00:00:00 -0400

A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81 percent. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.

The MIT researchers boosted the efficiency of a technique known as federated learning, which involves a network of connected devices that work together to train a shared AI model.

In federated learning, the model is broadcast from a central server to wireless devices. Each device trains the model using its local data and then transfers model updates back to the server. Data are kept secure because they remain on each device.

But not all devices in the network have enough capacity, computational capability, and connectivity to store, train, and transfer the model back and forth with the server in a timely manner. This causes delays that worsen training performance.

The MIT researchers developed a technique to overcome these memory constraints and communication bottlenecks. Their method is designed to handle a heterogenous network of wireless devices with varied limitations.

This new approach could make it more feasible for AI models to be used in high-stakes applications with strict security and privacy standards, like health care and finance.

“This work is about bringing AI to small devices where it is not currently possible to run these kinds of powerful models. We carry these devices around with us in our daily lives. We need AI to be able to run on these devices, not just on giant servers and GPUs, and this work is an important step toward enabling that,” says Irene Tenison, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Her co-authors include Anna Murphy ’25, a machine-learning engineer at Lincoln Laboratory; Charles Beauville, a visiting student from Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and a machine-learning engineer at Flower Labs; and senior author Lalana Kagal, a principal research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The research will be presented at the IEEE International Joint Conference on Neural Networks.

Reducing lag time

Many federated learning approaches assume all devices in the network have enough memory to train the full AI model, and stable connectivity to transmit updates back to the server quickly.

But these assumptions fall short with a network of heterogenous devices, like smartwatches, wireless sensors, and mobile phones. These edge devices have limited memory and computational power, and often face intermittent network connectivity.

The central server usually waits to receive model updates from all devices, then averages them to complete the training round. This process repeats until training is complete.

“This lag time can slow down the training procedure or even cause it to fail,” Tenison says.

To overcome these limitations, the MIT researchers developed a new framework called FTTE (Federated Tiny Training Engine) that reduces the memory and communication overhead needed by each mobile device.

Their framework involves three main innovations.

First, rather than broadcasting the entire model to all devices, FTTE sends a smaller subset of model parameters instead, reducing the memory requirement for each device. Parameters are internal variables the model adjusts during training.

FTTE uses a special search procedure to identify parameters that will maximize the model’s accuracy while staying within a certain memory budget. That limit is set based on the most memory-constrained device.

Second, the server updates the model using an asynchronous approach. Rather than waiting for responses from all devices, the server accumulates incoming updates until it reaches a fixed capacity, then proceeds with the training round.

Third, the server weights updates from each device based on when it received them. In this way, older updates don’t contribute as much to the training process. These outdated data can hold the model back, slowing the training process and reducing accuracy.

“We use this semi-asynchronous approach because want to involve the least powerful devices in the training process so they can contribute their data to the model, but we don’t want the more powerful devices in the network to stay idle for a long time and waste resources,” Tenison says.

Achieving acceleration

The researchers tested their framework in simulations with hundreds of heterogeneous devices and a variety of models and datasets. On average, FTTE enabled the training procedure to reach completing 81 percent faster than standard federated learning approaches.

Their method reduced the on-device memory overhead by 80 percent and the communication payload by 69 percent, while attaining near the accuracy of other techniques.

“Because we want the model to train as fast as possible to save the battery life of these resource-constrained devices, we do have a tradeoff in accuracy. But a small drop in accuracy could be acceptable in some applications, especially since our method performs so much faster,” she says.

FTTE also demonstrated effective scalability and delivered higher performance gains for larger groups of devices.

In addition to these simulations, the researchers tested FTTE on a small network of real devices with varying computational capabilities.

“Not everyone has the latest Apple iPhone. In many developing countries, for instance, users might have less powerful mobile phones. With our technique, we can bring the benefits of federated learning to these settings,” she says.

In the future, the researchers want to study how their method could be used to increase the personalized performance of AI models on each device, rather than focusing on the average performance of the model. They also want to conduct larger experiments on real hardware.

This work was funded, in part, by a Takeda PhD Fellowship.

With a swipe of a magnet, microscopic “magno-bots” perform complex maneuvers

Jennifer Chu | MIT News — Tue, 28 Apr 2026 11:00:00 -0400

Under a microscope, a bouquet of lollipop-like structures, each smaller than a grain of sand, waves gently in a petri dish of liquid. Suddenly, they snap together, like the jaws of a Venus flytrap, as a scientist waves a small magnet over the dish. What was previously an assemblage of tiny passive structures has transformed instantly into an active robotic gripper.

The lollipop gripper is one demonstration of a new type of soft magnetic hydrogel developed by engineers at MIT and their collaborators at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and the University of Cincinnati. In a study appearing today in the journal Matter, the MIT team reports on a new method to print and fabricate the gel, which can be made into complex, magnetically activated three-dimensional structures.

The new gel could be the basis for soft, microscopic, magnetically responsive robots and materials. Such magno-bots could be used in medicine, for instance to release drugs or grab biopsies when directed by an external magnet.

Making objects move with magnets is nothing new, at least at the macroscale. We can, for example, wave a refrigerator magnet over a pile of paper clips that will trail the magnet in response. And at the microscale, scientists have designed a variety of magnetic “micro-swimmers” — components that are smaller than a millimeter and can be directed remotely by a magnet to squeeze through small spaces. For the most part, these designs work by mixing magnetic particles into a printable resin and pulling the entire swimmer in the direction of an external magnet.

In contrast, the MIT team’s new material can be made into even more complex and deformable structures with micron-scale precision. These features could enable a magnetic millibot to move individual features and perform more complex maneuvers.

“We can now make a soft, intricate 3D architecture with components that can move and deform in complex ways within the same microscopic structure,” says study author Carlos Portela, the Robert N. Noyce Career Development Associate Professor of Mechanical Engineering at MIT. “For soft microscopic robotics, or stimuli-responsive matter, that could be a game-changing capability.”

The study’s MIT co-authors include graduate students Rachel Sun and Andrew Chen, along with Yiming Ji and Daryl Yee of EPFL and Eric Stewart of the University of Cincinnati.

In a flash

At MIT, Portela’s group develops new metamaterials — materials engineered with unique, microscopic architectures that give rise to beyond-normal material properties. Portela has fabricated a variety of such metamaterials, including extremely tough and stretchy architectures and designs that can manipulate sound and withstand violent impacts.

Most recently, he’s expanded his research to “programmable” materials, which can be engineered to change their properties in response to stimuli, such as certain chemicals, light, and electric and magnetic fields.

From the team’s perspective, magnetic stimuli stand out from the rest.

“With a magnetically responsive material, we have control at a distance and the response is instantaneous,” says co-lead author Andrew Chen. “We don’t have to wait for a slow chemical reaction or physical process, and we can manipulate the material without touching it.”

For the new study, the team aimed to create a magnetically responsive metamaterial that can be made into structures smaller than a millimeter. Researchers typically fabricate microstructures by using two-photon lithography — a high-resolution 3D printing technique that flashes a laser into a small pool of resin. With repeated flashes, the laser traces a microscopic pattern into the resin, which solidifies into the same pattern, ultimately creating a tiny, three-dimensional structure, layer by layer.

While 3D resin printing produces intricate microstructures, using the same process to print magnetic structures has been a challenge. Researchers have tried to combine the resin with magnetic nanoparticles before printing the mixture. But magnetic particles are essentially bits of metal that inherently scatter light away or agglomerate and sediment unintentionally. Scientists have found that any magnetic particles in the resin can reduce the laser’s power at a given spot and weaken the resulting structure or prevent its printing altogether.

“Directly 3D printing deformable micron-scale structures with a high fraction of magnetic particles is extremely difficult, often involving a tradeoff between magnetic functionality and structural integrity,” says Sun, a co-lead author on the work.

A printed double-dip

The researchers created a new way to fabricate magnetic microstructures, by combining 3D resin printing with a double-dip process. The researchers first applied conventional resin printing to create a microstructure using a typical polymer gel, with no added magnetic particles. Then they dipped the printed gel into a solution containing iron ions, which the gel can absorb. The iron-soaked structure is then dipped again in a second solution of hydroxide ions. The iron ions in the gel bond with the hydroxide ions, creating iron-oxide nanoparticles that are inherently magnetic.

With this new process, the team can print intricate structures smaller than a millimeter, and add magnetic properties to the structures after printing. What’s more, they are able to control how magnetic a structure’s individual features can be. They found that, by tuning the laser’s power as they print certain features, they can set how cross-linked, or “tight” the gel is when printed. The tighter the gel, the fewer magnetic particles it can form. In this way, the researchers can determine how magnetic each tiny feature can be.

“This provides unprecedented design freedom to print multifunctional structures and materials at the microscale,” Sun says.

As a demonstration, the team fabricated ball-and-stick structures resembling tiny lollipops. The structures were less than a millimeter in height, with balls that were smaller than a grain of sand. The researchers printed the lollipops out of polymer gel and infused each ball with different amounts of magnetic particles, giving them various degrees of magnetism. Under a microscope, they observed that when they passed an ordinary refrigerator magnet over the structures, the lollipops pulled toward the magnet in various degrees, in a configuration that mimicked gripping fingers.

“You could imagine a magnetic architecture like this could act as a small robot that you could guide through the body with an external magnet, and it could latch onto something, for instance to take a biopsy,” Portela says. “That is a vision that others can take from this work.”

The team also fabricated a magnetically responsive, “bistable” switch. They first printed a small millimeter-long rectangle of polymer gel and attached to either side four tiny, oar-like magnetic structures. Each oar measured about 8 microns thick — about the size of a red blood cell. When the team applied a magnet on one end of the rectangle, the oars flipped toward the magnet, pulling the rectangle in the same direction and locking it in that position. When the magnet was applied to the other side, the oars flipped again, pulling the rectangle, like a switch, in the opposite direction.

“We think this is a new kind of bistable mechanism that could be used, for instance, in a microfluidic device, as a magnetic valve to open or shut some flow,” Portela says. “For now, we’ve figured out how to fabricate magnetic complex architectures at the microscale and also spatially tune their properties. That opens up a lot of interesting ideas for soft miniature robots going forward.”

This research was supported, in part, by the National Science Foundation and the MathWorks seed grant program.

This work was performed, in part, in the MIT.nano fabrication and characterization facilities.

Robotically assembled building blocks could make construction more efficient and sustainable

Adam Zewe | MIT News — Tue, 28 Apr 2026 00:00:00 -0400

Robotically assembled building blocks could be a more environmentally friendly method for erecting large-scale structures than some existing construction techniques, according to a new study by MIT researchers.

The team conducted a feasibility study to evaluate the efficiency of constructing a simple building using “voxels,” which are modular 3D subunits that assemble into complex, durable structures.

After studying the performance of multiple voxels, the researchers developed three new designs intended to streamline building construction. They also produced a robotic assembler and a user-friendly interface for generating voxel-based building layouts and feeding instructions to the robots.

Their results indicate this voxel-based robotic assembly system could reduce embodied carbon — all of the carbon emitted during the lifecycle of building materials — by as much as 82 percent, compared with popular techniques like 3D concrete printing, precast modular concrete, and steel framing. The system would also be competitive in terms of cost and construction time. However, the choice of materials used to manufacture the voxels does play a major role in their carbon footprint and cost.

While scalability, durability, long-term robustness, and important considerations like fire resistance remain to be explored before such a system could be widely deployed, the researchers say these initial results highlight the potential of this approach for automated, on-site construction.

“I’m particularly excited about how the robotic assembly of discrete lattices can enable a practical way to apply digital fabrication to the built environment in a way that can let us build much more efficiently and sustainably,” says Miana Smith, a graduate student in the Center for Bits and Atoms (CBA) at MIT and lead author the study.

She is joined on the paper by Paul Richard, a graduate student at École Polytechnique Fédérale de Lausanne in Switzerland and former visiting researcher at MIT; Alfonso Parra Rubio, a CBA graduate student; and senior author Neil Gershenfeld, an MIT professor and the director of the CBA. The research appears in Automation in Construction.

Designing better building blocks

Over the past several years, researchers in the Center for Bits and Atoms have been developing voxels, which are lattice-structured building blocks that can be assembled into objects with high strength and stiffness, like airplane wings, wind turbine blades, and space structures.

“Here, we are taking aerospace principles and applying them to buildings. Why don’t we make buildings as efficiently as we make airplanes?” Gershenfeld says, based on prior work his lab has done on voxel assembly with NASA, Airbus, and Boeing.

To explore the feasibility of voxel-based assembly strategies for buildings, the researchers first evaluated the mechanical performance and sustainability of eight existing voxel designs, including a cuboctahedron made from glass-reinforced nylon and a Kelvin lattice made from steel.

Based on those evaluations, they developed a set of three voxels using a new geometry that could be more easily assembled robotically into a larger structure. The new design, based on a high-strength and high-stiffness octet lattice, mechanically self-aligns into rigid structures.

“The interlocking nature of these voxels means we can get nice mechanical properties without needing to have a lot of connectors in the system, so the construction process can run a lot faster,” Smith says.

To accelerate construction, they designed a robotic assembly system based on inchworm-like robots that crawl across a voxel structure by anchoring and extending their bodies. These Modular Inchworm Lattice Assembler robots, or MILAbots, use grippers on each end to place voxel building blocks and engage the snap-fit connections.

“The robots can assemble the voxels by dropping them into place and then stepping on them to have the pieces interlock. We can do precise maneuvers based on the mechanical relationship between the robots and the voxels,” Smith explains.

The team studied the embodied carbon needed to fabricate their new voxel designs using three materials: plastic, plywood, and steel. Then they evaluated the throughput and cost of using the robotic assembly system to build a simple, one-story building. The researchers compared these estimates with the performance of other construction methods.

Potential environmental benefits

They found that most existing voxels, and especially those made from plastics, performed poorly compared to existing methods in terms of sustainability, but the steel and wood voxels they designed offered significant environmental benefits.

For instance, utilizing their steel voxels would generate only 36 percent of the embodied carbon required for 3D concrete printing and 52 percent of the embodied carbon of precast concrete. The plywood voxels had the lowest carbon footprint, requiring about 17 percent and 24 percent of the embodied carbon needed, respectively.

“There is still a potential viable option for a plastics-based voxel approach, we just have to be a bit more strategic about which types of plastics, infills, and geometries we use,” Smith says.

In addition, projected on-site assembly time for the steel and wood voxel approaches averaged 99 hours, whereas existing construction methods averaged 155 hours.

These speed benefits rely on the distributed nature of voxel-based assembly. While one MILAbot working alone is far slower than existing techniques, with a team of 20 robots working in parallel, the system catches up to or surpasses existing automation methods at a lower cost.

“One benefit of this method is how incremental it is. You can start building, and if it turns out you need a new room, you can just add onto the structure. It is also reversible, so if your use changes, you can dissemble the voxels and change the structure,” Gershenfeld says.

The researchers also developed an interface that enables users to input or hand-design a voxelized structure. The automatic system determines the paths the MILAbots should follow for construction and sends commands to the assemblers.

The next step in this project will be a larger testbed in Bhutan, using the “super fab lab” that CBA helped set up there to replicate the robots to test construction for a planned sustainable city, Gershenfeld says.

Additional areas of future work include studying the stability of voxel structures under lateral loads, improving the design tool to account for the physics of the system, enhancing the MILAbots, and evaluating voxels that have integrated sheeting, insulation, or electrical and plumbing routing.

“Our work helps support why doing this type of distributed robot assembly might be a practical way to bring digital fabrication into building construction,” Smith says.

“This is yet another visionary example from Neil Gershenfeld and his team, of finding ways for buildings to build themselves with the help of tiny robotic machines. I’m now fascinated by how we can harness an idea like this to make it more affordable to make the outsides of buildings more engaging and joyful,” says Thomas Heatherwick, founder of the design and architecture firm Heatherwick Studio, who was not involved with this research.

This work was funded, in part, by the MIT Center for Bits and Atoms Consortia.

Mapping molecular markers of physical fitness

Anne Trafton | MIT News — Tue, 28 Apr 2026 00:00:00 -0400

Patterns of molecular activity in the blood may hold clues not only to how fit someone is, but also to the biological processes that support physical performance. Researchers at MIT, GE HealthCare, and the U.S. Military Academy at West Point have developed a computational model that links thousands of these molecular signals to fitness levels, revealing pathways that could inform future studies to improve fitness training and speed injury or disease recovery.

To develop their model, the researchers analyzed more than 50,000 biomarkers in 86 cadets at the U.S. Military Academy who were training for a military competition. Using these data, the researchers were able to identify molecular pathways that appear to contribute to higher levels of physical fitness.

“We had 50,000 measurements, and we wanted to get it down to about 100 where there’s some likelihood that the markers that we’re measuring are mechanistically linked to physical fitness. So, not just a statistical correlation, of which there will be many, but markers where there’s a likelihood that there is a causal relationship,” says Ernest Fraenkel, the Grover M. Hermann Professor in Health Sciences and Technology in MIT’s Department of Biological Engineering.

These biomarkers can be measured by analyzing blood samples, which could offer a simple way to provide an athlete, for example, or perhaps someone with chronic illness or a long-term injury, with additional information about potential areas to focus their efforts to reduce risk of injury, accelerate recovery, or improve their performance ceiling beyond what conventional measures show.

Azar Alizadeh, a principal scientist with GE HealthCare’s Healthcare Technology and Innovation Center, is the paper’s lead author. Fraenkel and Luca Marinelli, a senior principal scientist with GE HealthCare, are the senior authors of the new study, which appears in the journal Communications Biology.

Mapping fitness

To find the genetic basis of a simple trait such as height, scientists can perform large-scale studies known as genome-wide association studies (GWAS), in which genetic markers from thousands of people can be linked with height. However, the picture becomes much more complicated for traits such as physical fitness, which is determined by the interplay of many different genetic, physiological, and environmental factors.

The researchers set out to try to identify some of those factors, working with a group of 86 volunteers at the U.S. Military Academy at West Point who were training for the Sandhurst Military Skills Competition. Alizadeh led the experimental study design and execution, in collaboration with GE HealthCare, GE Research, West Point, and MIT scientists. During the three-month study period, volunteers participated in up to five sessions. At each session, blood samples were taken before and after intense exercise. The researchers also measured other traits such as lean muscle mass and VO₂ max (the maximum rate of oxygen consumption during exercise).

From the blood samples, the researchers were able to measure more than 50,000 biomarkers, which they obtained by analyzing DNA methylation patterns, sequencing messenger RNA transcripts, and analyzing thousands of the proteins and small molecules found in the samples.

From their set of 50,000 biomarkers, the researchers hoped to identify a smaller number that could predict overall physical fitness, as measured by performance on the Army Combat Fitness Test (ACFT). This test includes a 2-mile run, maximum deadlift (the heaviest weight a person can lift for a single repetition up to 340 pounds), and sprint-drag-carry, a test that involves sprinting, dragging a sled, and carrying kettlebells.

One way to do this would be to simply train a computational model to identify correlations between fitness and biomarkers. However, with only 86 subjects in the study, that approach would likely yield correlations that were random and did not actually contribute to physical fitness, Fraenkel says.

To take a more targeted approach, the researchers first created a network model that represents the interactions between the markers, based on existing databases that catalog those interactions. These connections might represent proteins that interact with each other in a signaling pathway, or a transcription factor that turns on a set of genes.

“We built a network that you can think of as a city map. You want to find the places in the city map that are lighting up — not just one light going on, but a whole bunch of houses or street lamps going on in the same neighborhood,” Fraenkel says. “We can find neighborhoods on this enormous molecular map that are active at the same time, in a way that correlates with the phenotype that we measure.”

“We built upon the network bioinformatics from the Fraenkel lab to create an end-to-end predictive modeling framework to discover biological expression circuits that drive groups of physical characteristics predictive of ACFT scores, for example, body composition or exercise physiology metrics like VO₂ max,” Marinelli says.

After feeding the measurements from the study participants into this predictive model, known as PhenoMol, the researchers were able to identify more than 100 biomarkers linked to performance on the ACFT. Fitness predictions based on these biomarkers were much more accurate than those of a model that correlated biomarkers with performance on the ACFT without taking network connections into account. Additionally, PhenoMol performed similarly to a model that predicted participants’ fitness based on measurements of their VO₂ and lean muscle mass.

Cellular pathways

The researchers found that the biomarkers identified by PhenoMol clustered into several different cellular pathways. Those include pathways involved in blood coagulation and the complement cascade — a part of the immune system involved in clearing damaged cells. Those systems likely help with recovery from tissue injury and stress response during exercise, Fraenkel says.

Another prominent cluster involves molecules related to the urea cycle, which is responsible for eliminating the ammonia that results from the breakdown of proteins. The model also identified biomarkers that are linked with the function of mitochondria (the organelles that generate energy within cells).

Fraenkel now hopes to dig deeper into which markers show someone’s current fitness, and which might reveal what their potential fitness levels could be. This could help to reveal potential strengths that might not show up in traditional fitness tests, he says.

That kind of prediction could be useful not only for athletic training, but also for other people who are recovering from an injury or disease, or people experiencing the effects of aging. For example, using this approach in different populations might provide useful information for an elderly person after a stroke, since such events often require months of therapy to regain significant mobility.

“This has relevance for the military and for sports teams, but also in a lot of normal life situations where maybe someone is going through rehabilitation for some injury or disease and they’ve hit a wall,” Fraenkel says. “Or during aging, you may be able to see when somebody’s losing capacity or when they have more capacity than they’ve been able to actualize.”

Molecular markers of fitness could also be used in clinical trials to rigorously test the potential benefits of popular food supplements and fitness programs, he adds.

To make the testing process simpler, the researchers would like to narrow down their pool of biomarkers to a handful that could be easily measured from a blood sample using a single method suitable for widespread use.

The research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), which states that the views, opinions, or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the U.S. government.

Six from MIT awarded 2026 Paul and Daisy Soros Fellowships for New Americans

Julia Mongo | Office of Distinguished Fellowships — Tue, 28 Apr 2026 00:00:00 -0400

Six MIT affiliates — Denisse Córdova Carrizales SM ’26; Ria Das ’21, MNG ’22; Ronak Desai; Stacy Godfreey-Igwe ’22; Arya Rao; and Ananthan Sadagopan ’24 — have been named 2026 P.D. Soros Fellows. In addition, P.D. Soros Fellow Avinash Vadali will begin a PhD in condensed-matter physics at MIT this fall.

The fellowship provides immigrants and the children of immigrants up to $90,000 in tuition and stipend support for up to two years of graduate studies. Interested students should contact Kim Benard, associate dean of distinguished fellowships in Career Advising and Professional Development.

Denisse Córdova Carrizales

Córdova Carrizales SM '26 is a PhD student in nuclear science and engineering in the lab of Professor Mingda Li, where she completed her master's work earlier this year. She is working on synthesizing and characterizing quantum materials with the goal of bridging fundamental science and industry to make our technology more energy-efficient and sustainable.

Córdova Carrizales, who is of Mexican descent, grew up in Houston, Texas, before attending Harvard University, where she graduated in 2023 with a BA in physics. At Harvard, she dove into experimental condensed-matter research. She also conducted research with the Princeton Plasma Physics Laboratory, Commonwealth Fusion Systems, and VEIR, spanning computational plasma physics and high-temperature superconducting magnet and cable engineering.

Her work includes coauthored papers in Nature Physics, Nature Materials, and Advanced Materials, as well as lead-author publications in Nano Letters and Physical Review Materials. In 2023, she received the LeRoy Apker Award from the American Physical Society.

Beyond research, Córdova Carrizales has advocated in Congress for nuclear disarmament and risk reduction and has written a piece on the nuclear stockpile stewardship program. At Harvard, she founded an organization to support first-generation college students studying physics. In a completely different arena, she performed as the lead in an off-Broadway show in New York.

Ria Das

Das ’21, MNG ’22 is a PhD student in the MIT Department of Electrical Engineering and Computer Science. She graduated from MIT in 2021 with a BS dual degree in mathematics along with electrical engineering and computer science, and received her master of engineering degree in 2022.

The daughter of Indian immigrant parents, Das grew up in Nashua, New Hampshire, where she struggled with issues of belonging and identity. These questions came to the forefront during her PhD studies at Stanford University. Das decided to step off the academic treadmill by taking a leave from her PhD to think more deeply about these topics.

During her leave, she traveled around the country before moving to New York to work at Basis Research Institute, an AI research nonprofit. As a research associate, Das developed an urban data team that worked with federal and municipal government agencies on issues of economic and housing equity, blending her interests in science and social problems. She then returned to MIT to complete her doctoral studies.

Today, Das works with Professor Joshua Tenenbaum in the Department of Brain and Cognitive Sciences to study how people undergo conceptual change to build more robust, accessible systems for automated (social) science and improved educational design. Looking ahead, she hopes to become a professor, collaborating closely with policy practitioners.

