Many engineers are more comfortable working behind the scenes developing technology than speaking to consumers about products. But Preethi Ashwath has learned to take center stage, talking about the “hearable” and wearable devices she has had a hand in developing as an analog integrated circuit designer in the consumer electronics division at Analog Devices of Wilmington, Mass.

The IEEE member is one of the few women who work in the specialized field, where about 11 percent are female, according to career platform Zippia.

Preethi Ashwath

Employer

Analog Devices, in Boston

Title

Integrated circuit designer

Member grade

Member

Alma maters

SJB Institute of Technology in Bengaluru, India; University of California, Irvine

Ashwath stepped out from behind the scenes last year to meet with attendees at the Consumer Electronics Show (CES) in Las Vegas. She demonstrated Analog’s new earbuds, which track people’s heart and lung health through vibrations in their ear canals.

After she demonstrated her ability to explain the technology to consumers in simple terms, Analog selected her to represent the company at other events. She says she has “come to see speaking as part of contributing to the field, rather than something separate from technical work.”

She also gives technical talks on various aspects of electrostatic discharge (ESD) and other topics, as well as participates in panel discussions, she says, because they “reach engineers well beyond my immediate team.”

Ashwath is an active volunteer with the IEEE Circuits and Systems Society.

“Whether through designing semiconductor products, publishing technical articles, reviewing conference papers, or speaking at industry events, I try to contribute to the growth of the engineering community and the advancement of the field,” she says.

Discovering circuit design at Intel

Ashwath, who grew up in Bengaluru, India, learned about electronics engineering from her high school physics tutor.

That introduction led her to take electronics as one of her core courses in her final years there. At the time, Ashwath’s older sister was pursuing an engineering degree.

“Between my interest in electronics and seeing her already on that path, it became clear to me that engineering was the way to go,” Ashwath says.

After earning a bachelor’s degree in electronics and communications engineering at the SJB Institute of Technology, in Bengaluru, she wanted to pursue a career in very-large-scale integration. But that changed once she moved to the United States and began her master’s program in electrical engineering at the University of California, Irvine.

She discovered her passion for circuits and devices during an internship at Intel in Santa Clara, Calif. She learned about circuit design, package design, and reliability verification tools such as Ansys Totem.

“Seeing every layer of the chip development process gave me a more complete picture of what goes into building a semiconductor product,” she says. “Through that experience, I realized I felt most connected to design.

“It was the hard work and creativity that drew me in. You start with a specification, build a circuit, simulate it, then wait for the chip to come back from fabrication. When your design works, it’s a real feeling of satisfaction. It teaches you patience and perseverance.”

After attaining her master’s degree, she was hired by Intel as an analog design engineer. She gained experience in mixed-signal circuit design before joining Analog. In her current role, she works on consumer hearables such as earbuds and wearables including fitness trackers and health monitors.

Her main focus is designing ESD pad rings, the borders of specialized circuits built around a microchip’s outermost edge to protect it from the discharge, which can damage circuit paths or introduce latent defects that lead to failures later in the chip’s life cycle.

“On a chip with multiple voltage domains, you have to plan how the protection structures are placed and connected across the whole pad ring—and get it right,” Ashwath says. “A mistake there could cause a chip to fail in the field in ways that are very hard to debug afterward.

“It’s detailed work that requires understanding both the device physics and how the whole chip fits together.”

Part of the job is designing and creating an integrated circuit’s building blocks, including ESD protection structures and other mixed-signal blocks. Ashwath conducts rigorous checks to verify they work as intended before being integrated into a chip.

She collaborates with ESD advisors, layout designers, and the digital team that designs the chip’s digital logic.

“I work with them to make sure my analog circuits and their digital blocks work together correctly,” she says. “No two days look exactly the same.”

In 2024 she was recognized with the company’s Impact Award for her “exceptional work on a critical project with significant cross-functional business impact.”

Presenting the final product

Ashwath was selected to represent Analog at events after a senior leader encouraged her to ask for high-visibility opportunities, she says.

“Most of my time is spent on designing circuits and simulations,” she says. “At CES, I stood in front of customers, engineers, and members of the press, explaining our technology and answering questions on the spot.”

The earbuds she demonstrated monitor minute vibrations from a user’s heart and lungs, according to the Analog website. The vibrations can detect arrhythmia, a leaky heart valve, or fluid in the lungs.

“Hearables are evolving from playing back audio to becoming health-monitoring platforms,” she says. “The ear turns out to be a good location for picking up physiological signals, so these devices can capture vital signs while someone is simply wearing them normally.

“The hard part is achieving high signal quality. The signals you’re trying to detect are very small and buried in the noise from the person’s movements and the surrounding environment.

“That’s where analog front-end design becomes critical. Extracting a meaningful signal from a noisy real-world environment is fundamentally an analog problem.”

Being face-to-face with customers at the conference was new to her.

“What struck me most was seeing how people react to the technology,” she says. “When you’re designing a block, you’re focused on specifications and margins. At CES, after explaining what the product does, I had a better understanding of why it matters to them.

“That connection between the circuit work and the final product is something you don’t always see from the design bench.”

Since the CES, Ashwath has presented other technologies she’s worked on. At the annual National Association of Music Merchants show in Anaheim, Calif., she demonstrated the A2B Automotive Audio Bus to the audio industry. The bus uses a low-cost unshielded twisted-pair cable to carry multichannel digital audio, control data, synchronized clock signals, and power in fewer than 50 microseconds, according to Analog.

“IEEE connects you with academia, which you don’t get much exposure to once you’re in industry. That connection between industry and academic work is valuable in both directions.”

At the company’s Global Technology Conference—an internal event held in Boston that draws engineers from the company’s more than 50 sites around the world—Ashwath led a demonstration of its solutions for augmented reality smart glasses.

“Each of these events has a different audience,” she says. “Learning to explain my technical work to different people has become a skill I’ve acquired.”

The most rewarding part of her job, she says, is seeing the end result of her work.

“You spend months on a design, and there’s a long wait between finishing it and finding out whether your instincts were right,” she says. “When the measurements match what you simulated, that’s the most satisfying moment in this job.

“The other part is knowing my work ends up in a real product. The circuits I design go into devices people actually use every day.”

Giving back to the community

Like many engineers, Ashwath joined IEEE to access research published in the IEEE Xplore Digital Library. It keeps her current in a field that keeps moving, she says.

During her first year of membership, in 2021, she discovered other perks including networking and educational opportunities.

“IEEE connects you to a community outside your own company,” she says. “Attending lectures and technical talks exposes you to approaches and problems you wouldn’t encounter in your own work.”

She eventually began seeking out volunteer opportunities, specifically with IEEE conferences and journals, because they are “how you stay current with what’s happening in circuits and systems beyond your own day-to-day work,” she says.

“As an analog designer,” she says, “being connected to that broader community matters. The field moves, and you need a way to keep learning from it.”

She serves as a peer reviewer of papers for several IEEE conferences including the IEEE International Symposium on Circuits and Systems and the IEEE International Midwest Symposium on Circuits and Systems. ISCAS is the flagship conference of the IEEE Circuits and Systems Society. MWSCAS is the oldest U.S. conference on the technologies.

“Someone has to assess whether submitted work is technically sound and worth presenting,” she says, “and that only happens if practicing engineers are willing to put in the time.”

The combination of learning from the community and contributing to it is what makes her membership worthwhile, she says.

“Analog design is a field where you’re always learning. Just when you feel you understand one architecture, there’s another one to learn,” she says. “Being an active learner isn’t optional in this field; it’s the requirement, and being part of the IEEE community helps with that.

“IEEE also connects you with academia, which you don’t get much exposure to once you’re in industry.

“Reviewing papers means reading what researchers are working on, which is often quite different from how the same problems get approached in a product environment. That connection between industry and academic work is valuable in both directions.”

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!

If you’re an engineer, you may feel like your skills aren’t commanding the premium they once did. Many engineers have faced layoffs or restructuring. But even for those of us who still have jobs, something corrosive is happening. You may have heard some version of this:

“ChatGPT answered that in five seconds. Why can’t you?”

Or, “I made this app in a weekend. Why is our team taking so long?”

I worked on a team where leadership said things like this directly to engineers. This was in 2024 and 2025, when the AI craze was in full swing and Claude and ChatGPT adoption was rising.

Demo-Driven Development

Generative AI (and the marketing behind it) gives people the illusion that the work is simpler than it is. A working demo hides complexity, and once someone has a website that works, they may decide they no longer need engineers, or start to distrust the ones they do have.

Some CEOs want to believe they can replace an engineering team with a few sexy prompts. Many companies also want them to believe this.

That website works great in the demo—under specific conditions and without users with thumbs for fingers. Then it has to plug into a system with millions of lines of old code, satisfy legal requirements, meet accessibility standards, and be delivered in a complex deployment process.

Thinking that writing code was the only bottleneck for deploying new software is like the Titanic seeing the tip of the iceberg and assuming there was nothing underneath it.

What you can do about it

Engineers have a unique position in the AI space, because you have the capacity to understand how these models work much better than the average person. Use this to your advantage.

Learn how AI models work. If you can’t already explain that LLMs are probabilistic, what tokens are, or why training data matters, spend a weekend fixing that. You don’t need to become a full-blown AI researcher. A few fundamentals will put you ahead of most people making the AI decisions.

Teach upward. Turn that knowledge into teaching moments for the people who lead you. Explain the limits of these tools, and why a demo and a production system are different animals. It keeps people from truly believing that a notebook full of prompts can replace an engineering team.