Ronak Desai

Desai is currently a student in the Harvard/MIT MD-PhD program, where his PhD focuses on chemistry. The son of immigrants from Gujarat, India, Desai was born in Tyler, Texas, and grew up in nearby Lindale. He earned his undergraduate degree at the University of Texas at Austin.

Desai spent a semester interning at the U.S. House of Representatives as a Bill Archer Fellow. He also completed biomedical research focused on studying and engineering novel polyketide synthases, aspiring to produce next-generation antibiotics by harnessing such newly engineered synthases.

Desai graduated with degrees in chemistry and biochemistry as a first-generation college student, Health Science Scholar, and Dean’s Honored Graduate, receiving nine scholarships throughout college. His research has resulted in publications in journals such as Cell and Nature Communications.

Desai hopes to combine his passions for medicine, science, and public policy in his career to advance the treatment of infectious diseases. He is conducting his doctoral research under Professor James J. Collins in the MIT Department of Biological Engineering and the Harvard-MIT Program in Health Sciences and Technology. Desai’s research centers on using artificial intelligence to discover and design novel antibiotics, an opportunity to advance treatments for patients worldwide.

Stacy Godfreey-Igwe

Godfreey-Igwe ’22 attended MIT as a QuestBridge and Gates Scholar, graduating in 2022 with a BS in mechanical engineering and a concentration in sustainable design. A Burchard Scholar, she also became the first student at MIT to complete a major in African and African diaspora studies. After graduating, she pursued a science policy fellowship in Washington and interned at the U.S. Department of Energy’s Building Technologies Office, where she worked to broaden adoption of heat pump technologies across diverse stakeholders.

Growing up in Richardson, Texas, as the daughter of Nigerian immigrants, Godfreey-Igwe developed an early awareness of structural inequality, particularly in how families like hers managed the burden of the severe Texas heat and high electricity costs. These experiences formed the basis of her lifelong journey seeking to address systemic inequities embedded in everyday systems.

Godfreey-Igwe is currently a doctoral student in the joint engineering and public policy - civil and environmental engineering program at Carnegie Mellon University (CMU), where she was selected for the inaugural CMU Rales Fellowship cohort. At CMU, she studies the impact of extreme heat on household energy use, particularly in vulnerable communities.

Beyond her research, Godfreey-Igwe organizes outreach and programming for local underrepresented students in STEM and participates in institutional efforts to expand access and belonging among graduate students. She aims to be a scholar and advocate whose work, drawing on her personal experiences, informs equitable energy solutions in a warming world.

Arya Rao

Rao is a student in the Harvard/MIT MD-PhD program. She completed her undergraduate degrees in biochemistry and computer science at Columbia University. Working with professors Pardis Sabeti (Harvard University) and Sangeeta Bhatia (MIT), Rao uses evolution as a lens for therapeutic design, developing artificial intelligence methods that read the genetic record and guide new intervention strategies.

Leveraging her dual training in medicine and computer science, Rao also leads the MESH AI Research Group at Mass General Brigham, where she develops simulation-based tools that test clinical AI systems in realistic educational settings before they reach patients.

Rao has been recognized for her work with a Forbes 30 Under 30 honor, the Massachusetts Medical Society Information Technology Award, the Harvard Presidential Public Service Fellowship, a Harvard Medical School Dean’s Innovation Award, and a Ladders to Cures Accelerator Award. She has published more than 30 manuscripts in publications including JAMA, Nature, and NEJM AI.

Growing up in rural northern Michigan, Rao was inspired by her parents, Konkani immigrants from India, who served as two of the area’s only physicians. She has always imagined a career that could leverage scientific innovation to improve patient care, especially for communities without access like her own. Going forward, she envisions a career as a surgeon-scientist that keeps her close to patients while taking on leadership that shapes how new technologies are evaluated, implemented, and made usable in the places that need them most.

Ananthan Sadagopan

Sadagopan ’24 grew up in Westborough, Massachusetts, as the child of immigrants from Chennai, India. He participated in chemistry competitions, winning the You Be the Chemist Challenge in middle school and earning a gold medal at the International Chemistry Olympiad for the United States in high school. He attended MIT for college, graduating in three years in 2024 with a bachelor’s degree in chemistry and biology.

At MIT, Sadagopan worked with Srinivas Viswanathan on computational biology projects and with William Gibson, Matthew Meyerson, and Stuart Schreiber on chemical biology projects. He led projects characterizing somatic perturbations of X chromosome inactivation in cancer, developing a machine-learning tool for cancer dependency prediction, using small molecules to relocalize proteins in cells, and creating a generalizable strategy to drug the most mutated gene in cancer, TP53. Sadagopan’s work has been patented and published in journals such as Cell and Nature Chemical Biology.

Sadogopan was president of the chemistry undergraduate association and led the events committee for MIT Science Olympiad. He is currently pursuing a PhD in biological and biomedical science at Harvard University as a Hertz Fellow and Herchel Smith Fellow. He is interested in de-risking new therapeutic strategies and hopes that his work will inspire pharma companies to bring first-in-class therapies to patients.

Self-organizing “pencil beam” laser could help scientists design brain-targeted therapies

Adam Zewe | MIT News — Mon, 27 Apr 2026 05:00:00 -0400

MIT researchers discovered a paradoxical phenomenon in optical physics that could enable a new bioimaging method that’s faster and higher-resolution than existing technology.

They discovered that, under the right conditions, a chaotic mess of laser light can spontaneously self-organize into a highly focused “pencil beam.”

Using this self-organized pencil beam, the researchers captured 3D images of the human blood-brain barrier 25 times faster than the gold-standard method, while maintaining comparable resolution.

By showing individual cells absorbing drugs in real-time, this technology could help scientists test whether new drugs for neurodegenerative disease like Alzheimer’s or ALS reach their targets in the brain, with greater speed and resolution.

“The common belief in the field is that if you crank up the power in this type of laser, the light will inevitably become chaotic. But we proved that this is not the case. We followed the evidence, embraced the uncertainty, and found a way to let the light organize itself into a novel solution for bioimaging,” says Sixian You, assistant professor in the MIT Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory for Electronics, and senior author of a paper on this imaging technique.

She is joined on the paper by lead author Honghao Cao, an EECS graduate student; EECS graduate students Li-Yu Yu and Kunzan Liu; postdocs Sarah Spitz, Francesca Michela Pramotton, and Federico Presutti; Zhengyu Zhang PhD ’24; Subhash Kulkarni, an assistant professor at Harvard University and the Beth Israel Deaconess Medical Center; and Roger Kamm, the Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering at MIT. The paper appears today in Nature Methods.

A surprising finding

The discovery began with an observation that initially puzzled the researchers.

The team previously developed a precise fiber shaper, a device that enables them to carefully tune the laser light shining through a multimode optical fiber. This type of optical fiber can carry a significant amount of power.

Cao was pushing the multimode fiber toward its limit to see how much power it could take.

Typically, the more power one pumps into the laser, the more disordered and scattered the beam of light becomes due to imperfections in the fiber.

But Cao observed that, as he increased the power almost to the point where it would burn the fiber, the light did the opposite of what was expected: It collapsed into a single, needle-sharp beam.

“Disorder is intrinsic to these fibers. The light engineering you typically need to do to overcome that disorder, especially at high power, is a longstanding hassle. But with this self-organization, you can get a stable, ultrafast pencil beam without the need for custom beam-shaping components,” You says.

To replicate this phenomenon, the researchers found they had to satisfy two simple, but precise conditions.

First, the laser must enter the fiber at a perfect, zero-degree angle. This is a more rigorous requirement than is usually used for these types of fibers. Second, the power must be dialed up until the light begins to interact with the glass of the fiber itself.

“At this critical power, the nonlinearity can counter the intrinsic disorder, creating a balance that transforms the input beam into a self-organized pencil beam,” Cao explains.

Typically, researchers conduct these experiments at much lower power levels for fear of destroying the fiber, in which case they wouldn’t see this self-organization. In addition, such precise on-axis alignment isn’t typically necessary since a multimode fiber can carry so much power.

But taken together, these two techniques can generate a stable pencil-beam without any complicated light engineering methods.

“That is the charm of this method — you could do this with a normal, optical setup and without much domain expertise,” You says.

A better beam

When the researchers performed characterization experiments of this pencil beam, it was more stable and high-resolution than many similar beams. Other beams often suffer from “sidelobes” — blurry halos of light that can distort images.

Their beam was more pristine and tightly focused.

Building on those experiments, the researchers demonstrated the use of this pencil-beam in biomedical imaging of the human blood-brain barrier.

This barrier is a tightly packed layer of cells that protects the brain from toxins, but it also blocks many medicines. Scientists and clinicians often want to see how drugs flow inside the vasculature of the blood-brain barrier and whether they reach their targets within the brain.

But with standard optical settings, the best one can do is capture one 2D section of the vasculature at a time, and then repeat the process multiple times to generate a fuller image, You explains.

Using this new technique, the researchers created an ultrafast, high-precision pencil beam that enabled them to dynamically track how cells absorb proteins in real-time.

“The pharmaceutical industry is especially interested in using human-based models to screen for drugs that effectively cross the barrier, as animal models often fail to predict what happens in humans. That this new method doesn’t require the cells to have a fluorescent tag is a game-changer. For the first time, we can now visualize the time-dependent entry of drugs into the brain and even identify the rate at which specific cell types internalize the drug,” says Kamm.

“Importantly, however, this approach is not limited to the blood-brain barrier but enables time-resolved tracking of diverse compounds and molecular targets across engineered tissue models, providing a powerful tool for biological engineering,” Spitz adds.

The team captured cellular-level 3D images that were higher quality than with other methods, and generated these images about 25 times faster.

“Usually, you have a tradeoff between image resolution and depth of focus — you can only probe so far at a time. But with our method, we can overcome this tradeoff by creating a pencil-beam with both high resolution and a large depth of focus,” You says.

In the future, the researchers want to better understand the fundamental physics of the pencil-beam and the mechanisms behind its self-organization. They also plan to apply the technique to other scenarios, such as imaging neurons in the brain, and work toward commercializing the technology.

“You’s group realized this beam that concentrates energy in time and space could be valuable for microscopy techniques that depend on the intensity of the light that illuminates the sample. They demonstrated just that and found advantages over ordinary laser beams for imaging. It will be scientifically interesting to fully understand the creation of the new pencil beams, which could find use in a variety of imaging applications,” says Frank Wise, the Samuel B. Eckert Professor of Engineering Emeritus at Cornell University, who was not involved with this work.

The work was supported by MIT startup funds, Novo Nordisk Research Development, a National Science Foundation (NSF) CAREER Award, CZI Dynamic Imaging from the Chan Zuckerberg donor-advised fund through the Silicon Valley Community Foundation, the Manton Foundation, and the Fairbairn Menstruation Science Fund.

A faster way to estimate AI power consumption

Adam Zewe | MIT News — Mon, 27 Apr 2026 00:00:00 -0400

Due to the explosive growth of artificial intelligence, it is estimated that data centers will consume up to 12 percent of total U.S. electricity by 2028, according to the Lawrence Berkeley National Laboratory. Improving data center energy efficiency is one way scientists are striving to make AI more sustainable.

Toward that goal, researchers from MIT and the MIT-IBM Watson AI Lab developed a rapid prediction tool that tells data center operators how much power will be consumed by running a particular AI workload on a certain processor or AI accelerator chip.

Their method produces reliable power estimates in a few seconds, unlike traditional modeling techniques that can take hours or even days to yield results. Moreover, their prediction tool can be applied to a wide range of hardware configurations — even emerging designs that haven’t been deployed yet.

Data center operators could use these estimates to effectively allocate limited resources across multiple AI models and processors, improving energy efficiency. In addition, this tool could allow algorithm developers and model providers to assess potential energy consumption of a new model before they deploy it.

“The AI sustainability challenge is a pressing question we have to answer. Because our estimation method is fast, convenient, and provides direct feedback, we hope it makes algorithm developers and data center operators more likely to think about reducing energy consumption,” says Kyungmi Lee, an MIT postdoc and lead author of a paper on this technique.

She is joined on the paper by Zhiye Song, an electrical engineering and computer science (EECS) graduate student; Eun Kyung Lee and Xin Zhang, research managers at IBM Research and the MIT-IBM Watson AI Lab; Tamar Eilam, IBM Fellow, chief scientist of sustainable computing at IBM Research, and a member of the MIT-IBM Watson AI Lab; and senior author Anantha P. Chandrakasan, MIT provost, Vannevar Bush Professor of Electrical Engineering and Computer Science, and a member of the MIT-IBM Watson AI Lab. The research is being presented this week at the IEEE International Symposium on Performance Analysis of Systems and Software.

Expediting energy estimation

Inside a data center, thousands of powerful graphics processing units (GPUs) perform operations to train and deploy AI models. The power consumption of a particular GPU will vary based on its configuration and the workload it is handling.

Many traditional methods used to predict energy consumption involve breaking a workload into individual steps and emulating how each module inside the GPU is being utilized one step at a time. But AI workloads like model training and data preprocessing are extremely large and can take hours or even days to simulate in this manner.

“As an operator, if I want to compare different algorithms or configurations to find the most energy-efficient manner to proceed, if a single emulation is going to take days, that is going to become very impractical,” Lee says.

To speed up the prediction process, the MIT researchers sought to use less-detailed information that could be estimated faster. They found that AI workloads often have many repeatable patterns. They could use these patterns to generate the information needed for reliable but quick power estimation.

In many cases, algorithm developers write programs to run as efficiently as possible on a GPU. For instance, they use well-structured optimizations to distribute the work across parallel processing cores and move chunks of data around in the most efficient manner.

“These optimizations that software developers use create a regular structure, and that is what we are trying to leverage,” explains Lee.

The researchers developed a lightweight estimation model, called EnergAIzer, that captures the power usage pattern of a GPU from those optimizations.

An accurate assessment

But while their estimation was fast, the researchers found that it didn’t take all energy costs into account. For instance, every time a GPU runs a program, there is a fixed energy cost required for setting up and configurating that program. Then each time the GPU runs an operation on a chunk of data, an additional energy cost must be paid.

Due to fluctuations in the hardware or conflicts in accessing or moving data, a GPU might not be able to use all available bandwidth, slowing operations down and drawing more energy over time.

To include these additional costs and variances, the researchers gathered real measurements from GPUs to generate correction terms they applied to their estimation model.

“This way, we can get a fast estimation that is also very accurate,” she says.

In the end, a user can provide their workload information, like the AI model they want to run and the number and length of user inputs to process, and EnergAIzer will output an energy consumption estimation in a matter of seconds.

The user can also change the GPU configuration or adjust the operating speed to see how such design choices impact the overall power consumption.

When the researchers tested EnergAIzer using real AI workload information from actual GPUs, it could estimate the power consumption with only about 8 percent error, which is comparable to traditional methods that can take hours to produce results.

Their method could also be used to predict the power consumption of future GPUs and emerging device configurations, as long as the hardware doesn’t change drastically in a short amount of time.

In the future, the researchers want to test EnergAIzer on the newest GPU configurations and scale the model up so it can be applied to many GPUs that are collaborating to run a workload.

“To really make an impact on sustainability, we need a tool that can provide a fast energy estimation solution across the stack, for hardware designers, data center operators, and algorithm developers, so they can all be more aware of power consumption. With this tool, we’ve taken one step toward that goal,” Lee says.

This research was funded, in part, by the MIT-IBM Watson AI Lab.

The power of “and” in energy and climate entrepreneurship

Charlotte Whittle | MIT Energy Initiative — Fri, 24 Apr 2026 13:27:00 -0400

A supportive ecosystem is a cornerstone in entrepreneurship, according to Georgina Campbell Flatter, the CEO of Greentown Labs. “If we really want to be driving the most transformational technologies to scale at a speed in which we need them to happen for our planet, we need to be thinking about the ecosystem that we build around it.” During a seminar titled MITEI Presents: Advancing the Energy Transition, Campbell Flatter spoke of “the power of ‘and’” — the importance of multiple people, companies, and solutions collaborating to advance energy and climate solutions — and how that underpins Greentown Labs’ mission. “Innovation is a team sport. No one can go alone,” she said.

Creating these ecosystems is paramount at Greentown Labs, the world’s largest energy and climate incubator. “Through the lens of Greentown, we think about the power of ‘and’ through how we can work together better in the ecosystems where we have physical presence, but also how we can connect better across ecosystems,” said Campbell Flatter. The concept of "and" also exists in energy and climate, innovation and deployment, science and entrepreneurship, and competitiveness and collaboration, she said. Campbell Flatter feels this expansive lens is especially important in our increasingly polarized world.

At its core, Greentown Labs is a place to cluster innovators together. “We have to be very intentional about how we support and accelerate and help those entrepreneurs,” said Campbell Flatter. There is a science behind this “innovation infrastructure” that involves not only bringing creative minds together, but also removing friction so startups can move faster. The majority of this friction exists in the gaps between innovation and deployment, often referred to as the “valleys of death.” The first valley of death happens between idea and prototype; the second valley of death happens between prototype and the first commercial pilot. Greentown often asks where their ecosystems can be most helpful, which has led them to focus on helping entrepreneurs bridge that second valley, according to Campbell Flatter.

“Entrepreneurs at the stage where they can’t quite afford space on their own, and maybe it takes six to 12 months to figure out the permitting anyway, come to Greentown,” said Campbell Flatter. “We’re actively thinking about the customers, the capital, the infrastructure needs that you have in order for you to move your way through this second valley.”

Part of Greentown’s decision to focus on the second valley came from MIT’s unique ability to bring innovators across the first valley of death — an ability that Campbell Flatter deemed “truly world class.” Referencing startups born from universities like MIT and Harvard, Campbell Flatter said “they're far more likely to be successful and scale because of the ecosystem they’re surrounded in. You’re getting feedback constantly from your peers, you’re getting support and mentorship — that all matters for the ecosystem.”

MIT also helps build this ecosystem by attracting innovators to the area. “Thirty percent of our entrepreneurs at Greentown are coming from out of state and moving to Massachusetts,” she said. “One, because Greentown’s a great home for them, but two, because of MIT and the talent that they can source from the ecosystem, which they are well aware of, and the knowledge, IP [intellectual property], and credibility.”

Not only is the symbiotic relationship between MIT and Greentown a powerful entrepreneurial ecosystem, but MIT has also been instrumental in Campbell Flatter’s own journey toward her current body of work. After completing her master’s degree in materials science at Oxford University, she graduated from the MIT Technology and Policy Program. Campbell Flatter credited her time as a graduate student at MIT for giving her an appreciation for how hard it is to commercialize technology, and for the importance of ecosystems, and for giving her an early sense of how energy and climate would define this century. “I think it is really important to recognize the intentionality behind MIT’s commitment to energy and climate,” said Campbell Flatter.

While at MIT, she ran the third iteration of the MIT Clean Energy Prize, advocating for the inclusion of a non-renewables chapter of the prize because she saw “how important it was to continue to decarbonize and bring efficiencies to the traditional energy sectors while we work on all these amazing new energy initiatives.” Greentown has put this into practice through their wide network of industry partners.

“I guess this early lesson I took from MIT was this idea that we must embrace the power of ‘and,’” said Campbell Flatter. “It slows innovation down when we don’t embrace and work together.”

This speaker series highlights energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. Visit the MIT Energy Initiative's events page for more information on this and additional events.

MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone

Rachel Gordon | MIT CSAIL — Fri, 24 Apr 2026 13:00:00 -0400

Every year, the countries competing in the International Mathematical Olympiad (IMO) arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. No one had ever collected them systematically, cleaned them, and made them available, not for AI researchers testing the limits of mathematical reasoning, and not for the students around the world training for these competitions largely on their own.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), King Abdullah University of Science and Technology (KAUST), and the company HUMAIN have now done exactly that.

MathNet is the largest high-quality dataset of proof-based math problems ever created. Comprising more than 30,000 expert-authored problems and solutions spanning 47 countries, 17 languages, and 143 competitions, it is five times larger than the next-biggest dataset of its kind. The work will be presented at the International Conference on Learning Representations (ICLR) in Brazil later this month.

What makes MathNet different is not only its size, but its breadth. Previous Olympiad-level datasets draw almost exclusively from competitions in the United States and China. MathNet spans dozens of countries across six continents, covers 17 languages, includes both text- and image-based problems and solutions, and spans four decades of competition mathematics. The goal is to capture the full range of mathematical perspectives and problem-solving traditions that exist across the global math community, not just the most visible ones.

"Every country brings a booklet of its most novel and most creative problems," says Shaden Alshammari, an MIT PhD student and lead author on the paper. "They share the booklets with each other, but no one had made the effort to collect them, clean them, and upload them online."

Building MathNet required tracking down 1,595 PDF volumes totaling more than 25,000 pages, spanning digital documents and decades-old scans in more than a dozen languages. A significant portion of that archive came from an unlikely source: Navid Safaei, a longtime IMO community figure and co-author who had been collecting and scanning those booklets by hand since 2006. His personal archive formed much of the backbone of the dataset.

The sourcing matters as much as the scale. Where most existing math datasets pull problems from community forums like Art of Problem Solving (AoPS), MathNet draws exclusively from official national competition booklets. The solutions in those booklets are expert-written and peer-reviewed, and they often run to multiple pages, with authors walking through several approaches to the same problem. That depth gives AI models a far richer signal for learning mathematical reasoning than the shorter, informal solutions typical of community-sourced datasets. It also means the dataset is genuinely useful for students: Anyone preparing for the IMO or a national competition now has access to a centralized, searchable collection of high-quality problems and worked solutions from traditions around the world.

"I remember so many students for whom it was an individual effort. No one in their country was training them for this kind of competition," says Alshammari, who competed in the IMO as a student herself. "We hope this gives them a centralized place with high-quality problems and solutions to learn from."

The team has deep roots in the IMO community. Sultan Albarakati, a co-author, currently serves on the IMO board, and the researchers are working to share the dataset with the IMO foundation directly. To validate the dataset, they assembled a grading group of more than 30 human evaluators from countries including Armenia, Russia, Ukraine, Vietnam, and Poland, who coordinated together to verify thousands of solutions.

"The MathNet database has the potential to be an excellent resource for both students and leaders seeking new problems to work on or looking for the solution to a difficult question," says Tanish Patil, deputy leader of Switzerland's IMO. "Whilst other archives of Olympiad problems do exist (notably, the Contest Collections forums on AoPS), these resources lack standardized formatting system, verified solutions, and important problem metadata that topics and theory require. It will also be interesting to see how this dataset is used to improve the performance of reasoning models, and if we will soon be able to reliably answer an important issue when creating novel Olympiad questions: determining if a problem is truly original."

MathNet also functions as a rigorous benchmark for AI performance, and the results reveal a more complicated picture than recent headlines about AI math prowess might suggest. Frontier models have made extraordinary progress: Some have reportedly achieved gold-medal performance at the IMO, and on standard benchmarks they now solve problems that would stump most humans. But MathNet shows that progress is uneven. Even GPT-5, the top-performing model tested, averaged around 69.3 percent on MathNet's main benchmark of 6,400 problems, failing nearly one-in-three Olympiad-level problems. And when problems include figures, performance drops significantly across the board, exposing visual reasoning as a consistent weak point for even the most capable models.

Several open-source models scored 0 percent on Mongolian-language problems, highlighting another dimension where current AI systems fall short despite their overall strength.

"GPT models are equally good in English and other languages," Alshammari says. "But many of the open-source models fail completely at less-common languages, such as Mongolian."

The diversity of MathNet is also designed to address a deeper limitation in how AI models learn mathematics. When training data skews toward English and Chinese problems, models absorb a narrow slice of mathematical culture. A Romanian combinatorics problem or a Brazilian number theory problem may approach the same underlying concept from a completely different angle. Exposure to that range, the researchers argue, makes both humans and AI systems better mathematical thinkers.

Beyond problem-solving, MathNet introduces a retrieval benchmark that asks whether models can recognize when two problems share the same underlying mathematical structure, a capability that matters both for AI development and for the math community itself. Near-duplicate problems have appeared in real IMO exams over the years because finding mathematical equivalences across different notations, languages, and formats is genuinely hard, even for expert human committees. Testing eight state-of-the-art embedding models, the researchers found that even the strongest identified the correct match only about 5 percent of the time on the first try, with models frequently ranking structurally unrelated problems as more similar than equivalent ones.

The dataset also includes a retrieval-augmented generation benchmark, testing whether giving a model a structurally related problem before asking it to solve a new one improves performance. It does, but only when the retrieved problem is genuinely relevant. DeepSeek-V3.2-Speciale gained up to 12 percentage points with well-matched retrieval, while irrelevant retrieval degraded performance in roughly 22 percent of cases.