Consider the criticism of today’s processes. Some of what we do has been treated as ceremony for too long. Be willing to say, “you know what, maybe you’re right, this process is too slow for the speed we can work at now.” Conceding that earns you the credibility to defend the steps that matter.

I still believe that speed at the cost of everything else is harmful—but that’s a topic for another time.

Find your voice. Engineers at the forefront of AI adoption need to push back and explain why we do things the way we do. Nobody else in the room is going to do it for us.

Don’t chase (too many) shiny objects

Your goal, in my opinion, is to save your organization from buying into the hype so much that they chase the wrong thing. Our job, before and after AI, is building safe and useful products for human beings, and engineers are still best suited for that.

—Brian

We Need to Embed Ethics Into Our Engineering Courses

The need to educate engineering students about ethics and social responsibility is widely recognized, but today’s curricula often miss the mark. Three professors propose a new approach: adding ethics training directly to core technical coursework.

Read more here.

The U.S. Just Bet $1 Billion on Quantum Chip Manufacturing

Looking for a career in quantum computing hardware? An IBM spin-off called Anderon could receive up to $1 billion in funding from the U.S. CHIPS and Science Act for its quantum foundry, established earlier this year. Could Anderon do for quantum hardware what TSMC did for conventional chips?

Read more here.

Meet the Anthropic Engineer Keeping Its AI Agent Trustworthy

When Gurmeet Saran joined Anthropic’s technical staff in 2025, he entered a role focused on a key question: How do you know whether you can trust a chatbot like Claude? Learn more about Saran’s path and his work at the AI company.

Read more here.

The U.S. semiconductor industry is booming but industry watchers fear the country’s workforce is unprepared.

By 2030, investments in manufacturing, design, materials, and packaging facilities in the United States will create demand for 189,000 new full-time semiconductor jobs. But the supply of such workers will only reach about 62,000 people, the National Network for Microelectronics Education (NNME) estimated in July. “The U.S. is facing a critical shortage of workers for the semiconductor industry,” says Jennifer Taylor-Mendoza, president of West Valley College in Saratoga, California. “Rebuilding this workforce is not optional.”

“Community colleges are the gateway,” says Leah Palmer, Executive Director of the Arizona Advanced Manufacturing Institute at Maricopa Community Colleges (MCC), a network of 10 colleges. “Not everyone can afford university. Not everyone is prepared for university.” But community colleges are well placed to meet those potential workers where they are and help them fill the need of the semiconductor industry.

The biggest shortage is of engineers, who make up about 60 percent of the total unmet demand, the NNME reports. But technicians to maintain machines, run quality control, and manage facilities are also in demand, to the tune of potentially tens of thousands of unfilled jobs by 2030. These jobs require only an associate’s degree or a high school diploma and some additional training. People with those qualifications now in the service industry might improve their lives by taking technical jobs, but only if they know these jobs exist.

The semiconductor worker shortfall is the kind of challenge that community colleges were built for, Taylor-Mendoza says. Community colleges have a long history of helping train people quickly and affordably for critical jobs such as nursing, court reporting, or making computer chips. Community colleges in regional semiconductor hubs are now offering apprenticeships, certificates, and other training to enable people to update their skills or pivot to new career paths in the industry. They funnel some graduates into engineering programs at nearby universities to get four-year degrees and recruit and train high school students.

If You Build It, Will They Come?

In response to the 2022 U.S. CHIPS and Science Act, companies have announced 140 new projects and investments of more than US $640 billion in the country, according to a September report by the Center for Security and Emerging Technology (CSET). The Taiwan Semiconductor Manufacturing Company (TSMC), for instance, plans to invest $165 billion in the US. Its chip fabrication plant in Arizona began production in 2024, and the company plans to open 3 more fabs and two packaging facilities. The Arizona fab employs about 3,500 people, and when another two open, the company will need about 6,000 employees in the region, including thousands of technicians.

“‘Build it and they will come’ doesn’t work” with semiconductor fabs, says Palmer of Maricopa Community Colleges (MCC), which collaborates with TSMC, Intel, and other companies on its semiconductor curricula. MCC recruits high school students and performs marketing in the region, educating people about jobs in the semiconductor industry and the training they need to get a foot in the door. Then they provide that training, often at no cost to students.

In 2023, MCC began offering a 10-day training program for the semiconductor industry called Quick Start. Palmer says 1,500 people have completed the program, which has a waiting list of thousands. At MCC and other community colleges, industry representatives review curricula to make sure they are tailored to the real needs of employers and are often a source of instructors, too.

For US$306, Quick Start students learn how to read schematics, use hand tools, and the basics of vacuum technology. But students only pay if they do not complete the program. Those who do finish get an invitation to an industry job fair.

Like many such community college programs, Quick Start is “stackable,” meaning students can convert their participation into credits toward more advanced certificates or degrees. Community colleges also offer training for existing semiconductor employees to improve their skills, apprenticeships, and training for high school students.