Alshammari wrote the paper with Safaei, HUMAIN AI engineer Abrar Zainal, KAUST Academy Director Sultan Albarakati, and MIT CSAIL colleagues: master's student Kevin Wen SB ’25; Microsoft Principal Engineering Manager Mark Hamilton SM ’22, PhD ‘25; and professors William Freeman and Antonio Torralba. Their work was funded, in part, by the Schwarzman College of Computing Fellowship and the National Science Foundation.

MathNet is publicly available at mathnet.csail.mit.edu.

Faces of MIT: Gabi Hott Soares

Katy Dandurand | MIT Human Resources — Fri, 24 Apr 2026 12:00:00 -0400

Gabi Hott Soares, associate director of student organizations and programming for the Student Organizations, Leadership, and Engagement Office (SOLE) in the Division of Student Life (DSL), empowers and equips students to lead and serve not only during their time at MIT, but also as they venture into their professional lives. With enthusiasm and a global mindset, she is dedicated to helping students thrive and reach their goals.

Hott Soares was working in Brazil in corporate communication and social responsibility for heavy‑industry companies, including metals, mining, steel, and oil and gas, when she moved to the United States in 2017 to attend the Hult International Business School in Cambridge. After graduating, she hoped to fulfill her dream of working in the United States, and initially planned to continue in the same industry. Once she arrived in Boston, however, she saw the potential of working in higher education and identified it as a field she wanted to pursue. The challenge, Hott Soares noted, was that as an international professional, she did not have anyone stateside who could recommend her.

Taking matters into her own hands, Hott Soares began attending meetups of Brazilian students and researchers in the Boston area to make connections. At one, she met an MIT student who invited her to volunteer as a marketing chair for his startup. Hott Soares worked with the startup for three months when she met another member of the team — the girlfriend of an MIT student — who mentioned that she was leaving a part‑time position within the MIT Spouses and Partners Connect (MS&PC) program. She asked Hott Soares if she would be interested in the role, and Hott Soares jumped at the opportunity to work at the Institute.

In her first position at MIT, Hott Soares worked directly with Aaron Donaghey, manager of event scheduling and special projects in the Campus Activities Complex (CAC), in a temporary office assistant position supporting CAC and SOLE. Located on the fifth floor of the Stratton Student Center, she greeted students and provided resources related to both offices. Intent on learning as much as she could about how both offices operated, she dedicated time to familiarizing herself with their functions, which was no small task. CAC, for example, manages several event spaces, including Kresge Auditorium and the MIT Chapel, and oversees thousands of events each year. Meanwhile, SOLE advises hundreds of student organizations recognized by the Association of Student Activities.

Six months later, when Hott Soares told Donaghey about her background and hope for a career at MIT, he encouraged her to apply to be the event support assistant within CAC. She was selected for the role, marking her first permanent role at MIT. On her path to continued growth at the Institute, and confident that new opportunities would come, she took advantage of the Institute’s career planning and development resources offered to employees. She worked one-on-one with Michele King Harrington, career development program administrator in human resources, and attended her workshops. King Harrington encouraged her to stay open to emerging opportunities, and in turn, Hott Soares immersed herself in learning everything she could about the Institute.

In 2021, she was promoted to senior administrative assistant for what is now known as Student Engagement and Campus Activities within DSL. A year later, she became assistant director of student organizations and programming in SOLE. In 2023, she was again promoted to associate director of student organizations and programming and received a DSL Infinite Mile Award in the category “Here for the Students.”

In her current role, Hott Soares leads the student events and programming boards area, which includes the Class Councils, Ring Committee, Senior Ball and Week Committees, and the Student Events Board. She interfaces daily with the student groups, helping them build community and plan activities and programs both on and off campus. While the skills she teaches students are applicable for their task at hand, they are also life skills that students will carry with them long after their time at MIT.

Serving people and nurturing the MIT community are what Hott Soares enjoys most. She reminds students that amid a rigorous course load and demanding commitments, it’s important to have fun — especially when they are celebrating an event they worked hard to plan. “Their time at MIT is one of the most beautiful times of their lives,” she says. “I want them to remember that.”

Soundbytes

Q: What part of your work makes you feel most proud?

Hott Soares: I am proud of being able to work with the most brilliant minds in the world and still be myself. When I am interacting with students, we want to help each other, and we can create a relationship that is based on empathy, respect, trust, and humility. I am grateful that I get to work with so many wonderful people.

Q: What advice would you give to a new staff member at MIT?

Hott Soares: Introduce yourself to people and take time to build relationships. Let others know what you do, what you want to do, and how you want to collaborate. Be humble, stay curious, and open to learning. MIT can feel fast-paced, but it is also a community full of people who genuinely care. You will thrive by being your true self!

Q: How would you describe the community at MIT?

Hott Soares: The people at MIT are amazing. Because I don’t have my family here, MIT is like home. The community is made up of people from different backgrounds and cultures, and I’ve always felt respected and like I belong. It is welcoming, safe, and compassionate. A shared sense of purpose, collaboration, creativity, and drive make MIT an inspiring place to work.

Three from MIT named 2026 Goldwater Scholars

School of Engineering | School of Science — Thu, 23 Apr 2026 15:00:00 -0400

Three MIT rising seniors have been selected to receive a 2026 Barry Goldwater Scholarship, including Deeksha Kumaresh in the School of Engineering and Anna Liu and Charlotte Myersin the School of Science. An estimated 5,000 college sophomores and juniors from across the United States were nominated for the scholarships, of whom only 454 were selected.

The Goldwater Scholarships have been conferred since 1989 by the Barry Goldwater Scholarship and Excellence in Education Foundation. These scholarships have supported undergraduates who go on to become leading scientists, engineers, and mathematicians in their respective fields.

Deeksha Kumaresh, a third-year biological engineering major, is an undergraduate researcher at the Hammond Lab. The Hammond Research Group at the MIT Koch Institute for Integrative Cancer Research focuses on the self-assembly of polymeric nanomaterials, with a major emphasis on the use of electrostatics and other complementary interactions to generate multifunctional materials with highly controlled architecture.

“Hands down, the mentors I’ve encountered have been the most significant part of my MIT journey,” Kumaresh says. “I’m also extremely grateful to the Hammond Lab, which has provided a supportive environment where I can make mistakes, learn, and grow as a researcher. I treasure the spontaneous conversations with lab members (about science or life) and their willingness to treat me seriously as an independent researcher, even as an undergraduate.”

Kumaresh is mentored by Paula Hammond, dean of the School of Engineering, Institute Professor, and professor of chemical engineering. Kumaresh's career goals are to pursue an MD/PhD. In the long term, she seeks to lead a bioengineering research lab to predict the efficacy and side effects of cancer therapies by developing systems-level computational and biological preclinical models.

“Receiving this scholarship has been incredibly meaningful, because it offered me the chance to reflect critically on my post-graduate goals and receive recognition for my journey for them,” Kumaresh says. “Earning this scholarship has welcomed me into a tight-knit community where I’ve already found so much guidance. Everyone is genuinely curious about everyone else’s interests and are eager to lend a hand however they can.”

Anna Liu, a third-year chemistry major, is an undergraduate researcher in the Radosevich Group. The overarching objective of the group’s research is to develop new catalysts, strategies, and reagents for synthetic chemistry. By designing and synthesizing new molecular compounds with unknown structure and function, the group hopes to learn more about the general principles enabling new chemical transformations.

Liu is mentored by professor of chemistry Alexander Radosevich. She plans to pursue a PhD in organic or inorganic chemistry and eventually lead research developing sustainable synthetic transformations informed by fundamental mechanistic and reactivity studies, and teach at the university level.

“Going through the Goldwater application process gave me a deeper understanding of my research project and helped me reflect on my intrinsic motivations to pursue research. I’m excited to use what I’ve learned to keep growing as a researcher,” Liu says. “I am so grateful for the countless mentors, teachers, labmates, classmates, friends, and family in my life who have believed in me, fostered my passion for chemistry, and taught me so much. Receiving this scholarship is truly a testament to their outstanding support!"

Charlotte Myers, a third-year physics and astronomy major, conducts research at the Kavli Institute for Astrophysics and Space Research, where she applies machine learning to model galactic structure, and at the Center for Theoretical Physics, where she studies theoretical models of dark matter. Her research interests center on the physics of dark matter, which she approaches from multiple perspectives — from its distribution on galactic scales to particle-level models.

Myers is mentored by Lina Necib, an assistant professor in the Department of Physics. She plans to pursue a PhD in theoretical physics and conduct research in cosmology and astroparticle physics, with a focus on the fundamental physics of dark matter, and teach at the university level.

“I am very grateful to my research advisors, Professor Necib, Dr. Starkman, and Professor Slatyer, for their guidance and support in helping me develop as a researcher,” Myers says. “I find it deeply rewarding to engage with open questions in physics, and I am excited to continue pursuing this work in graduate school and beyond. Receiving this scholarship has given me both the resources and the confidence to continue on that path, even when progress is not always linear.”

The scholarship program honoring Senator Barry Goldwater was designed to identify, encourage, and financially support outstanding undergraduates interested in pursuing research careers in the sciences, engineering, and mathematics. The Goldwater Scholarship is the preeminent undergraduate award of its type in these fields.

MIT takes top team honors in 86th Putnam Math Competition

Natalie Kwok | Department of Mathematics — Thu, 23 Apr 2026 14:00:00 -0400

In an outstanding performance at the 86th William Lowell Putnam Mathematical Competition, MIT’s team once again took the top spot for the sixth consecutive year. MIT secured four of the five Putnam Fellows, who are the five highest-ranking students, and the Elizabeth Lowell Putnam Prize, which is given to a woman whose “performance in the competition is particularly meritorious.”

The members of the winning team, consisting of junior Cheng Jiang, senior Luke Robitaille, and first-year Chunji Wang, were all awarded as Putnam Fellows alongside senior Zixiang Zhou, each receiving a $2,500 award for their performance. Notably, Robitaille is a four-time Putnam Fellow, having received the award for each year of his studies. For a second consecutive year, sophomore Jessica Wan was awarded the Elizabeth Lowell Putnam Prize and received $1,000.

Wan was also among the top 25 scorers, amongst 16 others from MIT: Warren Bei, Reagan Choi, Pico Gilman, Henry Jiang, Zhicheng Jiang, Papon Lapate, Gyudong Lee, Derek Liu, Maximus Lu, Krishna Pothapragada, Pitchayut Saengrungkongka, Qiao Sun, Allen Wang, Kevin Wang, and Yichen Xiao.

A legacy of success

“I was delighted to see how well the MIT students did on the Putnam exam this year, which reflects their hard work, talent, and enthusiasm,” says Professor Henry Cohn, who led class 18.A34 (Mathematical Problem Solving) this year, also informally known as the Putnam seminar.

MIT’s continued success in the Putnam competition stems from a variety of sources. Some of this is built on things like the seminar, where students get together to sharpen their skills by diving deep into tough problems and discussing solutions.

Cohn, a former participant in the Putnam, comments on the joy of teaching the seminar and seeing students’ progress. “When you spend a semester watching students present solutions to difficult problems, you start to understand how they think,” says Cohn. “It’s exciting to see them apply their abilities to new, difficult problems."

Professor Bjorn Poonen, who also led the seminar in previous years (and is a four-time Putnam Fellow), describes it as an opportunity to hone a spectrum of skills in competition preparation. “Knowing how to explain things well is really important for doing well on the Putnam and for everything else, and for this it really helps to have experience communicating with others, which is what the problem-solving seminar is all about.”

A shared passion for problem-solving

The students who take the Putnam thrive on all aspects of the competition, from the social to the exam itself.

“It’s not a school day, and we still get to do math,” Jiang describes his excitement for the competition. Indeed, getting to “do math” extends beyond formally sitting for the exam, to breaks and opportunities for discussion that are interspersed throughout the day. The students take each opportunity to come together as seriously as they do the competition, and it is this collective passion for problem-solving that builds a strong sense of community and brings students back year after year.

“The competition brings together hundreds of students from across campus representing many majors, years of graduation, and degrees of math contest experience, but what brings everyone together is a shared love of solving problems,” Cohn says. “You can see this in the clusters of students who stay to discuss the problems long after the exam has ended. Mathematics can sometimes feel like a solitary pursuit, but at this level, collaboration is key.”

Community complements the shared passion the math enthusiasts share for problems and puzzles. “You get a kind of satisfaction similar to when you get unstuck while doing a crossword puzzle and everything falls into place,” Poonen describes his own experience solving Putnam problems.

Consistency in certainty

The competition is also an opportunity to see familiar faces. Robitaille recalls his experiences in high school math olympiads, and highlights the friendly atmosphere at the Putnam. “Throughout college, I have stayed close with people I met at competitions,” Robitaille says. “There’s the whole background of times spent together, not just on contest day.”

An event for both community and challenge, the consistency and certainty of competition day is what brought Robitaille and Zhou back year after year. “Each time, you have a set amount of time to sit in the room and work on the problems,” Robitaille says. “If you were the type of person for whom that would be a fun thing, like me, it’s nice to have an opportunity to do it again occasionally.”

“It’s more fun than the real world, where everything is complicated,” Zhou adds with a smile.

The full list of 2025 winners can be found on the Putnam website.

New chip can protect wireless biomedical devices from quantum attacks

Adam Zewe | MIT News — Thu, 23 Apr 2026 00:00:00 -0400

As quantum computers advance, they are expected to be able to break tried-and-true security schemes that currently keep most sensitive data secure from attackers. Scientists and policymakers are working to design and implement post-quantum cryptography to defend against these future attacks.

MIT researchers have developed an ultra-efficient microchip that can bring post-quantum cryptography techniques to wireless biomedical devices, like pacemakers and insulin pumps. Such wearable, ingestible, or implantable devices are usually too power-constrained to implement these computationally demanding security protocols.

Their tiny chip, which is about the size of a very fine needle tip, also includes built-in protections against physical hacking attempts that can bypass encryption to steal user data, such as a patient’s social security number or device credentials. Compared to prior designs, the new technology is more than an order of magnitude more energy-efficient.

In the long run, the new chip could enable next-generation wireless medical devices to maintain strong security even as quantum computing becomes more prevalent. In addition, it could be applied to many types of resource-constrained edge devices, like industrial sensors and smart inventory tags.

“Tiny edge devices are everywhere, and biomedical devices are often the most vulnerable attack targets because power constraints prevent them from having the most advanced levels of security. We’ve demonstrated a very practical hardware solution to secure the privacy of patients,” says Seoyoon Jang, an MIT electrical engineering and computer science (EECS) graduate student and lead author of a paper on the chip.

Jang is joined on the paper by Saurav Maji PhD ’23; visiting scholar Rashmi Agrawal; EECS graduate students Hyemin Stella Lee and Eunseok Lee; Giovanni Traverso, an associate 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; and senior author Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science. The research was recently presented at the IEEE Custom Integrated Circuits Conference.

Stronger security

A large percentage of wireless biomedical devices, like ingestible biosensors for health monitoring, currently lack strong protection due to the computational demands of existing security protocols, Jang says.

But the complexity of post-quantum cryptography (PQC) can increase power consumption by two or three orders of magnitude.

Implementing PQC is of paramount importance, since agencies like the National Institute of Standards and Technology (NIST) will soon begin phasing out traditional cryptography protocols in favor of stronger PQC algorithms. In addition, some industry leaders believe rapid advances in quantum hardware make PQC implementation even more urgent.

To bring these power-hungry PQC protocols to wireless biomedical devices, the MIT researchers designed a customized microchip, known as an application-specific integrated circuit (ASIC), that greatly reduces energy overhead while guaranteeing the highest level of security.

“PQC is very secure algorithmically, but making a device resilient against physical attacks usually requires additional countermeasures that pump up the energy consumption at least two or three times. We want our chip to be robust to both security threats in a very lightweight manner,” Jang says.

A multi-pronged approach

To accomplish these goals, the researchers incorporated several design features into the chip.

First, they implemented two different PQC schemes to enhance robustness and “future-proof” their device in case one scheme is later proven to be insecure. To boost energy efficiency, they applied techniques that enable the PQC algorithms to share as much of the chip’s computational resources as possible.

Second, the researchers designed a highly efficient, on-chip true random number generator. This device continually generates random numbers to use for secret keys, which is essential to implement PQC.

Their on-chip design improves energy efficiency and security over standard approaches that usually receive random numbers from an external chip.

Third, they implemented countermeasures that prevent a type of physical hacking attempt, called a power side-channel attack, but only on the most vulnerable parts of the PQC protocols.

In power side-channel attacks, hackers steal secret information by analyzing the power consumption of a device while it processes data. The MIT researchers added just enough redundancy to the PQC operations to ensure the chip is protected from these types of attacks.

Fourth, they designed an early fault-detection mechanism so the chip will abort operations early if it detects a voltage glitch.

Wireless biomedical devices often have erratic power supplies, so they are susceptible to glitches that can cause an entire security procedure to fail. The MIT approach saves energy by stopping the chip from running a doomed procedure to completion.

“At the end of the day, because of the techniques we utilized, we can apply these post-quantum cryptography primitives while adding nothing to the overhead, with the added benefit of robustness to side-channel attacks,” Jang says.

Their device achieved between 20 to 60 times higher energy efficiency than all other PQC security techniques they compared it to, with a more compact area than many existing chips.

“As we transition into post-quantum approaches, providing strong security for even the most resource-limited devices is essential. This work shows that robust cryptographic protection for biomedical and edge devices can be achieved alongside energy efficiency and programmability,” says Chandrakasan.

In the future, the researchers want to apply these techniques to other vulnerable applications and energy-constrained devices.

This research was funded, in part, by the U.S. Advanced Research Projects Agency for Health.

MIT affiliates elected to the American Academy of Arts and Sciences for 2026

MIT News — Wed, 22 Apr 2026 16:00:00 -0400

Four MIT faculty members are among the roughly 250 leaders from academia, the arts, industry, public policy, and research elected to the American Academy of Arts and Sciences, the academy announced April 22. Thirteen additional MIT alumni were also honored.

One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.

MIT faculty elected from MIT in 2026 are:

Isaiah Andrews PhD ’14, Charles E. and Susan T. Harris Professor of Economics;
David Atkin, Barton L. Weller (1940) Professor of Economics;
Pablo Jarillo-Herrero, Cecil and Ida Green Professor of Physics; and
Benjamin Paul Weiss, Robert R. Shrock Professor of Earth and Planetary Sciences

MIT alumni elected this year include Mark Aguiar PhD ’99 (Economics); Mark G. Allen SM ’86, PhD ’89 (Chemical Engineering); Magdalena Balazinska PhD ’06 (EECS); Keren Bergman SM ’91, PhD ’94 (EECS); Sara Cherry PhD ’00 (Biology); Cynthia J. Ebinger SM ’86, PhD ’88 (EAPS); Charles L. Epstein ’78 (Mathematics); Shanhui Fan PhD ’97 (Physics); Atif Mian ’96, PhD ’01 (Mathematics with Computer Science and Economics); Sarah E. O'Connor PhD ’01 (Chemistry); Darryll J. Pines SM ’88, PhD ’92 (Mechanical Engineering); Phillip (Terry) Ragon ’72 (Physics); and Mansour Shayegan ’79, EE ’81, SM ’81, PhD ’83 (Electrical Engineering).

“We celebrate the achievement of each new member and the collective breadth and depth of their excellence – this is a fitting commemoration of the nation’s 250th anniversary,” said Academy President Laurie Patton.

Since its founding in 1780, the academy has elected leading thinkers from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 250 Nobel and Pulitzer Prize winners.

Teaching AI models to say “I’m not sure”

Rachel Gordon | MIT CSAIL — Wed, 22 Apr 2026 15:15:00 -0400

Confidence is persuasive. In artificial intelligence systems, it is often misleading.

Today's most capable reasoning models share a trait with the loudest voice in the room: They deliver every answer with the same unshakable certainty, whether they're right or guessing. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have now traced that overconfidence to a specific flaw in how these models are trained, and developed a method that fixes it without giving up any accuracy.

The technique, called RLCR (Reinforcement Learning with Calibration Rewards), trains language models to produce calibrated confidence estimates alongside their answers. In addition to coming up with an answer, the model thinks about its uncertainty in that answer, and outputs a confidence score. In experiments across multiple benchmarks, RLCR reduced calibration error by up to 90 percent while maintaining or improving accuracy, both on the tasks the model was trained on and on entirely new ones it had never seen. The work will be presented at the International Conference on Learning Representations later this month.

The problem traces to a surprisingly simple source. The reinforcement learning (RL) methods behind recent breakthroughs in AI reasoning, including the training approach used in systems like OpenAI's o1, reward models for getting the right answer, and penalize them for getting it wrong. Nothing in between. A model that arrives at the correct answer through careful reasoning receives the same reward as one that guesses correctly by chance. Over time, this trains models to confidently answer every question they are asked, whether they have strong evidence or are effectively flipping a coin.

That overconfidence has consequences. When models are deployed in medicine, law, finance, or any setting where users make decisions based on AI outputs, a system that expresses high confidence regardless of its actual certainty becomes unreliable in ways that are difficult to detect from the outside. A model that says "I'm 95 percent sure" when it is right only half the time is more dangerous than one that simply gets the answer wrong, because users have no signal to seek a second opinion.

"The standard training approach is simple and powerful, but it gives the model no incentive to express uncertainty or say I don’t know," says Mehul Damani, an MIT PhD student and co-lead author on the paper. "So the model naturally learns to guess when it is unsure."

RLCR addresses this by adding a single term to the reward function: a Brier score, a well-established measure that penalizes the gap between a model's stated confidence and its actual accuracy. During training, models learn to reason about both the problem and their own uncertainty, producing an answer and a confidence estimate together. Confidently wrong answers are penalized. So are unnecessarily uncertain correct ones.

The math backs it up: the team proved formally that this type of reward structure guarantees models that are both accurate and well-calibrated. They then tested the approach on a 7-billion-parameter model across a range of question-answering and math benchmarks, including six datasets the model had never been trained on.

The results showed a consistent pattern. Standard RL training actively degraded calibration compared to the base model, making models worse at estimating their own uncertainty. RLCR reversed that effect, substantially improving calibration with no loss in accuracy. The method also outperformed post-hoc approaches, in which a separate classifier is trained to assign confidence scores after the fact. "What’s striking is that ordinary RL training doesn't just fail to help calibration. It actively hurts it," says Isha Puri, an MIT PhD student and co-lead author. "The models become more capable and more overconfident at the same time."

The team also demonstrated that the confidence estimates produced by RLCR are practically useful at inference time. When models generate multiple candidate answers, selecting the one with the highest self-reported confidence, or weighting votes by confidence in a majority-voting scheme, improves both accuracy and calibration as compute scales.

An additional finding suggests that the act of reasoning about uncertainty itself has value. The researchers trained classifiers on model outputs and found that including the model's explicit uncertainty reasoning in the input improved the classifier's performance, particularly for smaller models. The model's self-reflective reasoning about what it does and doesn’t know contains real information, not just decoration.

In addition to Damani and Puri, other authors on the paper are Stewart Slocum, Idan Shenfeld, Leshem Choshen, and senior authors Jacob Andreas and Yoon Kim.

Plants can sense the sound of rain, a new study finds

Jennifer Chu | MIT News — Wed, 22 Apr 2026 05:00:00 -0400

The next time you find yourself lulled by the patter of rain outside your window, think how that same sprinkle might sound if you were a tiny seed planted directly below a free-falling droplet. Would you still be similarly soothed?

In fact, MIT engineers have found the opposite to be the case: Some seeds may come alive to the sound of rain. In experiments with rice seeds, the team found that the sound of falling droplets effectively shook the seeds out of a dormant state, stimulating them to germinate at a faster rate compared with seeds that were not exposed to the same sound vibrations.

The team’s findings, which are published today in the journal Scientific Reports, are the first direct evidence that plant seeds and seedlings can sense sounds in nature. Their experiments involved rice seeds that they submerged in shallow water. Rice can germinate in both soil and shallow water. The researchers suspect that many similar seed types may also respond to the sound of rain.

The team worked out a hypothesis to explain how the seeds might be doing this. They found that when a raindrop hits the surface of a puddle or the ground, it generates a sound wave that makes the surroundings vibrate, including any shallowly submerged seeds. These vibrations can be strong enough to dislodge a seed’s “statoliths,” which are tiny gravity-sensing organelles within certain cells of a seed. When these statoliths are jostled, their movement is a signal for seeds and seedlings to grow and sprout.

“What this study is saying is that seeds can sense sound in ways that can help them survive,” says study author Nicholas Makris, a professor of mechanical engineering at MIT. “The energy of the rain sound is enough to accelerate a seed’s growth.”