“Having a variety of programs is important,” says Pearl Chang Esau, CEO of Shàn Strategies, a consulting firm that works with TSMC to develop training for community colleges. Some students must work full-time. Others have families to take care of and long commutes to campus. “We have to increase access for learners, or we’re going to have a workforce gap,” she says.

Get In the Pool

Laura Marmolejo, dean of advanced manufacturing at Austin Community Colleges (ACC), says representatives of the more than 200 Texas semiconductor plants have told her they need new workers. She says the industry’s message is, “We need more people in the pool, otherwise we’re just stealing each other’s employees.”

Part of the problem is visibility. “No one knows what semiconductors are,” Marmolejo says. ACC invites high school students to tour their facilities.

A clean room is hard to imagine if you’ve never been in one, and 12-hour shifts in a full “bunny suit” with goggles and gloves might not be for everyone. So at ACC, students go down the rabbit hole, wearing clean-room suits for some practical classes. The school also uses virtual reality to simulate some environments students might encounter and to teach tasks like checking for a vacuum leak. “You have to have that experience to make a decision about whether this is right for you,” Palmer says.

At West Valley College, students in a 15-week program learn about the semiconductor industry, then learn about key equipment and how to troubleshoot it. Part of it takes place at the nearby Stanford Nanofabrication Facility, where students build a transistor over a semester. Next year, West Valley College will open a fab on campus.

Students at West Valley College perform laboratory work.West Valley College

Can They Close the Gap?

Tracking semiconductor program graduates’ subsequent careers is challenging, says Palmer. “When the student leaves the college, we have a hard time getting hiring data back.” MCC sends graduates surveys to ask them to self-report their employment status at 30, 60, and 90 days.

While the community college population skews young, 40 percent of Quick Start participants are aged 30 to 49, suggesting that the program is attracting people who want to switch careers, Palmer says. The program has reached 35 percent women, which is relatively high for advanced manufacturing, Palmer says, while 36 percent of participants were Latino or Hispanic and 13 percent were Black.

The CSET report concludes that developing a healthy U.S. semiconductor ecosystem will require reaching potential employees from underrepresented demographics, particularly women and Black and Hispanic people. Palmer is excited about the demographics of Arizona’s 10-day training program, which show that they are reaching a relatively diverse population.

Even with these efforts by community colleges, the NNME predicts there will be a shortfall in the U.S. semiconductor workforce. Without more efforts to fill that gap, they warn, workforce shortages will hold back expansion of the industry in the U.S.

“It’s an all-hands-on-deck moment,” says Chang Esau.

If you’ve ever wondered how Claude and similar AI agents can answer complex questions in seconds and where they get the information, you’re not alone.

Gurmeet Saran works to find that answer every day as an engineer at Anthropic in San Francisco, the AI research company behind Claude. The IEEE senior member is developing an infrastructure to understand the mechanics behind the chatbot’s behavior and to ensure its underlying architecture produces reliable outputs.

Gurmeet Saran

Employer

Anthropic in San Francisco

Title

Member of technical staff

Member grade

IEEE senior member

Alma maters

University of Kalyani, in India; Birla Institute of Technology and Science, in Pilani, India

“My job is to make sure the data is accurate, structured, and trustworthy, and that private data stays private,” he says. “As I learned earlier in my career, privacy can’t be a layer you bolt on—it has to be part of the architecture from the start.” If you can’t trust the logic, he adds, you can’t make reliable claims about the behavior, and that has real safety implications.

Safety is a priority for Anthropic, he says. According to Claude’s mission statement, the company wants the AI agent to be “genuinely helpful to the people it works with or on behalf of, as well as to society, while avoiding actions that are unsafe, unethical, or deceptive.” Crucial to the mission is ensuring that researchers can observe and understand how AI models behave, a challenge that is especially acute for large language models, Saran says.

“We have decent tools to monitor databases and web services,” he says, “but for LLMs, we’re still figuring it out.”

Solving data quality problems at Yahoo, Intuit, and Meta

Saran’s interest in computing systems dates back to his childhood in Kolkata, where he logged as much time as he could in the school computer lab.

“I remember the first time I used one of the computers,” he says. “What struck me was that you could write instructions, and the machine would follow them exactly. No ambiguity.

“That felt magical to me.”

After graduating from high school in 1999, he stayed in India to pursue advanced degrees. He earned a bachelor’s degree in electronics and telecommunication engineering in 2004 at the University of Kalyani. In 2010, he earned a master’s degree in software engineering from the Birla Institute of Technology and Science in Pilani.

While a graduate student, he began his career in 2007 at Yahoo as a technical lead of the data team in Bengaluru, India. While there, he encountered the project that shaped his career trajectory: working on web analytics and business intelligence reporting solutions.