Makris and his co-author, Cadine Navarro, a former graduate student in MIT’s Department of Urban Studies and Planning, suspect that the sound of rain is similar to the vibrations generated by other natural phenomena such as wind. They plan to follow up this work to investigate other natural vibrations and sounds plants may perceive.

Sound vibration

Plants are surprisingly perceptive. To help them survive, plants have evolved to sense and respond to stimuli in their surroundings. Some plants snap shut when touched, while others curl inward when exposed to toxic smells. And of course, most plants respond to light, reaching toward the sun to help them grow.

Plants can also sense gravity. A plant’s roots grow down, while its shoots push up against gravity’s pull. One way that plants sense and respond to gravity is through their statoliths. Statoliths are denser than a cell’s cytoplasm and can drift and sink through the cell, like a bit of sand in a jar of water. When a statolith finally settles to the bottom, its resting place on the cell’s membrane is a reflection of gravity’s direction and a signal for where a seed’s root or shoot should grow. If the statolith is dislodged, scientists have found that this can also trigger the seed to grow more.

Makris, whose work focuses on acoustics across a range of disciplines, became curious when Navarro asked him questions about seeds and sound. They wondered: Could sound be enough to jostle the statoliths and stimulate a seed to grow? And if so, what sounds in nature could be strong enough to have such an effect?

“I went back to look at work done by colleagues in the 1980s, who measured the sound of rain underwater. If you check, you’ll see it’s much greater than in the air,” Makris says. “It has to do with the fact that water is denser than air, so the same drop makes larger pressure waves underwater. So if you’re a seed that’s within a few centimeters of a raindrop’s impact, the kind of sound pressures that you would experience in water or in the ground are equivalent to what you’d be subject to within a few meters of a jet engine in the air.”

Such rain-induced soundwaves, Makris and Navarro suspected, might be enough to jostle statoliths and subsequently stimulate a seed’s growth.

Connecting a droplet’s dots

To test this idea, the researchers carried out experiments with rice seeds, which naturally grow in shallow watery fields. Over a large number of repeated experiments, the team submerged roughly 8,000 individual seeds of rice in shallow tubs of water and exposed sections of them to dripping water. The seeds were placed sufficiently far away from the falling droplets that only sound waves would reach them. The team varied the size and height of each water droplet to mimic raindrops during light, moderate, and heavy rainstorms.

The sound of rain, recorded by MIT researchers from underwater, within a rain puddle in Massachusetts during a moderate to heavy rainstorm.
Credit: Courtesy of the researchers

They also used a hydrophone to measure the acoustic vibrations created underwater by the water droplets. They compared these measurements to recordings they took in the field, such as in puddles, ponds, wetlands, and soils during rainstorms. The comparisons confirmed that their water droplets in the lab were generating rain-induced acoustic vibrations as in nature.

As they observed the rice seeds, the researchers found that the groups of seeds that were exposed to the sound of water were able to germinate 30 to 40 percent faster than the seed groups that were not exposed to rain sounds but were otherwise in identical conditions. They also found that seeds that were closer to the surface could better sense the droplets’ sounds and grow faster, compared to more submerged or more distant seeds.

These experiments showed that there is a connection between the sound of a water droplet and a seed’s ability to grow. The researchers propose that there may be a biological advantage to seeds that can sense rain: If they are close enough to the surface to respond to the sound of rain, they are likely at an optimal depth to soak up moisture and safely grow to the surface.

The team then worked out calculations to see whether the physical vibrations of the droplets would be enough to jostle the seeds’ microscopic statoliths. If so, this would point to the mechanism by which sound can directly stimulate a plant’s growth.

In their calculations, the researchers factored in a rain droplet’s size and terminal velocity (the constant speed that a falling object eventually reaches), and worked out the amplitude of sound vibration the droplet would generate. From this, they determined to what degree these vibrations in water or soil would displace, or shake a submerged or buried seed, and how a shaking seed would affect microscopic statoliths within individual cells.

Makris and Navarro found that the experiments they performed on rice seeds were consistent with their calculations: The sound of rain can indeed dislodge and jostle a seed’s statoliths. This mechanism is likely at the root of a plant’s ability to “sense” the sound of rain and grow in response.

“Brilliant research has been done around the world to reveal the mechanisms behind the ability of plants to sense gravity,” Makris notes. “Our study has shown that these same mechanisms seem to be providing plant seeds a means of perceiving submergence depths in the soil or water that are beneficial to their survival by sensing the sound of rain. It gives new meaning to the fourth Japanese microseason, entitled ‘Falling rain awakens the soil.’”

This work was supported, in part, by the MIT Bose Fellowship and the MIT Koch Chair.

T.L. Taylor named 2026-27 CASBS Fellow

Andrew Whitacre | Comparative Media Studies/Writing — Tue, 21 Apr 2026 19:10:00 -0400

MIT Comparative Media Studies/Writing Professor T.L. Taylor has been named a 2026-27 fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University (CASBS), a highly selective residential program that convenes scholars from a wide range of disciplines for a year of focused research, collaborative exchange, and intellectual engagement.

Professor Taylor — an ethnographer whose work sits at the intersection of sociology; media studies; and science, technology, and society — will be focusing on her current project exploring the rise of “immersion” in physical spaces as a contemporary cultural pursuit. While new entertainment undertakings like The Sphere in Las Vegas, interactive theater like Sleep No More, or Meow Wolf’s growing list of city-based immersive art projects have captured popular attention, Taylor’s project turns to their progenitor, a much older, more widespread instantiation of the immersive experience — the theme park.

Building on fieldwork undertaken over the last several years in Disney parks around the world, as well as interviews with both designers and attendees, she will be working on a new book that examines theme parks as sitting at the analytically rich intersection of design, infrastructure, and play. Extending her influential work on digital environments and online communities, this project bridges from game and virtual world studies to an examination of physical, immersive environments.

As in her prior work, Taylor treats leisure as an area of study worth taking seriously. Not dissimilar to gaming, there is a tendency to underestimate, or simply dismiss, the economic and cultural significance of these environments. In 2025, theme parks worldwide boasted 976 million visitors and the Walt Disney Co.’s “Experiences” division alone reported $10 billion in profit last year. Spaces of play and experiential engagement also regularly embody some of our most pressing contemporary conversations. Theme parks, she notes, are “at the heart of economic and media systems, technological development, and cultural imaginaries despite — like video games before them — often being dismissed as peripheral to ‘serious’ matters.”

The fellowship project frames theme parks as simultaneously operating on several levels: intentionally designed worlds “that invite people to step into them,” socio-technical infrastructures “meant to facilitate affective, embodied experience,” and as “playgrounds” that sometimes afford participation beyond corporate control and governance.

At the center of the work is a tension familiar from digital environments. “You invite people into a designed space,” she says, “but what happens when emergent culture collides with expectations of use?” One of the most interesting examples of this tension she has encountered in her fieldwork, for example, is of fan-organized live-action role-play within a theme park, a moment in which the environment functions as a playground for emergent experience within an otherwise tightly controlled commercial frame.

The CASBS fellowship will offer Taylor the time and intellectual cross-pollination needed to best situate, and even challenge, her new work. The program’s interdisciplinary cohort is drawn from across the social sciences, humanities, law, health, and other fields; it includes 36 scholars from 30 institutions. “It’s an amazing opportunity to work through the data and write in a really vibrant setting where conversation and cross-disciplinary engagement is at the heart of the experience” she says.

New study bridges the worlds of classical and quantum physics

Jennifer Chu | MIT News — Tue, 21 Apr 2026 19:05:00 -0400

When you throw a ball in the air, the equations of classical physics will tell you exactly what path the ball will take as it falls, and when and where it will land. But if you were to squeeze that same ball down to the size of an atom or smaller, it would behave in ways beyond anything that classical physics can predict.

Or so we’ve thought.

MIT scientists have now shown that certain mathematical ideas from everyday classical physics can be used to describe the often weird and nonintuitive behavior that occurs at the quantum, subatomic scale.

In a paper appearing today in the journal Proceedings of the Royal Society, the team shows that the motion of a quantum object can be calculated by applying an idea from classical physics known as “least action.” With their new formulation, they show they can arrive at exactly the same solution as the Schrödinger equation — the main description of quantum mechanics — for a number of textbook quantum-mechanical scenarios, including the double-slit experiment and quantum tunneling.

Such mysterious phenomena, that could only be understood through equations of quantum mechanics, can now also be described using the team’s new classical formulation. In essence, the researchers have built an exact mathematical bridge between the classical, everyday physical world and the world that happens at dimensions smaller than an atom.

“Before, there was a very tenuous bridge that worked only for reasonably large [quantum] particles,” says study co-author Winfried Lohmiller, a research associate in the Nonlinear Systems Laboratory at MIT. “Now we have a strong bridge — a common way to describe quantum mechanics, classical mechanics, and relativity, that holds at all scales.”

“We’re not saying there’s anything wrong with quantum mechanics,” emphasizes co-author Jean-Jacques Slotine, an MIT professor of mechanical engineering and information sciences, and of brain and cognitive sciences. “We’re just showing a different way to compute quantum mechanics, which is based on well-known classical ideas that we put together in a simple way.”

To infinity and far below

Slotine and Lohmiller derived the quantum bridge while working on solidly classical problems. The researchers are members of the MIT Nonlinear Systems Laboratory, which Slotine directs. He and his colleagues develop models to describe complex behavior in problems of robotic and aircraft control, neuroscience, and machine learning. To predict the behavior of such systems, engineers often look to the Hamilton-Jacobi equation, which is one of the major formulations of classical mechanics and is related to Newton’s famous laws of motion.

The Hamilton-Jacobi equation essentially represents an object’s motion as minimizing a quantity called the action. Take, for instance, a simple scenario in which a ball is thrown from point A to point B. Theoretically, the ball could take any number of zigzagging paths between the two points. But the equation states that the actual path should be one where the ball’s “action” is minimized at every single point along that path.

In this case, the term “action” refers to the sum over time of the difference between an object’s kinetic energy (the energy that is generating the motion) and its potential energy (the object’s stored energy). The actual path that a ball takes between point A and B should then be a sequence of positions where the overall difference between kinetic and potential energy is minimized.

Slotine and Lohmiller were applying the Hamilton-Jacobi equation, and the principle of least action, to a number of classical mechanics problems with constraints when they realized that the equation, with some mathematical extensions, could solve a famous problem in quantum mechanics known as the double-slit experiment.

The double-slit experiment illustrates one of the weird, nonclassical behaviors that arises at quantum scales. In the experiment, two slits are cut out of a metal wall. When a single photon — a quantum-scale particle of light — is shot toward the wall, classical physics predicts that you should see a spot of light on the other side of the wall, assuming that the photon flew straight through either one of the holes, following a single path.

But experimentalists have instead observed alternating bright and dark stripes. The reality-bending pattern is a result of a quantum mechanical phenomenon by which a photon takes more than one path simultaneously. In this context, when a single photon is shot toward the wall, it can pass through both holes at the same time, along two paths that end up interfering with each other. The pattern of stripes that results means that the photon’s two interfering paths must be wave-like. The experiment therefore demonstrates how a quantum particle can also behave, however improbably, like a wave.

Since the discovery of quantum mechanics, physicists have tried to explain the double-slit experiment using tools from classical, everyday physics. But they’ve only ever been able to approximate the experiment’s results.

Even the noted physicist Richard Feynman ’39 found the task impossible. He assumed that one would have to consider and average over every single theoretical path that a photon could take, whether it be a straight line or any variation of a zigzagging path through either of the two holes. Such an exercise would require calculating an infinite number of possible zigzag paths, which all contradict the classical smooth paths one would expect.

This last point is what Slotine and Lohmiller realized could be tweaked. Where classical physics assumes that an object must only take a single path from point A to B, quantum mechanics allows for an object to take multiple paths and multiple states simultaneously — a fundamental quantum property known as superposition.

The team wondered: What if classical physics could also entertain, at least mathematically, this notion of multiple paths? Then, they reasoned that an infinite number of paths wouldn’t have to be calculated. Instead, a much smaller number of “least action” classical paths might produce the exact same quantum result.

With this idea in mind, they looked back to the Hamilton-Jacobi equation to see how they might adapt its principles of least action to predict the double-slit experiment and other quantum phenomena.

“For a while we thought it was a little too good to be true,” Slotine says.

A particle’s destiny is in its density

In their new study, the team adds another ingredient of classical physics: “density,” which is, essentially, a probability that a given path is taken.

“We think of density in terms of fluid dynamics,” Lohmiller explains. “For the double-slit experiment, imagine pumping a hose toward the wall. What will happen is, most of the water will hit the center, but some droplets will also go toward the sides. A high density of water at the center means there is a high probability of finding a droplet along that path. And there will be a distribution, which we can compute.”

He and Slotine tweaked the Hamilton-Jacobi equation to include terms of density and multiple least action paths, and applied it to the double-slit experiment. They found that with this formulation, they only had to consider two classical paths through the two slits, as compared to Feynman’s infinity of zigzag paths. Ultimately, their calculations of classical density and action produced a wave function, or distribution of most probable paths that a photon could take, that was exactly the same as what was predicted by the Schrödinger equation, which is the central equation used to describe quantum-mechanical behavior.

“We show that the Schrödinger’s equation of quantum mechanics and the Hamilton-Jacobi equation of classical physics are actually identical given a suitable computation of density,” Slotine says. “That’s a purely mathematical result. We’re not saying that quantum phenomena happens at classical scales. We’re saying you can compute this quantum behavior with very simple classical tools.”

In addition to the double-slit experiment, the researchers showed the reworked equation can also predict other quantum mechanical behavior, such as quantum tunneling, in which particles such as electrons can pass through energy barriers that would not be possible according to classical physics. They could also derive the exact quantum wave of the electron in a hydrogen atom from the classical orbit of a planet. Finally, they revisited from this perspective the famous Einstein-Podolski-Rosen experiment, which started the modern study of quantum entanglement.

The researchers envision that scientists could use the new formula as a simple method to predict how certain quantum systems and devices will perform.

“There could be important implications for quantum computing, where quantum bits have these nonlinear energies that physicists must approximate, or for better understanding problems involving both quantum physics and general relativity,” Slotine offers. “In principle at least, we should now be able to characterize this quantum behavior exactly, with simple classical tools, and show that it’s not so mysterious after all.”

Two MIT alumnae named 2026 Gates Cambridge Scholars

Julia Mongo | Office of Distinguished Fellowships — Tue, 21 Apr 2026 18:35:00 -0400

Mitali Chowdhury ’24 and Christina Kim ’24 have been selected as 2026 Gates Cambridge Scholars. The highly competitive fellowship offers fully funded opportunities for postgraduate study in any field at Cambridge University in the U.K. Kim is a second-time Gates Cambridge Scholar.

MIT students interested in the Gates Cambridge Scholar program should contact Kim Benard, associate dean of distinguished fellowships in Career Advising and Professional Development.

Mitali Chowdhury

Chowdhury graduated from MIT with a BS in biological engineering and minors in both urban planning and environment and sustainability. Chowdhury has had a longstanding interest in reducing inequities in global health. At MIT, she pursued research in point-of-care diagnostics to identify and treat disease with accessible biotechnologies. She also helped develop low-cost testing for bacterial contamination in water in South Asia.

Chowdhury currently works at a startup advancing sequencing-based diagnostics. At Cambridge University, she will study for MPhil and PhD degrees in the Centre for Doctoral Training in Sensor Technologies. Her research will focus on CRISPR-based diagnostics to address antimicrobial resistance and expand equitable access to care.

Christina Kim

After graduating from MIT with a bachelor’s degree in chemistry and biology, Kim worked as a researcher in women’s health at the Wellcome Sanger Institute in Cambridge, U.K.

As a 2025 Gates Cambridge Scholar, Kim pursued an MPhil in research at the institute, focusing on using bioinformatics and tissue engineering to design novel in vitro models. Her second Gates Cambridge scholarship will fund her PhD studies.

How morality and ethics shaped India’s economic development

Maria Iacobo | School of Architecture and Planning — Tue, 21 Apr 2026 18:30:00 -0400

In a world leaning away from globalization, governments face a tough choice: Should they block dominant foreign companies to protect local businesses, or welcome them in hopes of fast-tracking economic growth and modernization?

In his recently published book, “Traders, Speculators, and Captains of Industry: How Capitalist Legitimacy Shaped Foreign Investment Policy in India” (Harvard University Press, November 2025), Jason Jackson, associate professor in political economy and urban planning in the MIT Department of Urban Studies and Planning, explains that these policy decisions aren’t just math, but long-standing and often heated moral debates over how businesses should conduct themselves, and who they serve.

Jackson argues that morality has a long history in economics and deserves more attention because, while ever-present in economic policy discourse, moral beliefs are often under-recognized or underappreciated.

“India is an exemplary case of ways in which moral beliefs shape economic policy decisions,” says Jackson. “But at the same time, I think it’s representative of a general feature of capitalism. It’s the perfect case.”

Jackson’s focus on India for this book stems from his interest in industrial policy and the politics of international development. Multinational firms have long been a source of controversy. They are seen as bringing two crucial resources to developing countries: finance and technology. However, while multinationals are potentially valuable contributors to economic development through the mechanism of foreign direct investment (FDI), they can also be monopolistic, dominating local industries and displacing domestic firms.

This long-standing tension in foreign investment policy became the backdrop for several emerging markets in developing countries — Brazil, Russia, India, China, and South Africa (BRICS) — in the early 2000s. India was growing at an extremely high level — 6-7 percent annually — and Indian companies were doing well, including those in industries that were seen as key to development, such as autos. Jackson wanted to understand why Indian companies were holding their own relative to foreign firms, which dominated more manufacturing in other places, and planned to focus on the period from the 1980s through the 2010s that coincides with the period of economic liberalization in India and, more broadly, with globalization. But while conducting field work, Jackson noticed that in describing how they made industrial policy decisions, Indian policymakers drew distinctions between firms that were fashioned in moral terms. There were some firms that policymakers believed would invest in technology and provide good jobs, and other firms — both foreign and domestic — seen as exploitative and not interested in engaging in activities that would advance economic growth and industrial transformation.

“I realized these distinctions had deep salience,” says Jackson. “My interlocutors would describe firms — especially foreign firms they saw as simply trading, or as exploitative — as ‘New East India’ companies, referencing the famous East India Company that was the governance authority in colonial India, but had been defunct for more than 150 years. That forced my research to become more historical, increasingly relying on archival work to make sense of these moralized distinctions between different types of business actors, whether foreign or domestic, and to understand how these beliefs became so powerful across Indian society.”

“Moral categories of capitalist legitimacy”

Jackson says there are several ways in which social scientists think that policymakers make decisions. One view considers the competing interest groups policymakers must negotiate with, in which case outcomes may depend on one group having more influence or power than others. Another approach assumes these individuals make decisions based on self-interest, particularly when their choices are perceived as corrupt.

“But what I found is that neither of these approaches gave enough credence to the ways in which policymakers in India grapple with quite technical and complex policy decisions regarding the type of development they want to promote in their country, and the types of companies they thought could help to achieve their development goals.” says Jackson. “Therefore, I was more interested in trying to understand what kind of ideas and beliefs animated their decision-making.”

What Jackson found was that Indian policymakers viewed both foreign firms and local Indian companies through what he terms “moral categories of capitalist legitimacy.” Would these firms invest in productive technologies? Would they provide good employment for the local population? Or would they be exploitative? These criteria were not only applied to multinational corporations. Even Indian family-controlled business groups were evaluated as to whether the gains accrued stayed within the confines of the extended family or whether they provided broader societal benefits.

Coca-Cola goes to India

The story of Coca-Cola in India is an example of the tension experienced with regulating foreign investment where multinational companies were seen as exploitative. The company made its initial foray into India in the 1950s, and over the next two decades its reach became extensive. In the late 1970s, India’s Minister of Industry George Fernandes was visiting a village in Bihar — a state with one of the highest levels of poverty — when he asked for a glass of water. Instead, he was told the water was not suitable to drink, and was given Coca-Cola.

“This struck Fernandes as deeply problematic,” says Jackson. “He later recalled thinking that ‘after 30 years of freedom in India, our villages do not have clean drinking water, but they do have Coca-Cola — which, of course, is made with purified water, so safe to drink. How was this possible?’” Fernandes returned to his office in New Delhi determined to do something about it.

Just a few years earlier, India had passed a law, the Foreign Exchange Regulation Act (FERA), which required foreign companies to dilute their equity to no more than 40 percent. The law was explicitly designed to encourage technology transfer, but Coca-Cola had not complied. Fernandes told Coca-Cola that it had to take on an Indian partner or it would have to leave. Coca-Cola chose the latter. In the following year, IBM was also kicked out of India when it similarly balked at complying with FERA and sharing its technology.

“These companies were very much seen in the mold of the East India Co.,” says Jackson. “A firm comes from abroad and extracts resources from India while giving little benefit to the country. These are all very clearly morally coded beliefs that played a crucial role in these policy decisions.”

With Coca-Cola out of India, the beverage market became wide open, and several Indian companies emerged. Thums Up, an Indian cola brand — founded by Ramesh Chauhan ’62 — took off and became the dominant cola by the 1980s. Chauhan developed its own unique formula independently.

In 1991, India accelerated its economic liberalization, especially around FDI, and FERA’s standards were diluted. Coca-Cola returned to India, again without a partner. Other major brands, including Pepsi, had also entered the market. By then, Thums Up had a market share in India of well over 80 percent, but, concerned with its ability to compete in a war between the deep-pocketed American multinational giants, Thums Up sold out to Coca-Cola for $60 million in 1993, a figure that was later deemed to be small.

Trader, speculator, or captain of industry?

Jackson says that in India, there were two competing interpretations of this story. In one version, Fernandes kicking out a global multinational firm was seen as a developing country establishing its economic sovereignty by making a bold policy decision and “risking all kind of geopolitical blowback that might follow from the U.S.,” says Jackson. “In this view, the Indian government’s bold move allowed local entrepreneurs and local companies like Chauhan and Thums Up to emerge.”

Yet an important counter narrative emerged that challenged the view that companies like Thums Up and figures like Chauhan are enterprising entrepreneurs.

“Maybe they just took advantage of protectionism to form a company and make some money,” says Jackson. “So rather than being an intrepid captain of industry, observers wondered whether maybe Chauhan was ‘simply a trader’ who took advantage of policy protection, but sold out as soon as the market became competitive.”

Later developments added some credibility to this view. Ironically, Coca-Cola was unable to remove Thums Up and Limca, another soda brand from Chauhan’s company, from its product lineup, and both remained extremely popular and widely consumed. This suggested to many observers that Thums Up could have survived the cola wars had it not sold out to the American multinational. The public had acquired a taste for the distinctly Indian beverages that Chauhan had created.

“This narrative encapsulates this kind of tension policymakers face: If we provide policy support to our enterprising entrepreneurs and they thrive, will they also do well for the country? Or are they simply opportunists who will take advantage of policy support in ways that benefit themselves but have little broader benefits to the country,” says Jackson.

This episode was just one of dozens of instances of conflicts between Indian companies and multinational firms in the liberalizing 1990s and 2000s, which the government was often compelled to adjudicate. Throughout this period, the question persisted: How would policymakers identify the business figures who could be agents of industrial development and economic transformation, whether foreign or domestic?

Ramesh Chauhan for one continued an enterprising path. He turned his attention to the bottled water industry in India and his brand — Bisleri — remains one of the country’s leading bottled water brands today.

How to expand the US economy

Peter Dizikes | MIT News — Tue, 21 Apr 2026 12:00:00 -0400

It’s an essential insight about our world: Innovation drives economic growth. For the U.S. to thrive, it must keep innovating. But how, and in what areas?

A new book co-authored by MIT faculty members focuses on six key areas where technology advances can drive the economy and support national security.

Those sectors — semiconductors, biotechnology, critical minerals, drones, quantum computing, and advanced manufacturing — are all built on U.S. know-how but are also areas where the country has either yielded a lead in production or innovation, or could yet fall behind.

As the book explains, a roadmap for U.S. prosperity and security involves sustaining notable areas of innovation and the national research ecosystem behind them, while rebuilding domestic manufacturing.

“In each of these areas, there are breakthroughs to be had, where the U.S. can leapfrog competitors and gain an advantage,” says Elisabeth Reynolds, an MIT expert on industrial innovation and editor of the new volume. “That’s a very exciting part of this.” She adds: “These areas are front and center for U.S. national economic and security policy.”

The book, “Priority Technologies: Ensuring U.S. Security and Shared Prosperity,” is published this week by the MIT Press. It features chapters by MIT faculty with expertise on the industrial sectors in question. Reynolds, a professor of the practice in MIT’s Department of Urban Studies and Planning, is a leading expert on industrial innovation and has long advocated for innovation-based growth that helps the U.S. workforce.