“We kept running into a frustrating problem,” he recalls. “Different teams would calculate the same website metric—something like daily active site users—using slightly different logic, and end up with conflicting answers. It sounds like a small issue, but when leadership is making decisions about resource allocation and product development using that data, not knowing which number is correct becomes a problem.”

The team built a central data repository with defined metrics, essentially serving as a single source of truth. Saran created a model that defined what “daily active site users” means, breaking down which events counted, how to handle edge cases (or when standard code fails), and more.

With the new model, data teams across the company could rely on a standard method to determine the number of daily active users for the projects and sites they were responsible for and obtain comparable results using the same logic.

From there, Saran and his team built Yahoo! Digits, an internal web analytics tool used for all the company’s products.

Saran left Yahoo in 2010 to join Intuit as a staff engineer in Mountain View, Calif. He helped build the analytics infrastructure that unified data across the company’s products, including TurboTax and QuickBooks accounting software. During his tenure, Intuit developed a feature that detected fraudulent tax filings in real time. It required Saran and other engineers to build a system to protect taxpayer data while adhering to strict U.S. Internal Revenue Service policies.

“IEEE operates in a lot of countries where engineers don’t have easy pathways into the global professional community. That kind of connection matters as AI development spreads beyond a handful of organizations.”

“This taught me that privacy and governance can’t be layers you bolt onto software after the fact,” he says. “They have to be part of the architecture from the start.”

Saran joined Meta (known as Facebook at the time) in 2016 as a lead software engineer in Menlo Park, Calif. The company was facing controversy over its video metrics: Meta publicly acknowledged an error that caused it to overstate the number of views for posted videos.

“This wasn’t a coding bug; it was an architectural problem,” Saran says. He built a system that worked similarly to what he had developed at Intuit, whereby one single truth was defined to govern all video view metrics across the site.

That system, Metric360, eventually became part of the site’s architecture. By the time he left Meta in 2021, he says, Metric360 was being used across the company to calculate video metrics.

He left the company to become a principal software engineer at Wealthfront, a financial services company in Palo Alto, Calif. He helped build the company’s data infrastructure.

Building trustworthy AI systems at Anthropic

Saran joined Anthropic in 2025 as a member of the technical staff.

To describe the implications of his work, he uses a medical analogy: “Imagine a doctor is monitoring a patient’s vital signs and making treatment decisions based on those numbers. What if one or a few of the medical devices used were slightly miscalibrated? Even if most readings are fine, it’s a serious problem because the difficult decisions happen in the margins.

“It’s the same with AI systems. My job is essentially to make sure the instruments are giving accurate readings—that the data means what it says it means.”

His job comes with high stakes and complex problems to solve.

“AI agents are especially tricky to observe,” he says. “When a system is taking sequences of actions [generating conversational responses with different users], you need a way to capture what it did, what information it was working from, and whether outcomes matched what the end user expected. And you have to do all of that without exposing anyone’s private information—the systems are designed to recognize sensitive data and keep it protected as it flows through. It’s a much more demanding observability problem than anything I’ve worked on before.”

Solving for system reliability while AI is still in its early days of adoption is both Saran’s passion and the problem that keeps him up at night, he says.

“I think making AI systems genuinely auditable and transparent is one of the more important engineering problems of the next decade,” he says, “and it doesn’t get talked about as much as model capabilities do.”

IEEE can play a role in shaping AI development

Saran joined IEEE in March 2025.

“After more than 20 years of building data infrastructure across organizations like Yahoo, Intuit, Meta, and Wealthfront,” he says, “I wanted to contribute back to the technical community through peer review, publishing, and engaging with the broader engineering research ecosystem.”

He says he believes IEEE can play an essential role in shaping the future of AI through its standards work.

“We have standards for everything, from building codes to software testing,” he says. “AI infrastructure needs something similar, and IEEE has the credibility and the reach to do that.”

Another problem that IEEE can solve for, he says, is access: “IEEE operates in a lot of countries where engineers don’t have easy pathways into the global professional community. That kind of connection matters, especially as AI development spreads beyond a handful of organizations in a few cities.”

Today’s engineers face an unprecedented acceleration in AI hardware complexity, as explained in the recent research article “Revisiting Edge AI: Opportunities and Challenges.” The article examines the rapid growth of edge AI and the challenges it creates, including resource constraints, model architecture limitations, and network demands across edge-AI deployments.

The acceleration is driven by a fundamental shift in how modern AI models are built and scaled. As the models have become much larger and more complex, they are computationally more demanding because they contain more parameters and require more calculations.

To meet the demands of scaling deep neural networks, the industry is increasingly developing AI chips that are designed for specific tasks.

One major reason is that moving data between memory and the processor has become a major limitation on AI performance.

The movement to confront the hardware bottleneck—the AI memory wall—has altered the trajectory of semiconductor innovation, shifting architectural priorities toward domain-specific accelerator platforms.

No longer can engineers evaluate systems statically; they must master joint hardware design and network-algorithm co-optimization to navigate the critical trade‑offs between throughput, latency, and operational efficiency.