“All of this can be good for everyone,” says MIT economist Simon Johnson, who wrote the foreword to the book. “Out of that flow of innovations and ideas, we can create more good jobs for all Americans. Pushing the technological frontier and turning that into jobs is definitely going to help.”

Making more chips

“Priority Technologies” grew out of an ongoing MIT seminar by the same name, which Reynolds and Johnson began holding in 2023, often with appearances by other MIT faculty.

Both Reynolds and Johnson bring vast experience to the subject of innovation and production. Among other things, Reynolds headed MIT’s Industrial Performance Center for over a decade and was executive director of the MIT Task Force on the Work of the Future. She served in the White House National Economic Council as special assistant to the president for manufacturing and development.

Johnson, the Ronald A. Kurtz (1954) Professor of Entrepreneurship at the MIT Sloan School of Management, shared the 2024 Nobel Prize in economics, with MIT’s Daron Acemoglu and the University of Chicago’s James Robinson, for work about the historical relationship between institutions and economic growth. He has co-authored numerous books, including, with Acemoglu, the 2023 book “Power and Progress,” about the trajectory and implications of artificial intelligence.

As it happens, “Priority Technologies” does not focus on AI, instead opting to examine other vital, and often related, areas of innovation.

“We do not think this is the entire list of priority technologies,” Johnson says. “This is a partial list, and there are lots of other ideas.”

In the chapter on semiconductors, Jesús A. del Alamo, the Donner Professor of Science in MIT’s Department of Electrical Engineering and Computer Science, calls them “the oxygen of modern society.” This U.S.-born industry has seen a large manufacturing shift away from the country, however, leaving it vulnerable in terms of security and the economy; about one-third of inflation experienced in 2021 stemmed from a chip shortage. As he notes, the U.S. is now in the process of rebuilding its capacity to make leading-edge logic chips, for one thing.

“With semiconductors, people thought the U.S. could lose the manufacturing, stay on top of the innovation and design side, and would be fine,” Reynolds says. “But it’s turned out to make the country quite vulnerable. So we’ve had a massive shift to rebuild semiconductor manufacturing capabilities here in the U.S., and I would argue that’s been a successful strategy in recent years.”

Bringing biotech back home

In biotechnology, relocating manufacturing in the U.S. is also key, using new technologies in the process. As J. Christopher Love, the Laurent Professor of Chemical Engineering, puts it in his chapter, while the U.S. is the leader in biotech research, it “lacks the manufacturing infrastructure and expertise necessary to bring these ideas to the market at the same pace as it generates innovative new products.” Among other remedies, he suggests that smaller, more flexible production facilities can help the U.S. “leapfrog” other countries on the manufacturing side. Love is also co-director of MIT’s Initiative for New Manufacturing, which aims to drive advances in U.S. production across industries.

“We have tremendous biotech innovation, we’re the leaders, but we have a bottleneck when it comes manufacturing,” Reynolds observes. “If we can break through that with new technologies, new production processes, we’re in a position to make us less vulnerable, from a supply chain point of view, and capture more of what is going to be a $4 trillion market over the next 15 years.”

A similar story holds in other areas. Many drone innovations were developed in the U.S., while much manufacturing has shifted to China. Fiona Murray, the William Porter (1967) Professor of Entrepreneurship, writes that the U.S. has an “opportunity to rebuild its production at scale,” although that will also require significant strengthening of its supply chains, too.

Elsa Olivetti, the Jerry McAfee (1940) Professor of Engineering and a professor of materials science and engineering, recommends a multifaceted approach to help the U.S. regain traction in the production of critical minerals, including better forms of extraction, manufacturing, and recycling, to reduce potential scarcities.

And in the quantum computing chapter, two MIT co-authors — William D. Oliver, the Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science and a professor of physics; and Jonathan Ruane, a senior lecturer at MIT Sloan — note that the sector could help accelerate drug discovery, materials science, and energy applications. Noting that the U.S. still leads in private-sector investment in the field but tails China in public-sector investment, they urge more research support and stronger supply chains for quantum computing components, among other recommendations.

“The country that achieves quantum leadership will gain decisive advantages in these strategically important industries,” they write.

The university engine

From industry to industry, the book makes clear that certain key issues are broadly important to U.S. competitiveness and growth. The partnership between the federal government and the world-leading research capacities of U.S. universities, for one thing, has given the country an initial lead in many economic sectors and promises to continue driving innovation.

At the same time, the U.S. would benefit from expanding and strengthening its domestic supply chains, in the process of building up more domestic manufacturing, and needs capital investment that will help hardware-side, physically substantial industrial growth.

“These common themes include supply chain resilience and manufacturing capability,” Reynolds says. “Can we help drive the country’s innovation ecosystem through expansion of our industrial system and manufacturing? That’s a big question.”

On the research front, she reflects, over the years, “It’s been amazing how much MIT-led research has aligned with national priorities — or maybe that’s not so surprising.”

The partnership between the U.S. federal government and universities as research engines was formalized in the 1940s, thanks in part to then-MIT president Vannevar Bush. According to some estimates, government investment in non-defense research and development alone has accounted for up to 25 percent of U.S. economic growth since World War II.

“Vannevar Bush realized it wasn’t about a stock of technology, it was about a flow of innovation,” Johnson says. “And that brilliant insight is still relevant today. I think that is the insight of the last century. And that’s what we’re trying to capture and reiterate and repeat.”

“This is not even the future. This is current.”

Scholars and industry leaders have praised “Priority Technologies.” Erica Fuchs, a professor of engineering and public policy at Carnegie Mellon University, has stated that when it comes to “ensuring American national security, economic competitiveness, and societal well-being,” the book underscores “the positive role technology can play in those outcomes.” Hemant Taneja, CEO of the venture capital firm General Catalyst, calls the volume “required reading for anyone interested in building the abundant, resilient future America deserves.”

For their part, Reynolds and Johnson hope the book will draw many kinds of readers interested in the economy, innovation, prosperity, and national security.

“We tried to make the volume accessible,” Reynolds says, noting that the book directly lays out “challenges for the country, and what we see as recommendations for next steps in how we position the country to succeed, and lead globally. Each of these chapters has something important to say.”

Johnson also notes the MIT scholars participating in the project want to enhance the ongoing policy conversation, in Washington and across the country, about supporting innovation and using it to drive U.S. economic and technological leadership.

“One reason to write a book is, you can’t pound the table with a podcast,” quips Johnson, who co-hosts a podcast, “Power and Consequences,” on major policy issues. In conversations with political leaders and their staffs, he adds, there is a core message to be transmitted about America and technology-driven growth: We have the knowledge and resources, but need to focus on supporting innovation while trying to increase domestic production.

“Here are the technologies we currently need,” Johnson says. “This is not imagination, this is not fanciful, this is not science fiction. This is not even the future. This is current. These are the technologies needed to defend the country and its interests. And we need to invest in these, and in everything we need to drive them forward.”

Managing traffic in space

Jennifer Chu | MIT News — Sun, 19 Apr 2026 10:05:00 -0400

Chances are, you’ve already used a satellite today. Satellites make it possible for us to stream our favorite shows, call and text a friend, check weather and navigation apps, and make an online purchase. Satellites also monitor the Earth’s climate, the extent of agricultural crops, wildlife habitats, and impacts from natural disasters.

As we’ve found more uses for them, satellites have exploded in number. Today, there are more than 10,000 satellites operating in low-Earth orbit. Another 5,000 decommissioned satellites drift through this region, along with over 100 million pieces of debris comprising everything from spent rocket stages to flecks of spacecraft paint.

For MIT’s Richard Linares, the rapid ballooning of satellites raises pressing questions: How can we safely manage traffic and growing congestion in space? And at what point will we reach orbital capacity, where adding more satellites is not sustainable, and may in fact compromise spacecraft and the services that we rely on?

“It is a judgement that society has to make, of what value do we derive from launching more satellites,” says Linares, who recently received tenure as an associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “One of the things we try to do is approach these questions of traffic management and orbital capacity as engineering problems.”

Linares leads the MIT Astrodynamics, Space Robotics, and Controls Lab (ARCLab), a research group that applies astrodynamics (the motion and trajectory of orbiting objects) to help track and manage the millions of objects in orbit around the Earth. The group also develops tools to predict how space traffic and debris will change as operators launch large satellite “mega-constellations” into space.

He is also exploring the effects of space weather on satellites, as well as how climate change on Earth may limit the number of satellites that can safely orbit in space. And, anticipating that satellites will have to be smarter and faster to navigate a more cluttered environment, Linares is looking into artificial intelligence to help satellites autonomously learn and reason to adapt to changing conditions and fix issues onboard.

“Our research is pretty diverse,” Linares says. “But overall, we want to enable all these economic opportunities that satellites give us. And we are figuring out engineering solutions to make that possible.”

Grounding practical problems

Linares was born and raised in Yonkers, New York. His parents both worked as school bus drivers to support their children, Linares being the youngest of six. He was an active kid and loved sports, playing football throughout high school.

“Sports was a way to stay focused and organized, and to develop a work ethic,” Linares says. “It taught me to work hard.”

When applying for colleges, rather than aim for Division I schools like some of his teammates, Linares looked for programs that were strong in science, specifically in aerospace. Growing up, he was fascinated with Carl Sagan’s “Cosmos” docuseries. And being close to Manhattan, he took regular trips to the Hayden Planetarium to take in the center’s immersive projections of space and the technologies used to explore it.

“My interest in science came from the universe and trying to understand our place within it,” Linares recalls.

Choosing to stay close to home, he applied to in-state schools with strong aeronautical engineering departments, and happily landed at the State University of New York at Buffalo (SUNY Buffalo), where he would ultimately earn his bachelor’s, master’s, and doctoral degrees, all in aerospace engineering.

As an undergraduate, Linares took on a research project in astrodynamics, looking to solve the problem of how to determine the relative orientation of satellites flying in formation.

“Formation flying was a big topic in the early 2000s,” Linares says. “I liked the flavor of the math involved, which allowed me to go a layer deeper toward a solution.”

He worked out the math to show that when three satellites fly together, they essentially form a triangle, the angles of which can be calculated to determine where each satellite is in relation to the other two at any moment in time. His work introduced a new controls approach to enable satellites to fly safely together. The research had direct applications for the U.S. Air Force, which helped to sponsor the work.

As he expanded the research into a master’s thesis, Linares also took opportunities to work directly with the Air Force on issues of satellite tracking and orientation. He served two internships with the U.S. Air Force Research Lab, one at Kirtland Air Force Base in Albuquerque, New Mexico, and the other in Maui, Hawaii.

“Being able to collaborate with the Air Force back then kind of grounded the research in practical problems,” Linares says.

For his PhD, he turned to another practical problem of “uncorrelated tracks.” At the time, the Air Force operated a network of telescopes to observe more than 20,000 objects in space, which they were working to label and record in a catalog to help them track the objects over time. But while detecting objects was relatively straightforward, the challenge came in correlating a detected object with what was already in the catalog. In other words, is what they were seeing something they had already seen?

Linares developed image analysis techniques to identify key characteristics of objects such as their shape and orientation, which helped the Air Force “fingerprint” satellites and pieces of space debris, and track their activity — and potential for collisions — over time.

After completing his PhD, Linares worked as a postdoc at Los Alamos National Laboratory and the U.S. Naval Observatory. During that time he expanded his aerospace work to other areas including space weather, using satellite measurements to model how Earth’s ionosphere — the upper layer of the atmosphere that is ionized by the sun’s radiation — affects satellite drag.

He then accepted a position as assistant professor of aerospace engineering at the University of Minnesota at Minneapolis. For the next three years, he continued his research in modeling space weather, tracking space objects and coordinating satellites to fly in swarms.

Making space

In 2018, Linares made the move to MIT.

“I had a lot of respect for the people and for the history of the work that was done here,” says Linares, who was especially inspired by the legendary Charles Stark “Doc” Draper, who developed the first inertial guidance systems in the 1940s that would enable the self-navigation of airplanes, submarines, satellites, and spacecraft for decades to come. “This was essentially my field, and I knew MIT was the best place to continue my career.”

As a junior faculty member in AeroAstro, Linares spent his first years focused on an emerging challenge: space sustainability. Around that time, the first satellite constellations were launching into low-Earth orbit with SpaceX’s Starlink, which aimed to provide global internet coverage via a huge network of several thousand coordinating satellites. The launching of so many satellites, into orbits that already held other active and nonactive satellites, along with millions of pieces of space debris, raised questions about how to safely manage the satellite traffic and how much traffic an orbit can sustain.

“At what level do we reach a tipping point, where we have too many satellites in certain orbital regimes?” Linares says. “It was kind of a known problem at the time, but there weren’t many solutions.”

Linares’ group applied an understanding of astrodynamics, and the physics of how objects move in space, to figure out the best way to pack satellites in orbital “shells,” or lanes that would most likely prevent collisions. They also developed a state-of-the-art model of orbital traffic, that was able to simulate the trajectories of more than 10 million individual objects in space. Previous models were much more limited in the number of objects they could accurately simulate. Linares’ open-source model, called the MIT Orbital Capacity Assessment Tool, or MOCAT, could account for the millions of pieces of space debris, in addition to the many intact satellites in orbit.

The tools that his group has developed are used today by satellite operators to plan and predict safe spacecraft trajectories. His team is continuing to work on problems of space traffic management and orbital capacity. They are also branching out into space robotics. The team is testing ways to teleoperate a humanoid robot, which could potentially help to build future infrastructure and carry out long-duration tasks in space.

Linares is also exploring artificial intelligence, including ways that a satellite can autonomously “learn” from its experience and safely adapt to uncertain environments.

“Imagine if each satellite had a virtual Doc Draper onboard that could do the de-bugging that we did from the ground during the Apollo missions,” Linares says. “That way, satellites would become instantaneously more robust. And it’s not taking the human out of the equation. It’s allowing the human to be amplified. I think that’s within reach.”

Professor Michael Laub and MIT alumni named 2025 AAAS Fellows

School of Science — Fri, 17 Apr 2026 14:15:00 -0400

MIT Professor Michael T. Laub as well as 21 MIT alumni have been elected as fellows of the American Association for the Advancement of Science (AAAS).

The 2025 class of AAAS Fellows includes 449 scientists, engineers, and innovators, spanning all 24 of AAAS disciplinary sections, who are recognized for their scientific achievements.

Laub, the Salvador E. Luria Professor in the MIT Department of Biology and an HHMI Investigator, studies the biological mechanisms and evolution of how cells process information to regulate their own growth and proliferation, using bacteria as a model organism to develop a deeper, fundamental understanding of how bacteria function and evolve. Laub was honored as a AAAS Fellow for distinguished contributions to the field of bacterial information processing, particularly to the understanding of coevolution of host-pathogen response and immunity.

“This year’s AAAS Fellows have demonstrated research excellence, made notable contributions to advance science, and delivered important services to their communities,” said Sudip S. Parikh, AAAS chief executive officer and executive publisher of the Science family of journals. “These fellows and their accomplishments validate the importance of investing in science and technology for the benefit of all.”

The following alumni were also named fellows of the AAAS:

Debra Auguste ’99
Julie Claycomb PhD ’04
Chris Clifton ’85, SM ’86
Kevin Crowston PhD ’91
Maitreya Dunham ’99
David Fike PhD ’07
Jianping Fu PhD ’07
Peter A. Gilman SM ’64, PhD ’66
Diane M. Harper ’80, SM ’82
Cherie R. Kagan PhD ’96
Elizabeth A. Kensinger PhD ’03
Kenro Kusumi PhD ’97
Charla Lambert ’96
Bennett A. Landman ’01, MNG ’02
Michael E. Matheny SM ’06
Paul David Ronney ScD ’83
Steven Semken ’80, PhD ’89
Sudipta Sengupta SM ’99, PhD ’06
Lawrence R. Sita PhD ’86
Jan M. Skotheim ’99
Beverly Park Woolf ’66

Why bother with plausible deniability?

Peter Dizikes | MIT News — Fri, 17 Apr 2026 11:00:00 -0400

Picture this scenario in a business: An employee, Brad, disclosed some information that wound up in the hands of a competitor. He may not have meant to, but he did, and a few people at the firm know this. So, at the next company meeting, another employee, Linda, looks pointedly at Brad and says, “I know that no one would ever dream of leaking information, intentionally or otherwise, from our discussions.”

Linda means the opposite of what she says, of course. She is letting people know that Brad is to blame. However, while Linda is making her message public, she also wants what we often call “plausible deniability” for her statement. If anyone asks later if she was insinuating anything about Brad, she can claim she was just making a general comment about the firm.

From the boardroom to the courtroom, the talk show, and beyond, people frequently seek plausible deniability for their statements. It seems to work, too. Indeed, to have plausible deniability, the denial need not be plausible.

“People can say, ‘That’s not what I meant,’ and completely get away with it, even though it’s totally obvious they’re lying,” says MIT philosopher Sam Berstler. “They wouldn’t be getting away with it in the same respect by putting the content in explicit words.”

She adds: “This should be very puzzling to us, because in both cases the intent is maximally obvious.”

So why does plausible deniability work, and work like this? And what does it tell us about how we interact? Berstler, who studies language and communication, has published a new paper on plausible deniability, examining these issues. It is part of a larger body of work Berstler is generating, focused on everyday interactions involving deception.

To understand plausible deniability, Berstler thinks we should recognize that our conversations cannot be understood simply by analyzing the words we use. Our interactions always take place in social contexts, often have a performative aspect, and occasionally intersect with “non-acknowedgement norms,” the practice of keeping quiet about what we all know. Plausible deniability is bound up with social practices that incentivize us to not be fully transparent.

“A lot of indirect speech is designed, as it were, to facilitate this kind of deniability,” Berstler says.

The paper, “Non-Epistemic Deniability,” is published in the journal MIND. Berstler, the Laurance S. Rockefeller Career Development Chair and assistant professor of philosophy at MIT, is the sole author.

Managing a personal “Cold War”

In Berstler’s view, there are multiple ways to create plausible deniability. One is through the practice of open secrets, the subject of one of her previous papers. An open secret is widely known information that is never acknowledged, for reasons of power or in-group identification, among other things. Indeed, no one even acknowledges that they are not acknowledging the open secret.

Examining open secrets led Berstler directly to her analysis of plausible deniability. However, the new paper focuses more on another way of creating plausible deniability, which she calls “two-tracking norms.” Two-tracking is when a group divides its communications into two parts: One track consists of official, limited, courteous interaction, and the second track consists more of informal, resentful, uncooperative interactions. Linda, in our example, is engaging in two-tracking.

But why do we two-track at all? Why not just be fully transparent? Well, in an office scenario, if Linda is mad that Brad divulged some company secrets, calling out Brad directly might lead to recriminations and conflict beyond what Linda is willing to tolerate for the sake of critizing Brad on the record.

“It's like a Cold War situation where we each have an interest in not letting the conflict go to a state where we’re firing warheads at each other, but we can’t just purely manage relations around the negotiating table because we’re adversaries,” Berstler says. “We’re going to aggress against each other, but in a limited way. In a two-track conversation, communicating in the second track is like fighting a proxy battle, but we’re also providing evidence to each other that we’re only going to engage in a proxy battle.”

In this way, Linda takes Brad to task and some people pick up on it, but Brad is not explicitly publicly shamed. And though he might be unhappy, he is less likely to wreck all company norms in an attempt to retaliate. The firm more or less rolls on as usual.

Waiting for Goffman

Where Berstler differs in part from other philosophers is in her emphasis on the extent to which social practices are integral to our ways of deploying deniability. Our interactions are not just limited to rhetoric, but have additional layers.

“What we mean can often be different from what we say, or enhanced from what we say,” Berstler says. “Sometimes we figure out what others mean by relying on what they say in literal language. But sometimes we’re relying on other things, like the context.”

So, back at the firm, the colleagues of Linda and Brad might have some knowledge of a confidentiality breach, or they might know that Linda does not usually speak up at meetings, or they might read things into her tone of voice and the way she appeared to look at Brad. There is more to be gleaned than her literal words.

In this kind of analysis, Berstler finds illumination in the work of the midcentury sociologist Erving Goffman, who studied in minute detail the performative parts of our everyday interactions and speech. Goffman, as Berstler notes in the paper, proposed that we have a ritualized, social self (or “face”) and that normal, everyday behavior generally allows us, and others, to keep this face intact.

Relatedly, Goffman and some of his intellectual followers concluded that habits such as two-tracking are very common in everyday life; the price we pay for saving face is a bit less transparency, and a bit more secrecy and deniability.

“What I’m suggesting is we have these other established practices like two-tracking and open secrecy, where the deniability is just a byproduct,” Berstler says.

What’s the solution?

By bringing sociological ideas into her work, Berstler is moving beyond the normal philosophical discussion of the subject. On the other hand, she is not directly disputing core ideas in linguistics or the philosophy of language; she is just suggesting we add another layer to our analysis of communication and meaning.

Digging into issues of plausible deniability also raises the question of what to do about it. There may be something pernicious in the practice, but calling out plausible deniability threatens to dismantle our social guardrails and break the “Cold War” norms used to help people co-exist.

Berstler, though, has another suggestion: Instead of calling out such subterfuge, we can become verbally and performatively skilled enough to counteract it.

“I think the actual answer is becoming rhetorically clever,” Berstler says. “It’s being the person who uses indirect speech to respond strategically, without violating these norms. That is possible. It also means you have agency. You could become very good at verbal sparring.”

Besides, Berstler says, “Often that can be more powerful than just calling them out, and demonstrates your own verbal fluency. I think we admire it when we see it. Conversational skill is an important component of being morally good, in these cases by reprimanding someone in a way that’s not going to be counterproductive.”

She adds: “People who buy into the rhetoric of transparency can be setting back their own interests. Maybe speaking transparently is morally virtuous in some respects, but given the reality of our speech practices, transparency is not necessarily going to be the most effective way of handling things.”

Jacob Andreas and Brett McGuire named Edgerton Award winners

Fri, 17 Apr 2026 09:40:00 -0400

MIT Associate Professor Jacob Andreas of the Department of Electrical Engineering and Computer Science [EECS] and MIT Associate Professor Brett McGuire of the Department of Chemistry have been selected as the winners of the 2026 Harold E. Edgerton Faculty Achievement Award. Established in 1982 as a permanent tribute to Institute Professor Emeritus Harold E. Edgerton’s great and enduring support for younger faculty members, this award is given annually in recognition of exceptional distinction in teaching, research, and service.

“The Department of Chemistry is extremely delighted to see Brett recognized for science that has changed how we think about carbon in space,” says Class of 1942 Professor of Chemistry and Department Head Matthew D. Shoulders. “Brett’s lab combines laboratory spectroscopy, radio astronomy, and sophisticated signal-analysis methods to pull definitive molecular fingerprints out of extraordinarily faint data. His discovery of polycyclic aromatic hydrocarbons in the cold interstellar medium has opened a powerful new window on astrochemistry. Moreover, Brett is inventing the creative and unique tools that make discoveries like this possible.”

“Jacob Andreas represents the very best of MIT EECS” says Asu Ozdaglar, EECS department head. “He is an innovative researcher whose work combines computational and linguistically informed approaches to build foundations of language learning. He is an extraordinary educator who has brought these forefront ideas into our core classes in natural language processing and machine learning. His ability to bridge foundational theory with real-world impact, while also advancing the social and ethical dimensions of computing, makes him truly deserving of the Edgerton Faculty Achievement Award.”

Andreas joined the MIT faculty in July 2019, and is affiliated with the Computer Science and Artificial Intelligence Laboratory. His work is in natural language processing (NLP), and more broadly in AI. He aims to understand the computational foundations of language learning, and to build intelligent systems that can learn from human guidance. Among other honors, Andreas has received Samsung’s AI Researcher of the Year award, MIT’s Kolokotrones and Junior Bose teaching awards, a 2024 Sloan Research Fellow award, and paper awards at the International Conference on Machine Learning and the Association for Computational Linguistics.

Andreas received his BS from Columbia University, his MPhil from Cambridge University (where he studied as a Churchill scholar), and his PhD in natural language processing from the University of California at Berkeley. His work in natural language processing has taken on thorny problems in the capability gap between humans and computers. “The defining feature of human language use is our capacity for compositional generalization,” explains Antonio Torralba, Delta Electronics Professor and faculty head of Artificial Intelligence and Decision-Making in the Department of EECS. “Many of the core challenges in natural language processing is addressed by simply training larger and larger neural models, but this kind of compositional generalization remains a persistent difficulty, and without the ability to generalize compositionally, the deep learning toolkit will never be robust enough for the most challenging real-world NLP tasks. Jacob’s work on compositional modeling draws new connections between NLP and work in computer vision and physics aimed at modeling systems governed by symmetries and other algebraic structures and, using them, they have been able to build NLP models exhibiting a number of new, human-like language acquisition behaviors, including one-shot word learning, learning via mutual exclusivity constraints, and learning of grammatical rules in extremely low-resource settings.”