The challenges are addressed in the new AI Processor Architecture, Design Principles, and Performance program, developed by IEEE Educational Activities with support from the IEEE Computer Society.

Topics covered

The five-course program provides a structured exploration of AI processor technologies, from fundamental design principles to advanced architectures and real-world deployment.

The topics are:

• Fundamental principles of design and functionality.
• Practical insights into advanced architectures.
• Understanding neural processing units for industry deployment.
• Emerging trends and evolving architectures.
• Designing for edge, cloud, quantum, and the Internet of Things (IoT).

The program addresses the needs of professionals across the AI hardware ecosystem, including hardware architects, chip designers, embedded systems developers, data‑center hardware engineers, and innovators exploring next‑generation processor ecosystems.

It is also valuable for those transitioning into AI chip design or seeking to understand the architectural forces shaping modern machine learning acceleration. For many, it provides the bridge between theoretical knowledge and the collaborative, cross‑disciplinary reasoning required in engineering environments.

The program is designed to explore how modern AI processors are conceived, structured, and optimized. The architectural layers that define contemporary AI hardware will be covered, including compute units, memory hierarchies, dataflows, and the performance characteristics that emerge from design decisions. The curriculum bridges theory and application, enabling participants to interpret architectural foundations, analyze trade‑offs, and understand how hardware structures shape computational efficiency across diverse environments.

By the end of the courses, learners will be able to evaluate processor behavior with the analytical precision expected of professionals working at the frontier of AI hardware design.

AI-generated avatars explain concepts

The program also uses a dialogue‑driven learning approach. Learners view conversations between AI-generated avatars that engage in scenario‑based dialogues. The avatars represent engineers tackling the same problems from various perspectives based on their different roles. The simulated storytelling and dialogues make advanced engineering concepts approachable without sacrificing depth or interactivity, because the user is occasionally challenged to decide the correct answer that leads to the best course of action.

A hardware engineer might push back against a systems engineer’s demands, for example, revealing the friction between physical constraints and algorithmic ambition. A computational validation specialist might interrogate a chip performance engineer’s optimism, exposing the gap between theoretical throughput and real‑world behavior. A heterogeneous systems architect could debate a multiprocessor coordination specialist about synchronization overheads, while a standards development engineer discusses regulatory implications with a technology strategy and compliance architect.

In the final course, an IoT systems architect and an embedded AI optimization engineer dissect the realities of deploying AI in constrained environments.

By the end of the course, learners will be able to evaluate processor behavior with the analytical precision expected of professionals working at the frontier of AI hardware design.

The avatar-driven learning environment creates a psychologically safer environment for learners, who might feel intimidated by traditional expert‑led videos. Research published in 2024 in IEEE Transactions on Learning Technologies showed that avatar‑based instruction can increase a learner’s confidence by up to 25 percent and improve retention of complex technical material.

Each course concludes with a module in which the two experts from different disciplines debate, question, and challenge each other’s assumptions.

Modeling expert reasoning through dialogue

The cross‑disciplinary conversations can do more than explain concepts; they can model how experts think. They can reveal the negotiations behind architectural choices, the competing priorities that shape system design, and the analytical rigor required to balance performance, efficiency, scalability, and compliance.

Learners observe engineering discourse, gaining insight into the reasoning patterns that drive innovation in AI processor development.

The program goes further by interrupting the dialogue at key moments and inviting the learner to step in. Instead of passively absorbing information, the learner decides how to resolve a trade‑off, predict the outcome of a design choice, or select the most defensible engineering path. The experience can feel less like a course and more like an apprenticeship inside an engineering team.

A new standard for advanced engineering education

By merging rigorous technical content with an innovative, dialogue‑driven delivery model, the program sets a standard for teaching advanced engineering. It captures the complexity of real‑world problem‑solving, humanizes the learning experience, and challenges participants to think like the engineers who are defining the future of AI hardware. It is not simply a new course; it is a new way of learning.

For individual access, visit the IEEE Learning Network.

For customized organizational options, contact a content specialist to discuss volume pricing.

In mid-September, Anthropic CEO Dario Amodei joined a growing group of researchers calling for the AI industry to slow down. While some have questioned these researchers’ motivation for voicing concerns about the rapid pace of development, the plea highlighted a key tension between safety and speed. The underlying problem is not simply a technical error, but a sociotechnical failure: Pressure to move quickly has made it hard for engineers to fully assess and manage new risks of increasingly powerful AI systems. It is a clear warning that responsible engineering requires more than technical expertise; engineers must also understand workplace pressures, moral and ethical rules, and the social consequences rooted in the systems they create.

Cases like this, alongside invasive surveillance technologies and biased AI tools, have exposed serious shortcomings in how engineers recognize and address the ethical and social implications of their work. While the need to educate graduates in ethics and social responsibility is widely recognized, a persistent gap remains—these topics are too often treated as afterthoughts, disconnected from the technical content of engineering coursework. This siloed approach leaves graduates ill-equipped to address real-world challenges; indeed, only about a third of engineering professionals in the United States report having received training on their public welfare responsibilities during their formal engineering education.

We are working to bridge this gap by designing two complementary instructional approaches, one that integrates social issues directly into foundation technical coursework and a second that prepares students to act as “public welfare watchdogs” through a standalone engineering course. By embedding ethics and social responsibility training directly into the core of undergraduate engineering coursework, we equip future professionals with the agency they will need to protect the public good.

The gap in instruction about ethics and social responsibility

Across the engineering profession, there is a persistent belief that technical skill-building is central to engineering, while ethical, social, and public welfare concerns are peripheral or distracting. This technical/social dualism is evident even in the evaluation of scholarly research. For instance, we recently submitted an academic position paper discussing this ethics education gap to a leading peer-reviewed engineering journal. It was dismissed by reviewers along the very same dualism it described.

One reviewer warned that it would be “highly irresponsible” to challenge technical skill-building as “the foundation of any kind of engineering” by adding ethics and social responsibility training into engineering classrooms. Another reviewer objected that integrating these considerations into foundational courses risks “distracting the students from fully absorbing the technology.” These comments miss the core argument of our work: that ethics and social responsibility are integral to responsible technical practice.

Today, accreditation agencies like ABET (formerly known as the Accreditation Board for Engineering and Technology) and professional licensing bodies like the National Society of Professional Engineers mandate student outcomes related to social and public welfare consequences. But these curriculum requirements tend to be unhelpfully generic and minimally enforced. As a result, engineering students often receive ethics instruction through introductory or non-engineering coursework, where the focus is on codes of conduct and academic integrity, rather than deeper topics of social responsibility. This instruction is almost always disconnected from the complex real-world dilemmas engineers face.

Indeed, we find that only ethics instruction in actual engineering courses (not in other parts of the curriculum) can effectively instill ethical awareness as professionals. Concerningly, only about 4 in 10 engineers in the United States recall receiving this kind of training, and 3 in 10 report never receiving training on their public welfare responsibilities at any point in their careers. This leaves most of the profession underprepared to handle the complex challenges of engineering practice.

A new approach: Integrating technical and social dimensions

To advance a new paradigm—one that embeds ethics and social responsibility issues within the undergraduate engineering curriculum—we’ve designed two innovative course-based approaches: a library of one-hour sociotechnical modules for foundational circuits courses (described here and here) and a new engineering course dedicated to public welfare responsibilities.

The first approach integrates discussions about relevant social issues directly into foundational engineering courses, where students begin to form their identities as engineers. “Introduction to Circuits” is typically the first course in the electrical engineering major and is a requirement for many other engineering disciplines, making it a powerful site for introducing social content and ethical reasoning. We designed several one-hour modules for this course, where social issues provide context for a technical topic, offering a manageable entry point for faculty who may feel ill-prepared to teach about ethics and social responsibility.

While many engineering faculty recognize the importance of teaching students about these topics, they struggle to integrate them into their courses amid heavy teaching and research demands. To overcome these challenges, each module provides ready-to-use materials, including lecture slides with scripts and homework and exam problems. Integrating these modules directly into the course signals to students that sociotechnical awareness is not an “add-on,” but a required professional competency.

Our module on hospital power prioritization, for instance, connects the technical topics of power and equivalent circuits to the ethical challenges of healthcare infrastructure. Students learn about the reality of “red outlets” in medical settings, debating the power needs of specific wards (e.g., the intensive care unit, operating rooms, and physiotherapy rooms) to decide which branches should receive emergency power during a hypothetical outage.

Our conflict minerals module connects the technical topic of capacitors to the global tantalum supply chain. Students calculate the amount of tantalum used in cell phones and identify mining locations in regions like the Democratic Republic of the Congo, where revenue often fuels armed conflict and human rights abuses. Students research the conflict-mineral policies of major companies such as Apple and Samsung and reflect on their future roles as ethical designers and consumers.

Student responses to these modules have been overwhelmingly positive. In interviews, students told us that the content helped them get away from the “stigma that engineers only worry about math” and made the work more meaningful. We plan to share the modules widely to help circuits instructors across the globe bridge the technical-social divide.

Beyond these integrated modules, our second approach is a standalone engineering course—Public Welfare Responsibilities in Engineering (PWRE)—that helps round out students’ sociotechnical education by preparing them to fulfill their professional obligations as engineers. While most dedicated ethics courses end by introducing engineers’ abstract responsibilities, PWRE helps students recognize specific public welfare concerns, enhances their motivation to act when they encounter them, and teaches concrete, practical intervention strategies.