Within EECS, Andreas has developed multiple advanced courses in natural language processing, as well as new exercises designed to get students to grapple with important social and ethical considerations in machine learning deployment. “Jacob has taken a leading role in completely modernizing and extending our course offerings in natural language processing,” says award nominator Leslie Pack Kaelbling, Panasonic Professor in the Department of EECS. “He has led the development of a modern two-course sequence, which is a cornerstone of the new AI+D [artificial intelligence and decision-making] major, routinely enrolling several hundred students each semester. His command of the area is broad and deep, and his classes integrate classical structural understanding of language with the most modern learning-based approaches. He has put MIT EECS on the worldwide map as a place to study natural language at every level.”

Brett McGuire joined the MIT faculty in 2020 and was promoted to associate professor in 2025. His research operates at the intersection of physical chemistry, molecular spectroscopy, and observational astrophysics, where he seeks to uncover how the chemical building blocks of life evolve alongside and help shape the birth of stars and planets. A former Jansky Fellow and then Hubble Postdoctoral Fellow at the National Radio Astronomy Observatory, McGuire has a BS in chemistry from the University of Illinois and a PhD in physical chemistry from Caltech. His honors include a 2026 Sloan Fellowship, the Beckman Young Investigator Award, the Helen B. Warner Prize for Astronomy, and the MIT Award for Teaching with Digital Technology.

The faculty who nominated McGuire for this award praised his extraordinary public outreach, his immediate willingness to take on teaching class 5.111 (Principles of Chemical Science), a General Institute Requirement (GIR) course comprised of 150–500 students, and his service to both the MIT and astrochemical communities.

“Brett is at the very top of astrochemical scientists in his age group due to his discovery of fused carbon ring compounds in the cold region of the ISM [interstellar medium], an observation that provides a route for carbon incorporation in planets,” says Sylvia Ceyer, the John C. Sheehan Professor of Chemistry in her nomination statement. “His extensive involvement in service-oriented activities within the astrochemical/physical community is highly unusual for a junior scientist, and is testament to the value that the astronomical community places in his wisdom and judgement. His phenomenal organizational skills have made his contributions to graduate admission protocols and seminar administration at MIT the envy of the department. And most importantly, Brett is a superb teacher, who cares deeply about students’ understanding and success, not only in his course, but in their future endeavors.”

“As an assistant professor, Brett volunteered to teach 5.111, a large GIR course with 150–500 students, and has received some of the best teaching evaluations among all faculty who have led the subject,” says Mei Hong, the David A. Leighty Professor of Chemistry. “He has a natural talent in explaining abstract physical chemistry concepts in an engaging manner. His slides, which he prepared from scratch instead of modifying from previous years’ material from other professors, are clear, and … the combination of lucid explanation and humor has generated great enthusiasm and interest in chemistry among students.”

Subject evaluations from McGuire’s courses praised his humor, the clarity of his explanations, and his ability to transform a lecture into a “science show.” “I haven't felt this sort of desire for the depth of understanding in a subject beyond just a straight grade [in some time],” says one student. “Brett definitely stimulated that love of learning for me.”

“Brett is an outstanding faculty member who is dedicated to fostering student learning and success,” says Jennifer Weisman, assistant director of academic programs in chemistry. “He is thoughtful, caring, and goes above and beyond to help his colleagues, students, and staff.”

“I’m thrilled to be selected for the Edgerton Award this year,” says McGuire. “The award is nominally for teaching, research, and service; MIT and the chemistry department in particular have been an incredible place to learn and grow in all these areas. I’m incredibly grateful for the mentorship, enthusiasm, and support I have received from my colleagues, from my students both in the lab and in the classroom, and from the MIT community during my time here. I look forward to many more years of exciting discovery together with this one-of-a-kind community.”

Bringing AI-driven protein-design tools to biologists everywhere

Zach Winn | MIT News — Fri, 17 Apr 2026 00:00:00 -0400

Artificial intelligence is already proving it can accelerate drug development and improve our understanding of disease. But to turn AI into novel treatments we need to get the latest, most powerful models into the hands of scientists.

The problem is that most scientists aren’t machine-learning experts. Now the company OpenProtein.AI is helping scientists stay on the cutting edge of AI with a no-code platform that gives them access to powerful foundation models and a suite of tools for designing proteins, predicting protein structure and function, and training models.

The company, founded by Tristan Bepler PhD ’20 and former MIT associate professor Tim Lu PhD ’07, is already equipping researchers in pharmaceutical and biotech companies of all sizes with its tools, including internally developed foundation models for protein engineering. OpenProtein.AI also offers its platform to scientists in academia for free.

“It’s a really exciting time right now because these models can not only make protein engineering more efficient — which shortens development cycles for therapeutics and industrial uses — they can also enhance our ability to design new proteins with specific traits,” Bepler says. “We’re also thinking about applying these approaches to non-protein modalities. The big picture is we’re creating a language for describing biological systems.”

Advancing biology with AI

Bepler came to MIT in 2014 as part of the Computational and Systems Biology PhD Program, studying under Bonnie Berger, MIT’s Simons Professor of Applied Mathematics. It was there that he realized how little we understand about the molecules that make up the building blocks of biology.

“We hadn’t characterized biomolecules and proteins well enough to create good predictive models of what, say, a whole genome circuit will do, or how a protein interaction network will behave,” Bepler recalls. “It got me interested in understanding proteins at a more fine-grained level.”

Bepler began exploring ways to predict the chains of amino acids that make up proteins by analyzing evolutionary data. This was before Google released AlphaFold, a powerful prediction model for protein structure. The work led to one of the first generative AI models for understanding and designing proteins — what the team calls a protein language model.

“I was really excited about the classical framework of proteins and the relationships between their sequence, structure, and function. We don’t understand those links well,” Bepler says. “So how could we use these foundation models to skip the ‘structure’ component and go straight from sequence to function?”

After earning his PhD in 2020, Bepler entered Lu’s lab in MIT’s Department of Biological Engineering as a postdoc.

“This was around the time when the idea of integrating AI with biology was starting to pick up,” Lu recalls. “Tristan helped us build better computational models for biologic design. We also realized there’s a disconnect between the most cutting-edge tools available and the biologists, who would love to use these things but don’t know how to code. OpenProtein came from the idea of broadening access to these tools.”

Bepler had worked at the forefront of AI as part of his PhD. He knew the technology could help scientists accelerate their work.

“We started with the idea to build a general-purpose platform for doing machine learning-in-the-loop protein engineering,” Bepler says. “We wanted to build something that was user friendly because machine-learning ideas are kind of esoteric. They require implementation, GPUs, fine-tuning, designing libraries of sequences. Especially at that time, it was a lot for biologists to learn.”

OpenProtein’s platform, in contrast, features an intuitive web interface for biologists to upload data and conduct protein engineering work with machine learning. It features a range of open-source models, including PoET, OpenProtein’s flagship protein language model.

PoET, short for Protein Evolutionary Transformer, was trained on protein groups to generate sets of related proteins. Bepler and his collaborators showed it could generalize about evolutionary constraints on proteins and incorporate new information on protein sequences without retraining, allowing other researchers to add experimental data to improve the model.

“Researchers can use their own data to train models and optimize protein sequences, and then they can use our other tools to analyze those proteins,” Bepler says. “People are generating libraries of protein sequences in silico [on computers] and then running them through predictive models to get validation and structural predictors. It’s basically a no-code front-end, but we also have APIs for people who want to access it with code.”

The models help researchers design proteins faster, then decide which ones are promising enough for further lab testing. Researchers can also input proteins of interest, and the models can generate new ones with similar properties.

Since its founding, OpenProtein’s team has continued to add tools to its platform for researchers regardless of their lab size or resources.

“We’ve tried really hard to make the platform an open-ended toolbox,” Bepler says. “It has specific workflows, but it’s not tied specifically to one protein function or class of proteins. One of the great things about these models is they are very good at understanding proteins broadly. They learn about the whole space of possible proteins.”

Enabling the next generation of therapies

The large pharmaceutical company Boehringer Ingelheim began using OpenProtein’s platform in early 2025. Recently, the companies announced an expanded collaboration that will see OpenProtein’s platform and models embedded into Boehringer Ingelheim’s work as it engineers proteins to treat diseases like cancer and autoimmune or inflammatory conditions.

Last year, OpenProtein also released a new version of its protein language model, PoET-2, that outperforms much larger models while using a small fraction of the computing resources and experimental data.

“We really want to solve the question of how we describe proteins,” Bepler says. “What’s the meaningful, domain-specific language of protein constraints we use as we generate them? How can we bring in more evolutionary constraints? How can we describe an enzymatic reaction a protein carries out such that a model can generate sequences to do that reaction?”

Moving forward, the founders are hoping to make models that factor in the changing, interconnected nature of protein function.

“The area I am excited about is going beyond protein binding events to use these models to predict and design dynamic features, where the protein has to engage two, three, or four biological mechanisms at the same time, or change its function after binding,” says Lu, who currently serves in an advisory role for the company.

As progress in AI races forward, OpenProtein continues to see its mission as giving scientists the best tools to develop new treatments faster.

“As work gets more complex, with approaches incorporating things like protein logic and dynamic therapies, the existing experimental toolsets become limiting,” Lu says. “It’s really important to create open ecosystems around AI and biology. There’s a risk that AI resources could get so concentrated that the average researcher can’t use them. Open access is super important for the scientific field to make progress.”

A regulatory loophole could delay ozone recovery by years

Zach Winn | MIT News — Thu, 16 Apr 2026 05:00:00 -0400

Often hailed as the most successful international environmental agreement of all time, the 1987 Montreal Protocol continues to successfully phase out the global production of chemicals that were creating a growing hole in the ozone layer, causing skin cancer and other adverse health effects.

MIT-led studies have since shown the subsequent reduction in ozone-depleting substances is helping stratospheric ozone to recover. (It could return to 1980 levels by as early as 2040, according to some estimates.) But the Montreal Protocol made an exception in its rules for the use of ozone-depleting substances as feedstocks in the production of other materials. That’s because it was thought that only a small amount — just 0.5 percent — of the ozone-depleting substances used for this purpose would leak into the atmosphere.

In recent years, however, scientists have observed more ozone-depleting substances in the atmosphere than expected, and have increased their estimates of leakage from feedstocks.

Now an international group of scientists, including researchers from MIT, has calculated the impact of different feedstock leakage rates on the ozone’s fragile recovery. They find the higher leakage rates, if not addressed by the Montreal Protocol, could delay ozone recovery by about seven years.

“We’ve realized in the last few years that these feedstock chemicals are a bug in the system,” says author Susan Solomon, the Lee and Geraldine Martin Professor of Environmental Studies and Chemistry, who was part of the original research team that linked the chemicals to the ozone hole. “Production of ozone-depleting substances has pretty much ceased around the world except for this one use, which is when you have a chemical you convert into something else.”

The paper, which was published in Nature Communications today, is the first to comprehensively quantify the impact of leaked feedstocks, which are currently used to make plastics and nonstick chemicals. They are also used to make substitute chemicals for the ones regulated under the Montreal Protocol. The researchers say it shows the importance of curbing use and preventing leakage of such feedstocks, especially as the production of their end products, like plastic, is projected to grow.

“We’ve gotten to the point where, if we want the protocol to be as successful in the future as it has been in the past, the parties really need to think about how to tighten up the emissions of these industrial processes,” says first author Stefan Reimann of the Swiss Federal Laboratories for Materials Science and Technology.

“To me, it’s only fair, because so many other things have already been completely discontinued. So why should this exemption exist if it’s going to be damaging?” says Solomon.

Joining Reimann on the paper are his colleagues Martin K. Vollmer and Lukas Emmenegger; Luke Western and Susan Solomon of the MIT Center for Sustainability Science and Strategy and the Department of Earth, Atmospheric and Planetary Sciences; David Sherry of Nolan-Sherry and Associates Ltd; Megan Lickley of Georgetown University; Lambert Kuijpers of the A/gent Consultancy b.v.; Stephen A. Montzka and John Daniel of the National Oceanic and Atmospheric Administration; Matthew Rigby of the University of Bristol; Guus J.M. Velders of Utrecht University; Qing Liang of the NASA Goddard Space Flight Center; and Sunyoung Park of Kyungpook National University.

Repairing the ozone

In 1985, scientists discovered a growing hole in the ozone layer over Antarctica that was allowing more of the sun’s harmful ultraviolet radiation to reach Earth’s surface. The following year, researchers including Solomon traveled to Antarctica and discovered the cause of the ozone deterioration: a class of chemicals called chlorofluorocarbons, or CFCs, which were then used in refrigeration, air conditioning, and aerosols.

The revelations led to the Montreal Protocol, an international treaty involving 197 countries and the European Union restricting the use of CFCs. The subsequent decision to exempt the use of ozone-depleting substances for use as feedstocks was based partially on industry estimates of how much of their feedstocks leaked.

“It was thought that the emissions of these substances as a feedstock were minor compared to things like refrigerants and foams,” Western says. “It was also believed that leakage from these sources was minor — around half a percent of what went in — because people would essentially be leaking their profits if their feedstocks were released into the atmosphere.”

Unfortunately, some of those assumptions are no longer true. Western and Reimann are part of the Advanced Global Atmospheric Gases Experiment (AGAGE), a global monitoring network co-founded by Ronald Prinn, MIT’s TEPCO Professor of Atmospheric Science. AGAGE monitors emissions of ozone-depleting substances around the world, and in recent years researchers have revised their estimates of feedstock leakage upwards, to about 3.6 percent. For some chemicals, the number was even higher.

In the new paper, the researchers estimated a 3.6 percent feedstock leakage as the baseline for most chemicals. They compared that with a scenario where 0.5 percent of feedstocks are leaked from 2025 onward and a scenario with zero feedstock-related emissions. The researchers also looked at production trends between 2014 and 2024 to project how much of each specific ozone-depleting chemical would be used as feedstock between 2025 and 2100.

The analysis shows that until 2050, total ozone-depleting chemical emissions decrease in all scenarios as rising feedstock emissions are offset by declining uses enforced by the Montreal Protocol. In the scenario with continued 3.6 percent leakage, however, emissions level off around 2045, and total emissions only decrease by 50 percent overall by 2100.

The researchers then evaluated the impact of feedstock-related emissions on stratospheric ozone depletion. In the scenario where feedstock leakage is 0.5 percent, the ozone returns to its 1980 status by 2066. In the scenario with zero feedstock leakage, the ozone reclaims its 1980 health in 2065. But in the baseline scenario, the recovery is delayed about seven years, to 2073.

“This paper sends an important message that these emissions are too high and we have to find a way to reduce them,” Reimann says. “Either that means no longer using these substances as feedstocks, swapping out chemicals, or reducing the leakage emissions when they are used.”

A global response

Solomon is confident industries will be able to adjust to the latest findings.

“There are a lot of innovators in the chemical industry,” Solomon says. “They make new chemicals and improve chemicals for a living. It’s true they can perhaps get too entrenched with certain chemicals, but it doesn’t happen that often. Actually, they’re usually quite willing to consider alternatives. There are thousands of other chemicals that could be used instead, so why not switch? That’s been the attitude.”

Solomon says the fact that AGAGE can detect the impact of feedstock emissions is a testament to the progress the world has made in reducing emissions from other sources up to this point. She believes raising awareness of the feedstock problem is the first step.

“This isn’t the first time that the AGAGE Network has made measurements that have allowed the world to see we need to do a little better here or there,” Western says. “Often, it’s just a mistake. Sometimes all it takes is making people more aware of these things to tighten up some processes.”

Members of the Montreal Protocol meet every year. In those meetings, they split into working groups around different topics. Feedstock emissions are already one of those topics, so participants will review the evidence together. Typically, they release a statement about mitigation strategies if needed.

“We wanted to raise the warning flag that something is wrong here,” Reimann says. “We could reduce the period of ozone depletion by years. It might not sound like a long time, but if you could count the skin cancer cases you’d avoid in that time, it would seem quite significant.”

The work was supported, in part, by the U.S. National Science Foundation, the U.S. National Aeronautics and Space Administration (NASA), the Swiss Federal Office for the Environment, the VoLo Foundation, the United Kingdom Natural Environment Research Council, and the Korea Meteorological Administration Research and Development Program.

Youth may increase vulnerability to a carcinogen found in contaminated water and some drugs

Anne Trafton | MIT News — Thu, 16 Apr 2026 00:00:00 -0400

A new study from MIT suggests that a carcinogen that has been found in medications and in drinking water contaminated by chemical plants may have a much more severe impact on children than adults.

In a study of mice, the researchers found that juveniles exposed to drinking water containing this compound, known as NDMA, showed dramatically higher rates of DNA damage and cancer than adults.

The findings may help to explain an epidemiological association between childhood cancer and prenatal exposure to NDMA in people living near a contaminated site in Wilmington, Massachusetts, the researchers say. The study also suggests that it is critical to evaluate the impact of potential carcinogens across all ages.

“We really hope that groups that do safety testing will change their paradigm and start looking at young animals, so that we can catch potential carcinogens before people are exposed,” says Bevin Engelward, an MIT professor of biological engineering. “As a solution to cancer, cancer prevention is clearly much better than cancer treatment, so we hope we can spot dangerous chemicals before people are exposed, and therefore prevent extensive cancer risk.”

MIT postdoc Lindsay Volk is the lead author of the paper. Engelward is the senior author of the study, which appears in Nature Communications.

From DNA damage to cancer

NDMA (N-Nitrosodimethylamine) can be generated as a byproduct of many industrial chemical processes, and it is also found in cigarette smoke and processed meats. In recent years, NDMA has been detected in some formulations of the drugs valsartan, ranitidine, and metformin. It was also found in drinking water in Wilmington, Massachusetts, in the 1990s, as a result of contamination from the Olin Chemical site.

In 2021, a study from the Massachusetts Department of Health suggested a link between that water contamination and an elevated incidence of childhood cancer in Wilmington. Between 1990 and 2000, 22 Wilmington children were diagnosed with cancer. The contaminated wells were closed in 2003.

Also in 2021, Engelward and others at MIT published a study on the mechanism of how NDMA can lead to cancer. In the new Nature Communications paper, Engelward and her colleagues set out to see if they could determine why the compound appears to affect children more than adults.

Most studies that evaluate potential carcinogens are performed in mice that are at least 4 to 6 weeks old, and often older. For this study, the researchers studied two groups of mice — one 3 weeks old (juvenile), and one 3 months old (adult). Each group was given drinking water with low levels of NDMA, about five parts per million, for two weeks.

Inside the body, NDMA is metabolized by a liver enzyme called CYP2E1. This produces toxic metabolites that can damage DNA by adding a small chemical group known as a methyl group to DNA bases, creating lesions known as adducts.

When the researchers examined the livers of the mice, they found that juveniles and adults showed similar levels of DNA adducts. However, there were dramatic differences in what happened after that initial damage. In juvenile mice, DNA adducts led to significant accumulation of double-stranded DNA breaks, which occur when cells try to repair adducts. These breaks produce mutations that eventually lead to the development of liver cancer.

In the adult mice, the researchers saw essentially no double-stranded breaks and significantly fewer mutations compared to juveniles. Furthermore, the livers did not develop severe pathology, including tumors, even though they experienced the same initial level of DNA adducts.

“The initial structural changes to the DNA had very different consequences depending on age,” Engelward says. “The double-stranded breaks were exclusively observed in the young.”

Further experiments revealed that these differences stem from differences in the rates of cell proliferation. Cells in the juvenile liver divide rapidly, giving them more opportunity to turn DNA adducts into mutations, while cells of the adult liver rarely divide.

“This really emphasizes the overall problem that we’re trying to highlight in the paper,” Volk says. “With toxicological studies, oftentimes the standard is to use fully grown mice. At that point, they’re already slowing down cell division, so if we are testing the harmful effects of NDMA in adult mice, then we’re completely missing how vulnerable particular groups are, such as younger animals.”

While most of these effects were seen in the liver, because that is where NDMA is metabolized, a few of the mice developed other types of cancer, including lung cancer and lymphoma.

Adult risk is not zero

For most of these studies, the researchers used mice that had two of their DNA repair systems knocked out. This speeds up the mutation process, allowing the researchers to see the effects of NDMA exposure more easily, without needing to study a large population of mice.

However, a small study in mice with normal DNA repair showed that juveniles experienced NDMA-induced double-strand breaks, regenerative proliferation, and large-scale mutations that were completely absent in adults. This occurs because the fast-growing juveniles possess highly active DNA replication machinery that encounters the DNA adducts before the cell has time to repair them.

The researchers also found that if they treated adult mice with thyroid hormone, which stimulates proliferation of liver cells, those cells began accumulating mutations as quickly as the juvenile liver cells. Previous work done in the Engelward laboratory has shown that inflammation can also stimulate cell proliferation-driven vulnerability to DNA damage, so the findings of this study suggest that anything that causes liver inflammation could make the adult liver more vulnerable to damage caused by agents such as NDMA.

“We certainly don’t want to say that adults are completely resistant to NDMA,” Volk says. “Everything impacts your susceptibility to a carcinogen, whether that’s your genetics, your age, your diet, and so forth. In adults, if they have a viral infection, or a high fat diet, or chronic binge alcohol drinking, this can impact proliferation within the liver and potentially make them susceptible to NDMA.”

The researchers are now investigating how a high-fat diet might influence cancer development in mice that also have exposure to NDMA.

This collaborative effort across several MIT labs was funded by the National Institutes of Environmental and Health Sciences (NIEHS) Superfund Research Program, a NIEHS Core Center Grant, a National Institutes of Health Training Grant, and the Anonymous Fund for Climate Action.

MIT study reveals a new role for cell membranes

Anne Trafton | MIT News — Thu, 16 Apr 2026 00:00:00 -0400

Cells are enveloped by a lipid membrane that gives them structure and provides a barrier between the cell and its environment. However, evidence has recently emerged suggesting that these membranes do more than simply provide protection — they also influence the behavior of the protein receptors embedded in them.

A new study from MIT chemists adds further support to that idea. The researchers found that changing the composition of the cell membrane can alter the function of a membrane receptor that promotes proliferation.

Epidermal growth factor receptor (EGFR) can be locked into an overactive state when the cell membrane has a higher than normal concentration of negatively charged lipids, the researchers found. This may help to explain why cancer cells with high levels of those lipids enter a highly proliferative state that allows them to divide uncontrollably.

“The longstanding dogma of what a membrane does is that it’s just a scaffold, an organizational structure. However, there have been increasing observations that suggest that maybe these membrane lipids are actually playing a role in receptor function,” says Gabriela Schlau-Cohen, the Robert T. Haslam and Bradley Dewey Professor of Chemistry at MIT and the senior author of the study.

The findings open up the possibility of discovering new ways to treat tumors by neutralizing the negative charge, which might turn down EGFR signaling, she adds.

Shwetha Srinivasan PhD ’22 is the lead author of the paper, which appears in the journal eLife. Other authors include former MIT postdocs Xingcheng Lin and Raju Regmi, Xuyan Chen PhD ’25, and Bin Zhang, an associate professor of chemistry at MIT.

Receptor dynamics

The EGF receptor, which is found on cells that line body surfaces and organs, is one of many receptors that help control cell growth. Some types of cancer, especially lung cancer and glioblastoma, overexpress the EGF receptor, which can lead to uncontrolled growth.

Like most receptor proteins, EGFR spans the entire cell membrane. Until recently, it has been challenging to study how signals are conveyed across the entire receptor, because of the difficulty of creating membranes that have proteins going all the way through them and then studying both ends of those proteins.

To make it easier to study these signaling processes, Schlau-Cohen’s lab uses nanodiscs, a special type of self-assembling membrane that mimics the cell membrane. When making these discs, the researchers can embed receptors in them, allowing the team to study the function of the full-length receptor.

Using a technique called single molecule FRET (fluorescence resonance energy transfer), the researchers can study how the shape of the receptor changes under different conditions. Single molecule FRET allows them to measure the distance between different parts of the protein by labeling them with fluorescent tags and then measuring how fast energy travels between the tags.

In previous work, Schlau-Cohen and Zhang used single molecule FRET and molecular dynamics simulations to reveal what happens when EGFR binds to EGF. They found that this binding causes the transmembrane section of the receptor to change shape, and that shape-shift triggers the section of the receptor that extends inside the cell to activate cellular machinery that stimulates growth.

Stuck in an overactive state

In the new study, the researchers used a similar approach to investigate how altering the composition of the membrane affects the function of the receptor. First, they explored how elevated levels of negatively charged lipids would affect the cell membrane and EGFR function.

Normally, about 15 percent of the cell membrane is made up of negatively charged lipids. The researchers found that membranes with negatively charged lipids in the range of 15 to 30 percent behaved normally, but if that level reached 60 percent, then the EGFR receptor would become locked into an active state.

In that state, the pro-growth signaling pathway is turned on all the time, even when no EGF is bound to the receptor. Many cancer cells show increased levels of these lipids, and this mechanism could help to explain why those cells are able to grow unchecked, Schlau-Cohen says.

“If the membrane has high levels of negatively charged lipids, then it’s always in that open conformation. It doesn’t matter if ligand is bound or unbound,” she says. “It’s always in the conformation that’s telling the cell to grow, not just when EGF binds.”

The researchers also used this system to explore the role of cholesterol in EGFR function. When the researchers created nanodiscs with elevated cholesterol levels, they found that the membranes became more rigid, and this rigidity suppressed EGFR signaling.

The research was funded by the National Institutes of Health and MIT’s Department of Chemistry.

Waves hit different on other planets

Jennifer Chu | MIT News — Thu, 16 Apr 2026 00:00:00 -0400

On a calm day, a light breeze might barely ripple the surface of a lake on Earth. But on Saturn’s largest moon Titan, a similar mild wind would kick up 10-foot-tall waves.

This otherworldly behavior is one prediction from a new wave model developed by scientists at MIT. The model is the first to capture the full dynamics of waves and what it takes to whip them up under different planetary conditions.

In a study published in the Journal of Geophysical Research: Planets, the MIT team introduces the model, which they’ve aptly coined “PlanetWaves.” They apply the model to predict how waves behave on planetary bodies that might host liquid lakes and oceans, including Titan, ancient Mars, and three planets beyond the solar system.

The model predicts that a gentle wind would be enough to stir up huge waves on Titan, where lakes are filled with light liquid hydrocarbons. In contrast, it would take hurricane-force winds to barely move the surface of a lake on the exoplanet 55-Cancri e, which is thought to be a lava world covered in hot, dense liquid rock.

“On Earth, we get accustomed to certain wave dynamics,” says study author Andrew Ashton, associate scientist at the Woods Hole Oceanographic Institution (WHOI) and faculty member of the MIT-WHOI Joint Program. “But with this model, we can see how waves behave on planets with different liquids, atmospheres, and gravity, which can kind of challenge our intuition.”

The team is particularly keen to understand how waves form on Titan. The large moon is the only other planetary body in the solar system other than the Earth that is known to currently host liquid lakes.

“Anywhere there’s a liquid surface with wind moving over it, there’s potential to make waves,” says Taylor Perron, the Cecil and Ida Green Professor of Earth, Atmospheric and Planetary Sciences at MIT. “For Titan, the tantalizing thing is that we don’t have any direct observation of what these lakes look like. So we don’t know for sure what kind of waves might exist there. Now this model gives us an idea.”

If humans were to one day to send a probe to Titan’s lakes, the team’s new model could inform the design of wave-resilient spacecraft.

“You would want to build something that can withstand the energy of the waves,” says lead author Una Schneck, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “So it’s important to know what kind of waves these instruments would be up against.”

The study’s co-authors include Charlene Detelich and Alexander Hayes of Cornell University and Milan Curcic of the University of Miami.

“The first puff”

When wind blows over water, it creates waves that can be strong enough to carve out coastlines and redistribute sediment brought to the coast by rivers. Through this process, waves can be a significant force in shaping a landscape over time. Schneck and her colleagues, who study landscape evolution on Earth and other planets, wondered how waves might behave on other worlds where gravity, atmospheric conditions, and liquid compositions can be very different from what is found on Earth.

“There have been attempts in the past to predict how gravity will affect waves on other planets,” Schneck says. “But they don’t quantify other factors such as the composition of the liquid that is making waves. That was the big leap with this project.”

She and her colleagues developed a full wave model that takes into account not just a planet’s gravity, but also properties of its surface liquid, such as its density, viscosity, and surface tension, or how resistant a liquid is to rippling. The team also incorporated the effect of a planet’s atmospheric pressure. With this model, they aimed to predict how a planet’s liquid surface would evolve in response to winds of a given speed.

“Imagine a completely still lake,” Ashton offers. “We’re trying to figure out the first puff that will make those first little tiny ripples, on up to a full ocean wave.”

Making waves

The team first tested their new model with wave data on Earth. They used measurements of waves that were collected by buoys across Lake Superior over 20 years. They found that the model, which took into account Earth’s gravity, the composition of liquid (water), and atmospheric conditions, was able to accurately predict what windspeeds it would take to generate waves across the lake, and how high the waves grew with a given wind strength.

The researchers then applied the model to predict how waves would behave on other planetary bodies that are known to host liquid on their surface. They looked first to Titan, where NASA’s Cassini mission previously captured radar images of lake formations, which scientists suspect are currently filled with liquid methane and ethane. The team used the new model to calculate the moon’s wave dynamics given its gravity, atmospheric pressure, and liquid composition.

They found that on Titan, it’s surprisingly easy to make waves. The relatively light liquid, combined with low gravity and atmospheric pressure, means that even a gentle wind can stir up huge waves.

“It kind of looks like tall waves moving in slow motion,” Schneck says. “If you were standing on the shore of this lake, you might feel only a soft breeze but you would see these enormous waves flowing toward you, which is not what we would expect on Earth.”

The researchers also considered wave activity on ancient Mars. The Red Planet hosts many impact basins that may have once been filled with water, before the planet’s atmosphere dissipated and the water evaporated away. One of those basins is Jezero Crater, which is currently being explored by NASA’s Perseverance rover. With the new model, the team showed that as Mars’ atmosphere gradually disappeared, reducing its pressure over time, it would have required stronger winds to make the same waves.

Beyond the solar system, the researchers applied the model to three different exoplanets. The first, LHS1140b, is a “cool super-Earth,” meaning that it is colder and larger than Earth. The planet hosts liquid water, though because it is so large, it has a stronger gravity. The model showed that the same wind on Earth would generate much smaller waves of water on the super-Earth, due to its difference in gravity.

The team also considered Kepler 1649b, a Venus-like planet, which has a gravity similar to Earth’s, with lakes of sulfuric acid, which is about twice as dense as water. Under these conditions, the researchers found that it would take strong winds to make even a ripple on the exo-Venus, compared to on Earth.

This effect is even more pronounced for the third planet, 55-Cancri e — a lava world that has both a higher gravity than Earth and a much denser, more viscous surface liquid. Scientists suspect that the planet hosts oceans of liquefied rock. In this environment, the model predicts that hurricane-force winds on Earth, of about 80 miles per hour, would generate only small waves of a few centimeters in height on the lava world.

Aside from illuminating new ways that waves can behave on other planets, Perron hopes the model will answer longstanding questions of planetary landscape formation.

“Unlike on Earth where there is often a delta where a river meets the coast, on Titan there are very few things that look like deltas, even though there are plenty of rivers and coasts. Could waves be responsible for this?” Perron wonders. “These are the kinds of mysteries that this model will help us solve.”

This work was supported, in part, by NASA and the National Science Foundation.

Multitasking quantum sensors can measure several properties at once

Zach Winn | MIT News — Wed, 15 Apr 2026 00:00:00 -0400

A special class of sensors leverages quantum properties to measure tiny signals at levels that would be impossible using classical sensors alone. Such quantum sensors are currently being used to study the inner workings of cells and the outer depths of our universe.

Particularly promising are solid-state quantum sensors, which can operate at room temperature. Unfortunately, most solid-state quantum sensors today only measure one physical quantity at a time — such as the magnetic field, temperature, or strain in a material. Trying to measure both the magnetic field and temperature of a material at the same time causes their signals to get mixed up and measurements to become unreliable.

Now, MIT researchers have created a way to simultaneously measure multiple physical quantities with a solid-state quantum sensor. They achieved this by exploiting entanglement, where particles become correlated into a single quantum state. In a new paper, the team demonstrated its approach in a commonly used quantum sensor at room temperature, measuring the amplitude, frequency, and phase of a microwave field in a single measurement. They also showed the approach works better than sequentially measuring each property or using traditional sensors.

The researchers say the approach could enable quantum sensors that can deepen our understanding of the behavior of atoms and electrons inside materials and living systems like cancer cells.

“Quantum multiparameter estimation has been mostly theoretical to date,” says co-lead author of the paper Takuya Isogawa, a graduate student in nuclear science and engineering. “There have been very few experiments that actually demonstrate it, and that work focused on photons. We wanted to demonstrate multiparameter estimation in a more application-oriented setup: a solid-state quantum sensor in use today.”

Joining Isogawa on the paper are co-lead authors Guoqing Wang PhD ’23 and MIT PhD candidate Boning Li. The other authors on the paper are former MIT visiting students Zhiyao Hu and Ayumi Kanamoto; University of Tokyo PhD candidate Shunsuke Nishimura; Chinese University of Hong Kong Professor Haidong Yuan; and Paola Cappellaro, MIT’s Ford Professor of Engineering, a professor of nuclear science and engineering and of physics, and a member of the Research Laboratory of Electronics.

Quantum effects for measurement

Quantum sensors exploit quantum effects like entanglement, spin states, and superposition to measure changes in magnetic fields, electric fields, gravity, acceleration, and more. As such, they can be used to measure the activity of single molecules in ways that are useful for understanding biology and space, like tracking the activity of metabolites or enzymes inside cells.

One particularly useful sensor in biology leverages what’s known as nitrogen-vacancy (NV) centers in diamonds, a defect where a carbon atom in the diamond’s crystal lattice is replaced by a nitrogen atom, and a neighboring lattice site is missing, or vacant. The defect hosts an electronic spin whose transition frequencies can be read out optically. The NV center’s spin state is extremely sensitive to external effects, such as magnetic fields and temperature, which can shift the spin state in ways that can be measured at extremely high resolution.

Unfortunately, different external effects change the energy resonances of the spin in similar ways, making it difficult to measure multiple effects at once. The result is that most solid-state quantum sensor applications measure a single physical quantity at one time.

“If you can only measure one quantity at a time, you have to repeat experiments to measure quantities one by one,” Isogawa says. “That takes more time, which means less sensitivity. It also makes experiments more susceptible to errors.”

For their experiment, the researchers used NV centers inside of a 5-square-millimeter diamond. They pointed a laser into the diamond and studied its fluorescence to make their measurements, a common approach for such sensors. To study the electronic spin of the NV center, they used a microwave antenna. To study the spin of the nitrogen atom they used a radio frequency field.

“We used those two spins as two qubits,” Isogawa says, referring to the building blocks of quantum computing systems. “If you have only one qubit, you can only measure one outcome: basically, 0 or 1. It’s the probability that it spins up or down. Think of it like a coin toss, with the probability of getting heads or tails. With two qubits, we increased the parameters that we could extract.”

The system worked because the spins of the sensor qubit and auxiliary qubit were entangled, a quantum property where the state of one particle is dependent on another. With one qubit, you get a binary outcome. With two, you get four possible outcomes with a total of three possible parameters.

The two qubits allowed researchers to measure those three quantities simultaneously using a technique known as the Bell state measurement.

Other researchers had used the Bell state measurement at extremely low temperatures before, but the MIT researchers developed a new technique to perform the measurement at room temperature. That technique was first proposed by Wang, who was previously a graduate student in Professor Cappellaro’s lab.

The researchers used the approach to simultaneously measure the amplitude, detuning, and phase of a microwave magnetic field. The researchers also say the approach could be used to measure electric fields, temperature, pressure, and strain.

“Measuring these parameters simultaneously can help us explore spin waves in materials, which is an important topic in condensed matter physics,” Isogawa says. “NV center sensors have extremely high spatial resolution and versatility. It can measure a lot of different physical quantities.”

More practical quantum sensing

The researchers say this work is an important step toward using solid-state quantum sensors to more fully characterize systems in biomedical research and materials characterization. That’s because multiparameter estimation had never been achieved in realistic settings or in widely used quantum sensors.

“What makes the NV center quantum sensors so special is they can operate at room temperature,” Isogawa says. “It’s very suitable for biological measurements or condensed matter physics experiments.”

Although the researchers say their sensor didn’t measure each quantity at the highest possible precision, in future work they plan to explore if their approach can achieve higher precision for each parameter.

They also plan to explore how their approach works to characterize heterogenous materials.

“In an extremely uniform environment, you could use many different classical and quantum sensors and measure each physical quantity at the same time,” Isogawa says. “But if the physical quantities change at different locations, you need high spatial sensors, and you need a sensor that can measure multiple physical quantities. This approach has major advantages in such situations.”

The work was supported, in part, by the U.S. National Science Foundation, the National Research Foundation of Korea, and the Research Grants Council of Hong Kong.

Carbon removal project supports Maine’s blue economy, broader marine health

Anne Wilson | Department of Mechanical Engineering — Tue, 14 Apr 2026 00:00:00 -0400

Oceans absorb roughly 25 to 30 percent of the carbon dioxide (CO₂) that is released into the atmosphere. When this CO₂ dissolves in seawater, it forms carbonic acid, making the water more acidic and altering its chemistry. Elevated levels of acidity are harmful to marine life like corals, oysters, and certain plankton that rely on calcium carbonate to build shells and skeletons.

“As the oceans absorb more CO₂, the chemistry shifts — increasing bicarbonate while reducing carbonate ion availability — which means shellfish have less carbonate to form shells,” explains Kripa Varanasi, professor of mechanical engineering at MIT. “These changes can propagate through marine ecosystems, affecting organism health and, over time, broader food webs.”

Loss of shellfish can lead to water quality decline, coastal erosion, and other ecosystem disruptions, including significant economic consequences for coastal communities. “The U.S. has such an extensive coastline, and shellfish aquaculture is globally valued at roughly $60 billion,” says Varanasi. “With the right innovations, there is a substantial opportunity to expand domestic production.”

“One might think, ‘this [depletion] could happen in 100 years or something,’ but what we’re finding is that they are already affecting hatcheries and coastal systems today,” he adds. “Without intervention, these trends could significantly alter marine ecosystems and the coastal economies that rely on them over time.”

Varanasi and T. Alan Hatton, the Ralph Landau Professor of Chemical Engineering, Post-Tenure, at MIT, have been collaborating for years to develop methods for removing carbon dioxide from seawater and turn acidic water back to alkaline. In recent years, they’ve partnered with researchers at the University of Maine Darling Marine Center to deploy the method in hatcheries.

“The way we farm oysters, we spawn them in special tanks and rear them through about a two-week larval period … until they’re big enough so that they can be transferred out into the river as the water warms up,” explains Bill Mook, founder of Mook Sea Farm. Around 2009, he noticed problems with production of early-stage larvae. “It was a catastrophe. We lost several hundred thousand dollars’ worth of production,” he says.

Ultimately, the problem was identified as the low pH of the water that was being brought in: The water was too acidic. The farm’s initial strategy, a common practice in oyster farming, was to buffer the water by adding sodium bicarbonate. The new approach avoids the use of chemicals or minerals.

“A lot of researchers are studying direct air capture, but very few are working in the ocean-capture space,” explains Hatton. “Our approach is to use electricity, in an electrochemical manner, rather than add chemicals to manipulate the solution pH.”

The method uses reactive electrodes to release protons into seawater that is collected and fed into the cells, driving the release of the dissolved carbon dioxide from the water. The cyclic process acidifies the water to convert dissolved inorganic bicarbonates to molecular carbon dioxide, which is collected as a gas under vacuum. The water is then fed to a second set of cells with a reversed voltage to recover the protons and turn the acidic water back to alkaline before releasing it back to the sea.

Maine’s Damariscotta River Estuary, where Mook farms is located, provides about 70 percent of the state’s oyster crop. Damian Brady, a professor of oceanography based at the University of Maine and key collaborator on the project, says the Damariscotta community has “grown into an oyster-producing powerhouse … [that is] not only part of the economy, but part of the culture.” He adds, “there’s actually a huge amount that we could learn if we couple the engineering at MIT with the aquaculture science here at the University of Maine.”

“The scientific underpinning of our hypothesis was that these bivalve shellfish, including oysters, need calcium carbonate in order to form their shells,” says Simon Rufer PhD ’25, a former student in Varanasi’s lab and now CEO and co-founder of CoFlo Medical. “By alkalizing the water, we actually make it easier for the oysters to form and maintain their shells.”

In trials conducted by the team, results first showed that the approach is biocompatible and doesn't kill the larvae, and later showed that the oysters treated by MIT's buffer approach did better than mineral or chemical approaches. Importantly, Hatton also notes, the process creates no waste products. Ocean water goes in, CO₂ comes out. This captured CO₂ can potentially be used for other applications, including to grow algae to be used as food for shellfish.

Varanasi and Hatton first introduced their approach in 2023. Their most recent paper, “Thermodynamics of Electrochemical Marine Inorganic Carbon Removal,” which was published last year in journal Environmental Science & Technology, outlines the overall thermodynamics of the process and presents a design tool to compare different carbon removal processes. The team received a “plus-up award” from ARPA-E to collaborate with University of Maine and further develop and scale the technology for application in aquaculture environments.

Brady says the project represents another avenue for aquaculture to contribute to climate change mitigation and adaptation. “It pushes a new technology for removing carbon dioxide from ocean environments forward simultaneously,” says Brady. “If they can be coupled, aquaculture and carbon dioxide removal improve each other’s bottom line."

Through the collaboration, the team is improving the robustness of the cells and learning about their function in real ocean environments. The project aims to scale up the technology, and to have significant impact on climate and the environment, but it includes another big focus.

“It’s also about jobs,” says Varanasi. “It’s about supporting the local economy and coastal communities who rely on aquaculture for their livelihood. We could usher in a whole new resilient blue economy. We think that this is only the beginning. What we have developed can really be scaled.”

Mook says the work is very much an applied science, “[and] because it’s applied science, it means that we benefit hugely from being connected and plugged into academic institutions that are doing research very relevant to our livelihoods. Without science, we don’t have a prayer of continuing this industry.”

Jazz in the key of life

Peter Dizikes | MIT News — Sun, 12 Apr 2026 00:00:00 -0400

It is not hard to find glowing reviews of saxophonist Miguel Zenón, a creative jazz artist whose compositions incorporate musical elements from his native Puerto Rico.

For instance, The Jazz Times called “Jibaro,” Zenón’s breakthrough 2005 album, “profound yet joyful.” The New York Times called the same music “strong and light,” adding that we have “rarely seen a jazz composer step forward with a project so impressively organized, intellectually powerful and well played from the start.”

In 2009, when Zenón won a prestigious MacArthur Fellowship, the MacArthur Foundation called Zenón’s work “elegant and innovative,” with “a high degree of daring and sophistication.” In 2012, The New York Times reviewed another Zenón work, “Puerto Rico Nació en Mi: Tales From the Diaspora,” by calling the music “deeply hybridized and original, complex but clear.”

As you may have noticed, these notices all contain multiple descriptive terms. That’s because Zenón’s work is many things at once: jazz, combined with other musical genres; technically rigorous, and supple; novel, yet steeped in tradition. Indeed, Zenón has always seen jazz as being multifaceted.

“What I discovered, when I first encountered jazz, was this idea that you were using improvisation to portray your personality directly to your listeners,” Zenón explains. “And it was connected to a very interesting and intricate improvisational language. That provided something I hadn’t encountered in music before, this idea that you could have something personal and heartfelt walking hand in hand with something that was intellectual and brainy. That balance spoke to me.”

It is still speaking. In 2024, Zenón won the Grammy Award for Best Latin Jazz Album for “El Arte Del Bolero Vol. 2,” a collaboration with Venezuelan pianist Luis Perdomo, a musical partner in the Miguel Zenón Quartet.

Zenón has taught at MIT for three years now. He became a tenured faculty member last year, in MIT’s Music and Theater Arts program, where he helps students find the same satisfaction in music that he does.

“When I first got into music, I was looking for fulfillment,” Zenón says. “It wasn’t about success. I was just looking for music to fulfill something within me. And I still search for that now. And sometimes it still feels like it did 25 or 30 years ago, when I first encountered that feeling. It’s nice to have that in your pocket, to say, this is what I’m looking for, that initial feeling.”

Paradise in the Back Bay

Zenón grew up in San Juan, Puerto Rico. Around age 11, he started attending a performing arts school and playing the saxophone. In his last year of school, Zenón was admitted into college to study engineering. However, a few years before, he had encountered something new: jazz. Zenón’s training had been in classical music. But jazz felt different.

“Discovering jazz music ignited a passion for music in me that had not existed up to that point,” says Zenón, who decided to pursue music in college. “I kind of jumped ship, and it was a blind jump. I didn’t know what to expect, I didn’t know what was on the other side, I didn’t have any artists or any musicians in my family. I just followed a hunch, followed my heart.”

After teachers recommended he study at the renowned Berklee College of Music in Boston, Zenón worked to find a scholarship and funding.

“This was way before the internet. I was looking at catalogs,” Zenón recalls. “I had never been to Boston in my life, I didn’t even know what Berklee looked like. But at Berklee it was the first time I was able to connect with a jazz teacher in a formal way, to learn about history, theory, harmony, and I soaked in it. Also, I was surrounded by young people like myself, who were as enamored and passionate about music as I was. It really felt like paradise.”

After earning his BA from Berklee in 1998, Zenón then moved to New York City. He earned an MA from the Manhattan School of Music in 2001 and began playing more extensively with new bandmates.

“I just wanted to be able to play with people who were better than me, and learn from the experience,” Zenón says. He started generating new ideas, writing music, and performing publicly. With Antonio Sánchez, Hans Glawischnig, and Perdomo, he founded the Miguel Zenón Quartet.

“That led to going into the studio and making an album,” Zenón recounts. “And that led to more experience, and more albums.”

Did it ever. Zenón has now been the leader for about 20 albums, mostly featuring the quartet. (After several years, Henry Cole replaced Sánchez as the group’s drummer.) Zenón has played on many recordings by other artists, and helped found the SFJAZZ Collective.

Not many prolific musicians will name any one recording as their best, and Zenón is the same way, but he is willing to cite a few that were milestones for him.

“Jibaro” draws on the music of Puerto Rico’s jibaro singers, troubadors using 10-line stanzas with eight-syllable lines, something Zenón adopted for jazz-quartet use. “Esta Plena,” a 2009 record, fuses jazz and the structures of “plena,” a traditional percussion-based Puerto Rican song form. “Alma Adentro,” a 2011 album, covers classic songs from Puerto Rico.

“It would be impossible for me to pick one favorite, but what I would say is, there are a couple of albums in the earlier part of my career that explored a balance between things coming from a jazz world and coming from traditional Puerto Rican traditional music and folklore, when I was able to feel like that balance was right, it felt like me,” Zenón says. “This is what I have to give. This is my persona.”

In 2008, Zenón was also honored with a Guggenheim Fellowship, which helped him conduct music research, another facet of his career. Zenón has often extensively interviewed traditional Puerto Rican musicians about the intricacies of their works before writing material in those forms.

And Zenón has made a point of giving back, founding the Caravana Cultural, a project that brings free jazz concerts to rural Puerto Rico.

Work, joy, and love

Zenón is now settled in at MIT, which boasts a vibrant music program. More than 1,500 MIT students take a music class each year, and over 500 students participate in one of 30 campus ensembles. Last year, MIT opened its new Edward and Joyce Linde Music Building, a purpose-built performance, rehearsal, and teaching space.

“There are definitely students at MIT who could be at some of the best music schools in the world,” Zenón says. “That’s not in question.”

Moreover, among MIT students, Zenón says, “There is a communal approach to music. Everything they do, they do for each other. They look out for each other, they work together. And that has been one of the most rewarding things to see.”

He continues: “Of course the students are brilliant and the faculty are too. In terms of what I like to teach, it’s been a good fit for me personally, and I couldn’t be happier about the opportunity. There’s more and more interest in jazz, more and more interest in creating things together, and there’s a unique mindset being built in front of our eyes.”

He is also pleased to work in the Linde Music Building: “It’s amazing to have the building, not only in terms of the facilities, but it’s also a symbol of the place music has within the Institute. We’re not just talking about music, we’re creating it. It’s a great commitment from the school and says a lot about our leadership.”

Meanwhile, along with teaching, Zenón’s own recording career continues at full speed. With Luis Perdomo, he is working on “El Arte Del Bolero Vol. 3,” the follow-up to his Grammy-winning album. And Zenón has plans for still another album, to be recorded in Puerto Rico with a large ensemble, based on music he is writing about Puerto Rico’s history and present.

“Things are always linked,” Zenón explains. “Once you finish one project, the next one starts. It feels natural for me to do it that way.”

In conversation, Zenón is engaging, genial, and reflective. So what advice does he have for younger musicians? Not everyone who plays an instrument will become Miguel Zenón. But what about people who want to pursue music, not knowing how far it will take them?

“If you find something you enjoy, just enjoy it for the sake of it,” Zenón says. “Find what brings joy, and make sure you don’t lose that. Having said that, with music, like any art form, or anything else in life, in order to make progress, it takes work and commitment. There’s no hiding that. So if music is something you’re serious about, set goals you can achieve over time, so you always have something to work for. In my experience, that’s key. But I always pair that with the idea of joy and love for music — keeping that love close to your heart.”

Professor Emeritus Jack Dennis, pioneering developer of dataflow models of computation, dies at 94

Jane Halpern | Department of Electrical Engineering and Computer Science — Fri, 10 Apr 2026 17:40:00 -0400

Jack Dennis, an influential MIT professor emeritus of computer science and engineering, died on March 14 at age 94. The original leader of the Computation Structures Group within the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), he pioneered the development of dataflow models of computation, and, subsequently, many novel principles of computer architecture inspired by dataflow models.

The second child of an engineer and a textile designer, Dennis showed early interest in both engineering and music, rewriting Gilbert and Sullivan lyrics with his parents and playing piano with the Norwalk Symphony Orchestra in Connecticut as a teen, while building a canoe at home with his father. As an undergraduate at MIT, he developed his wide array of interests further, joining the VI-A Cooperative Program in Electrical Engineering; working at the Air Force Cambridge Research Laboratories on projects in speech processing and novel radar systems; participating in the model railroad club; and joining the MIT Symphony Orchestra, where he met his first wife, Jane Hodgson ’55, SM ’56, PhD ’61. (The two later separated when she went to study medicine in Florida.)

Dennis earned his BS (1953), MS (1954), and ScD (1958) from MIT before joining the then-Department of Electrical Engineering as a faculty member. He was promoted to full professor status in 1969. His doctoral thesis, entitled, “Mathematical Programming and electrical networks,” explored analogies between electric circuit theory and quadratic programming problems. Ideas he developed in that paper further crystallized in his 1964 paper, “Distributed solution of network programming problems,” which created an important early class of digital distributed optimization solvers.

In a 2003 piece that Dennis wrote for his undergraduate class’s 50th reunion, he remembered his earliest encounters with computers at the Institute: “I prepared programs written in assembly language on punched paper tape using Frieden 'Flexowriters,' and stood aside watching the myriad lights blink and flash while operator Mike Solamita fed the tapes [...] That was 1954. Fifty years later, much has changed: A room full of vacuum tubes has become a tiny chip with millions of transistors. A phenomenon once limited to research laboratories has become an industry producing commodity products that anyone can own and use beneficially.”

Dennis’ influence in steering that change was profound. As a collaborator with the teams behind both Project MAC and Multics, the earliest attempts to allow multiple users to work with a single computer seemingly simultaneously (i.e., a time-shared operating system), Dennis helped to specify the unique segment addressing and paging mechanisms that became a fundamental part of the General Electric Model 645 computer. His insights stemmed from a tendency to pay equal attention to both hard- and software when others considered themselves specialists in one or the other.

“I formed the Computation Structures Group [within CSAIL] and focused on architectural concepts that could narrow the acknowledged gap between programming concepts and the organization of computer hardware,” Dennis explained in his 2003 recollection. “I found myself dismayed that people would consider themselves to be either hardware or software experts, but paid little heed to how joint advances in programming and architecture could lead to a synergistic outcome that might revolutionize computing practice.”

Dennis’ emphasis on synergy did not go unnoticed. Gerald Sussman, the Panasonic Professor of Electrical Engineering, points out “the relationship of [Dennis’] dataflow architecture to single-assignment programs, and thus to pure functional programs. This coupled the virtue of referential transparency in programming to the effective use of hardware parallelism. Dennis also pioneered the use of self-timed circuits in digital systems. The ideas from that work generalize to much of the work on highly distributed systems.”

The Computation Structures Group attracted multiple scholars interested in developing asynchronous computing and dataflow architecture, many of whom became lifelong friends and collaborators. These included Peter Denning, with whom Dennis and Joseph Qualitz co-authored the textbook “Machines, Languages, and Computation” (1978); the late Arvind, who became faculty head of computer science for the Department of Electrical Engineering and Computer Science (EECS), and the late Guang R. Gao, who became distinguished professor of electrical and computer engineering at the University of Delaware.

In recognition of his contributions to the Multics project, Dennis was elected fellow of the Institute of Electrical and Electronics Engineers (IEEE). Many additional honors would follow: He received the Association for Computing Machinery (ACM)/IEEE Eckert-Mauchly Award in 1984; was inducted as a fellow of the ACM (1994); was named to the National Academy of Engineering (2009); was elected to the (ACM) Special Interest Group on Operating Systems (SIGOPS) Hall of Fame (2012); and was awarded the IEEE John von Neumann Medal (2013).

A successful researcher, Dennis was perhaps equally influential in the development of EECS’ curriculum, developing six subjects in areas of computer theory and systems: Theoretical Models for Computation; Computation Structures; Structure of Computer Systems; Semantic Theory for Computer Systems; Semantics of Parallel Computation; and Computer System Architecture (taught in collaboration with Arvind.) Several of the courses that Dennis developed continue to be taught, in updated form, to this day.

Following his retirement from teaching in 1987, he consulted on projects relating to parallel computer hardware and software for such varied groups as NASA Research Institute for Advanced Computer Science; Boeing Aerospace; McGill University; the Architecture Group of Carlstedt Elektronik in Gothenburg, Sweden; and Acorn Networks, Inc. His fruitful relationship with former student Guang Gao continued in the form of a lecture tour through China, as well as co-authorship of a book, “Dataflow Architecture,” currently in progress at MIT Press.

A voracious lifelong learner, Dennis was fond of repeating a friend’s observation that “a scholar is just a book’s way of making another book.” In a full and active retirement, he still made room for music, trying his hand at composing; performing at Tanglewood as a tenor in Chorus Pro Musica; playing piano at the marriage of Guang Gao’s son Nick; and joining the chorus at the First Church in Belmont, Massachusetts, where his celebration of life (with concurrent livestreaming) will be held on Monday, June 8, at 2 p.m.

Dennis is survived by his wife Therese Smith ’75; children David Hodgson Dennis of North Miami, Florida; Randall Dennis of Connecticut; and Galen Dennis, a resident of Australia.

The flawed fundamentals of failing banks

Peter Dizikes | MIT News — Thu, 09 Apr 2026 00:00:00 -0400

Bank runs are dramatic: Picture Depression-era footage of customers lined up, trying to get their deposits back. Or recall Lehmann Brothers emptying out in 2008 or Silicon Valley Bank collapsing in 2023.

But what causes these runs in the first place? One viewpoint is that something of a self-fulfilling prophecy is involved. Panic spreads, and suddenly many customers are seeking their money back, until an otherwise solid institution is run into the ground.

That is not exactly Emil Verner’s position, however. Verner, an MIT economist, has been studying bank failures empirically for years and now has a different perspective. Verner and his collaborators have produced extensive evidence suggesting that when banks fail, it is usually because they are in a fundamentally shaky position. A bank run generally finishes off an already flawed business rather than upending a viable one.

“What we essentially find is that banks that fail are almost always very weak, and are in trouble,” says Verner, who is the Jerome and Dorothy Lemelson Professor of Management and Financial Economics at the MIT Sloan School of Management. “Most banks that have been subject to runs have been pretty insolvent. Runs are more the final spasm that brings down weak banks, rather than the causes of indiscriminate failures.”

This conclusion has plenty of policy relevance for the banking sector and follows a lengthy analysis of historical data. In one forthcoming paper, in the Quarterly Journal of Economics, Verner and two colleagues reviewed U.S. bank data from 1863 to 2024, concluding that “the primary cause of bank failures and banking crises is almost always and everywhere a deterioration of bank fundamentals.” In a 2021 paper in the same journal, Verner and two other colleagues studied banking data from 46 countries covering 1870-2016, and found that declining bank fundamentals usually preceded runs. And currently, Verner is working to make more historical U.S. bank data publicly available to scholars.

Seen in this light, sure, bank runs are damaging, but bank failures likely have more to do with bad portfolios, poor risk management, and minimal assets in reserve, rather than sentiment-driven client behavior.

“From the idea that bank crises are really about sudden runs on bank debt, we’re moving to thinking that runs are one symptom of crisis that runs deeper,” Verner says. “For most people, we’re saying something reasonable, refining our knowledge, and just shifting the emphasis,” Verner says.

For his research and teaching, Verner received tenure at MIT last year.

Landing in a “great place”

Verner is a native of Denmark who also lived in the U.S. for several years while growing up. Around the time he was finishing school, the U.S. housing market imploded, taking some financial institutions with it.

“Everything came crashing down,” Verner said. “I got obsessed with understanding it.”

As an undergraduate, he studied economics at the University of Copenhagen. After three years, Verner was unconvinced the discipline had fully explained financial crises. He decided to keep studying economics in graduate school, and was accepted into the PhD program at Princeton University.

Along the way, Verner became a historically minded economist, digging into data and cases from past decades to shed light on larger patterns about crises and bank insolvency.

“I’ve always thought history was extremely fascinating in itself,” Verner says. And while history may not repeat, he notes, it is “a really valuable tool. It helps you think through what could happen, what are similar scenarios, and how agents acted when facing similar constraints and incentives in the past.”

For studying financial crises in particular, he adds, history helps in multiple ways. Crises are rare, so historical cases add data. Changes over time, like more financial regulations and more complex investment tools, provide different settings to examine the same cause-and-effect issues. “History is a useful laboratory to study these questions,” Verner says.

After earning his PhD from Princeton, Verner went on the job market and landed his faculty position at MIT Sloan. Many aspects of Institute life — the classroom experience, the collegiality, the campus — have strongly resonated with him.

“MIT is a great place,” Verner says simply. “Great colleagues, great students.”

Focused on fundamentals

Over the last decade, Verner has published papers on numerous topics in addition to banking crises. As an outgrowth of his doctoral work, for instance, he published innovative papers examining the dampening effect that household debt has on economic growth in many countries. He also co-authored the lead paper in an issue of the American Economic Review last year examining the way German hyperinflation after World War I reallocated wealth to large business with substantial debt, leading them to grow faster.

Still, the main focus of Verner’s work right now is on banking crises and bank failures — including their causes. In a 2024 paper looking at private lending in 117 countries since 1940, Verner and economist Karsten Müller showed that financial crises are often preceded by credit booms in what scholars call the “non-tradeable” sector of the economy. That includes industries such as retail or construction, which do not produce easily tradeable goods. Firms in the non-tradeable sector tend to rely more heavily on loans secured by real estate; during real estate booms, such firms use high valuations to borrow more, and they become more vulnerable to crashes — which helps explain why bank portfolios, in turn, can crater as well.

In recent years, in the process of studying these topics, Verner has helped expand the domain of known U.S. historical data in the field. Working with economists Sergio Correia and Stephan Luck, Verner has helped apply large language models to historical newspaper collections, unearthing information about 3,421 runs on individual banks from 1863 to 1934; they are making that data freely available to other scholars.

This topic has important policy implications. If runs are a contagion bringing down worthy banks, then one solution is to provide banks with more liquidity to get through the crisis — something that has indeed been tried in the U.S. However, if bank failures are more based in fundamentals about risk and not keeping enough capital on hand, more systemic policy options about best practices might be logical. At a minimum, substantive new research can help alter the contents of those discussions.

“When banks fail, it’s usually because these banks have taken a lot of risk and have big losses,” Verner says. “It’s rarely unjustified. So that means these types of liquidity interventions alone are not enough to stop a crisis.”

The expansive research Verner has helped conduct includes a number of specific indicators that fundamentals are a big factor in failure. For instance, examining how infrequently banks recover their all assets shows how shaky their foundations are.

“The recovery rate on assets is informative about how solvent a bank was,” Verner says. “This is where I think we’ve contributed something new.” Some economists in the past have cited particular examples of struggling banks making depositors whole, but those are exceptions, not the rule. “Sometimes people argue this or that bank was actually solvent because depositors ended up getting all their money back, and that might be true of one bank, but on aggregate it’s not the case,” Verner says.

Overall, Verner intends to keep following the facts, digging up more evidence, and seeing where it leads.

“While there is this notion that liquidity problems can arise pretty much out of nowhere, I think we are changing that emphasis by showing that financial crises happen basically because banks become insolvent,” Verner underscores. “And then the bank run is that final dramatic spasm — which slightly shifts how we teach and talk about it, and perhaps think about the policy response.”

Desirée Plata appointed associate dean of engineering

Mary Beth Gallagher | School of Engineering — Wed, 08 Apr 2026 12:45:00 -0400

Desirée Plata, the School of Engineering Distinguished Climate and Energy Professor in the MIT Department of Civil and Environmental Engineering, has been named associate dean of engineering, effective July 1.

In her new role, Plata will focus on fostering early-stage research initiatives across the school’s faculty and on strengthening entrepreneurial and innovation efforts. She will also support the school’s Technical Leadership and Communication (TLC) Programs, including: the Gordon Engineering Leadership Program, the Daniel J. Riccio Graduate Engineering Leadership Program, the School of Engineering Communication Lab, and the Undergraduate Practice Opportunities Program.

Plata will join Associate Dean Hamsa Balakrishnan, who continues to lead faculty searches, fellowships, and outreach programs. Together, the two associate deans will serve on key leadership groups including Engineering Council and the Dean’s Advisory Council to shape the school’s strategic priorities.

“Desirée’s leadership, scholarship, and commitment to excellence have already had a meaningful impact on the MIT community, and I look forward to the perspective and energy she will bring to this role,” says Paula T. Hammond, dean of the School of Engineering and Institute Professor in the Department of Chemical Engineering.

Plata’s research centers on the sustainable design of industrial processes and materials through environmental chemistry, with an emphasis on clean energy technologies. She develops ways to make industrial processes more environmentally sustainable, incorporating environmental objectives into the design phase of processes and materials. Her work spans nanomaterials and carbon-based materials for pollution reduction, as well as advanced methods for environmental cleanup and energy conversion.  Plata directs MIT’s Parsons Laboratory, which conducts interdisciplinary research on natural systems and human adaptation to environmental change.

Plata is a leader on campus and beyond in climate and sustainability initiatives. She serves as director of the MIT Climate and Sustainability Consortium (MCSC), an industry–academia collaboration launched to accelerate solutions for global climate challenges. She founded and directs the MIT Methane Network, a multi-institution effort to cut global methane emissions within this decade. Plata also co-directs the National Institute of Environmental Health Sciences MIT Superfund Research Program, which focuses on strategies to protect communities concerned about hazardous chemicals, pollutants, and other contaminants in their environment.

Beyond academia, Plata has co-founded two climate and energy startups, Nth Cycle and Moxair. Nth Cycle is redefining metal refining and the domestic battery supply chain. Earlier this month, the company signed a $1.1 billion off-take agreement to help establish a secure and circular technology for battery minerals.

Her company Moxair specializes in advanced approaches for low-level methane monitoring and destruction. In 2026, with support from the U.S. Department of Energy and collaboration with MIT, Moxair will build and demonstrate a first-of-a-kind dilute methane oxidation technology to tackle methane emissions using transition metal catalysts.

As an educator, Plata has helped develop programs that enhance research experience for students and postdocs. She played a pivotal role in the founding of the MIT Postdoctoral Fellowship Program for Engineering Excellence, serving on its faculty steering committee, overseeing admissions, and leading both the academic track and entrepreneurship track. She also helped design the MCSC Climate and Sustainability Scholars Program, a yearlong program open to juniors and seniors across MIT.

Plata earned a BS in chemistry from Union College in 2003 and a PhD in the joint MIT-Woods Hole Oceanographic Institution program in oceanography and applied ocean science in 2009. After completing her doctorate, she held faculty positions at Mount Holyoke College, Duke University, and Yale University. While at Yale, she served as associate director of research at the university’s Center for Green Chemistry and Green Engineering. In 2018, Plata joined MIT’s faculty in the Department of Civil and Environmental Engineering.

Her work as a scholar and educator has earned numerous awards and honors. She received MIT’s Harold E. Edgerton Faculty Achievement Award in 2020, recognizing her excellence in research, teaching, and service. She has also been honored with an NSF CAREER Award and the Odebrecht Award for Sustainable Development. Plata is a fellow of the American Chemical Society and was a Young Investigator Sustainability Fellow at Caltech.

Plata is a two-time National Academy of Engineering Frontiers of Engineering Fellow and a two-time National Academy of Sciences Kavli Frontiers of Science Fellow. Her dedication to mentoring was recognized with MIT’s Junior Bose Award for Excellence in Teaching and the Frank Perkins Graduate Advising Award.

Physicists zero in on the mass of the fundamental W boson particle

Jennifer Chu | MIT News — Wed, 08 Apr 2026 12:00:00 -0400

When fundamental particles are heavier or lighter than expected, physicists’ understanding of the universe can tip into the unknown. A particle that is just beyond its predicted mass can unravel scientists’ assumptions about the forces that make up all of matter and space. But now, a new precision measurement has reset the balance and confirmed scientists’ theories, at least for one of the universe’s core building blocks.

In a paper appearing today in the journal Nature, an international team including MIT physicists reports a new, ultraprecise measurement of the mass of the W boson.

The W boson is one of two elementary particles that embody the weak force, which is one of the four fundamental forces of nature. The weak force enables certain particles to change identities, such as from protons to neutrons and vice versa. This morphing is what drives radioactive decay, as well as nuclear fusion, which powers the sun.

Now, scientists have determined the mass of the W boson by analyzing more than 1 billion proton-colliding events produced by the Large Hadron Collider (LHC) at CERN (the European Organization for Nuclear Research) in Switzerland. The LHC accelerates protons toward each other at close to the speed of light. When they collide, two protons can produce a W boson, among a shower of other particles.

Catching a W boson is nearly impossible, as it decays almost immediately into two types of particles, one of which, a neutrino, is so elusive that it cannot be detected. Scientists are left to measure the other particle, known as a muon, and model how it might add up to the total mass of its parent, the W boson. In the new study, scientists used the Compact Muon Solenoid (CMS) experiment, a particle detector at the LHC that precisely tracks muons and other particles produced in the aftermath of proton collisions.

From billions of proton-proton collisions, the team identified 100 million events that produced a W boson decaying to a muon and a neutrino. For each of these events, they carried out detailed analyses to narrow in on a precise mass measurement. In the end, they determined that the W boson has a mass of 80360.2 ± 9.9 megaelectron volts (MeV). This new mass is in line with predictions of the Standard Model, which is physicists’ best rulebook for describing the fundamental particles and forces of nature.

The precision of the new measurement is on par with a previous measurement made in 2022 by the Collider Detector at Fermilab (CDF). That measurement took physicists by surprise, as it was significantly heavier than what the Standard Model predicted, and therefore raised the possibility of “new physics,” such as particles and forces that have yet to be discovered.

Because the new CMS measurement is just as precise as the CDF result and agrees with the Standard Model along with a number of other experiments, it is more likely that physicists are on solid ground in terms of how they understand the W boson.

“It’s just a huge relief, to be honest,” says Kenneth Long, a lead author of the study, who is a senior postdoc in MIT’s Laboratory for Nuclear Science. “This new measurement is a strong confirmation that we can trust the Standard Model.”

The study is authored by more than 3,000 members of CERN’s CMS Collaboration. The core group who worked on the new measurement includes about 30 scientists from 10 institutions, led by a team at MIT that includes Long; Tianyu Justin Yang PhD ’24; David Walter and Jan Eysermans, who are both MIT postdocs in physics; Guillelmo Gomez-Ceballos, a principal research scientist in the Particle Physics Collaboration; Josh Bendavid, a former research scientist; and Christoph Paus, a professor of physics at MIT and principal investigator with the Particle Physics Collaboration.

Piecing together

The W boson was first discovered in 1983 and is predicted to be the fourth heaviest among all the fundamental particles. Multiple experiments have aimed to narrow in on the particle’s mass, with varying degrees of precision. For the most part, these experiments have produced measurements that agree with the Standard Model’s predictions. The 2022 measurement by Fermilab’s CDF experiment is the one significant outlier. It also happens to be the most precise experiment to date.

“If you take the CDF measurement at face value, you would say there must be physics beyond the Standard Model,” says co-author Christoph Paus. “And of course that was the big mystery.”

Paus and his colleagues sought to either support or refute the CDF’s findings by making an independent measurement, with an experiment that matches CDF’s precision. Their new W boson mass measurement is a product of 10 years’ worth of work, both to analyze actual particle collision events and to simulate all the scenarios that could produce those events.

For their new study, the physicists analyzed proton collision events that were produced at the LHC in 2016. When it is running, the particle collider generates proton collisions at a furious rate of about one every 25 nanoseconds. The team analyzed a portion of the LHC’s 2016 dataset that encompasses billions of proton-proton collisions. Among these, they identified about 100 million events that produced a very short-lived W boson.

“A particle like the W boson exists for a teeny tiny moment — something like 10^-24 seconds — before decaying to two particles, one of which is a neutrino that can’t be measured directly,” Long explains. “That’s the tricky part: You have to measure the other particle — a muon — really well, and be able to piece things together with only one piece of the puzzle.”

Gathering momentum

When a muon is produced from the decay of a W boson, it carries half of the W boson’s mass, which is converted into momentum that carries the muon away from the original collision. Due to the strong magnetic field inside the CMS detector, the electrically charged muon follows a path whose curvature is a function of its momentum. Scientists’ challenge is to track the muon’s path and every interaction it may have with other particles and its surroundings, in order to estimate its initial momentum.

The muon’s momentum is also influenced by the momentum of the W boson before it decays. Decoding the impact of the W boson’s motion from the effects of its mass presented a major challenge. To infer the W boson mass, the team first carried out simulations of every scenario they could think of that a muon might experience after a proton-proton collision in the chaotic environment of the particle collider. In all, the team produced 4 billion such simulated events described by state-of-the-art theoretical calculations. The simulations encoded diverse hypotheses about how the muon momentum is affected by the physical features of the CMS detector, as well as uncertainties in the predictions that govern W boson production in LHC collisions.

The researchers compared their simulations with data from the 2016 LHC run. For every proton-proton collision event that occurs in the collider, scientists can use the CMS detector at CERN’s LHC to precisely measure the energy and momentum of resulting particles such as muons. The team analyzed CMS measurements of muons that were produced from over 100 million W boson events. They then overlaid this data onto their simulations of the muon momentum, which they then converted to a new mass for the W boson.

That mass — 80360.2 ± 9.9 megaelectron volts — is significantly lighter than the CDF experiment’s measurement. What’s more, the new estimate is within the range of what the Standard Model predicts for the W boson’s mass, bolstering physicists’ confidence in the Standard Model and its descriptions of the major particles and forces of nature.

“With the combination of our really precise result and other experiments that line up with the Standard Model’s predictions, I think that most people would place their bets on the Standard Model,” Long says. “Though I do think people should continue doing this measurement. We are not done.”

“We want to add more data, make our analysis techniques more precise, and basically squeeze the lemon a little harder. There is always some juice left,” Paus adds. “With a better look, then we can say for certain whether we truly understand this one fundamental building block.”

This work was supported, in part, by multiple funding agencies, including the U.S. Department of Energy, and the SubMIT computing facility, sponsored by the MIT Department of Physics.

MIT graduate engineering and business programs ranked highly by U.S. News for 2026-27

MIT News — Tue, 07 Apr 2026 00:01:00 -0400

U.S. News and World Report has again placed MIT’s graduate program in engineering at the top of its annual rankings, released today. The Institute has held the No. 1 spot since 1990, when the magazine first ranked such programs.

The MIT Sloan School of Management also placed highly, occupying the No. 6 spot for the best graduate business programs.

Among individual engineering disciplines, MIT placed first in six areas: aerospace/aeronautical/astronautical engineering, chemical engineering, computer engineering (tied with the University of California at Berkeley), electrical/electronic/communications engineering (tied with Stanford University and Berkeley), materials engineering, and mechanical engineering. It placed second in nuclear engineering.

In the rankings of individual MBA specialties, MIT placed first in four areas: business analytics, entrepreneurship (with Stanford), production/operations, and supply chain/logistics. It placed second in executive MBA programs (with the University of Chicago).

U.S. News bases its rankings of graduate schools of engineering and business on two types of data: reputational surveys of deans and other academic officials, and statistical indicators that measure the quality of a school’s faculty, research, and students. The magazine’s less-frequent rankings of graduate programs in the sciences, social sciences, and humanities are based solely on reputational surveys.

In the sciences, ranked by U.S. News for the first time in four years, MIT’s doctoral programs placed first in four areas: biology (with Scripps Research Institute), chemistry (with Berkeley and Caltech), computer science (with Carnegie Mellon University and Stanford), and physics (with Caltech, Princeton University, and Stanford). The Institute placed second in mathematics (with Harvard University, Stanford, and Berkeley).