In addition to teaching students about professional codes, such as the IEEE Code of Ethics, PWRE engages students in discussion of where these formal definitions may fall short, and how structural barriers such as workplace culture can hinder engineers from fulfilling these responsibilities. Then, the course equips students with intervention strategies. Students learn about their rights and responsibilities as whistleblowers, review tactics for seeking advice from professional societies, and practice writing op-eds to raise the alarm about ethical issues to the broader public. Our research shows that the course produces notable shifts in students’ ethical agency, strengthening their ability to serve as “public welfare watchdogs” in the modern workplace.

Ethical engineers for the future

Today’s engineering graduates face complex sociotechnical challenges beyond AI expansion, including climate change and global supply chains; they need tools to navigate ambiguity and act ethically in uncertain contexts. To meet these evolving demands, engineering education must move beyond outdated modes of instruction that focus only on building technical skills—some of which may soon be outsourced to AI. We must fundamentally reimagine the role of ethics and social responsibility, embedding them across the engineering curriculum and throughout the professional formation of engineers.

All engineers learn to solve problems, but ethical engineers can also discern which problems are worth solving and anticipate the consequences of innovation for society. By centering ethics and social responsibility as core priorities of the profession, we can prepare engineers to be stewards of technology and trusted voices in public discourse.

As one student reflected after participating in our modules, “We are a part of the issue if we don’t decide to fix it.”

By 2028, genetic engineering and gene therapy will be used frequently to treat a variety of diseases, thanks to advances in artificial intelligence, according to the recently released 2030 Technology Megatrends Report from IEEE.

“We are seeing a fundamental shift in how technology evolves,” says IEEE Fellow Dejan Milojicic, chair of the IEEE Future Directions Committee’s Industry Advisory Board, the group responsible for the report. “Artificial intelligence is no longer operating in a vacuum; it is now deeply connected to our energy, infrastructure, and physical systems.” Milojicic, a Hewlett Packard Enterprise Fellow, is a vice president at HPE Labs in Milpitas, Calif.

AI underpins many of the report’s 30 technologies and five “megatrends,” which are technology shifts with the potential to reshape industries and everyday life for decades. A megatrend isn’t a single breakthrough but a collection of related technologies.

For the 2030 report, 166 experts—from 38 countries across six continents—drawn from industry, academia, and government identified 30 breakthrough technologies across the five megatrends: AI, energy, health, space, and physical AI. The experts graded the trends against five criteria: likelihood of success, impact to humanity, maturity, market adoption, and horizon to adoption.

AI as a common thread

Interconnected systems drive the megatrends, and the connection among them is AI.

The report notes that AI technology is advancing faster than any previous revolution, including industrial, electrical, and digital. The pace has turned the technology into general-purpose infrastructure, Milojicic says.

That evolution carries risks, though, the experts say. The report cites the lack of computing infrastructure and energy sources as constraints on the ability to scale up AI, and it says the technology’s growth will require reshaping the workforce.

Workers have always needed to adapt to stay competitive, Milojicic says, adding that AI adoption isn’t an all-or-nothing approach. Businesses should decide when and how to apply it, he says.

The experts say long-term success will require both technical capabilities and attention to the human-AI relationship. Conditions including trust, security, explainability, and policymaking will determine which AI-supported technologies are most likely to scale, they say.

“This report shifts the conversation to something far more important: how these advances will intersect with one another to shape human lives,” Mary Ellen Randall, 2026 IEEE president and CEO, said in a news release about the report. “The data makes clear that trust, safety, and human connection must guide every major breakthrough in the decade ahead.”

Health care is a high-impact bet

The experts scored personalized medicine as the study’s highest-impact technology, at 4.93 out of 5, for its potential to improve human life.

The report credits AI-driven breakthroughs in personalized diagnostics and therapeutic treatments, driven by innovative biotech startups. Within two years, the experts predict, genetic engineering and gene therapy will be used in therapeutic areas.

Within five years, earlier disease diagnoses will move out of the lab and into day-to-day medical practice, the report predicts. Meanwhile, it says, researchers likely will be able to manufacture simple synthetic proteins—molecules engineered from scratch, rather than found in nature—to perform a specific job in the body, such as targeting a tumor or supplying a missing enzyme.

Energy wakes up for AI

Increased power demand by AI is outpacing the energy sector’s ability to keep up. Tech infrastructure—data centers, networks, and computing hardware—already uses an estimated 10 percent of electricity globally, according to the news release.

The experts predict that within two to three years, energy storage will double in developed countries.

The energy industry has been relatively dormant for years, Milojicic says, with little breakthrough innovation since the times of Thomas Edison and Nikola Tesla.

“Data centers can ramp up within milliseconds, yet it takes months for the power and energy sector to ramp up,” he says. “The governments of various countries will have to rethink their approach to power generation to stay ahead of demand.”

The experts cite the Jevons paradox—the idea that efficiency gains tend to increase overall consumption. As AI chip efficiency improves, organizations will run more hardware to build ever-larger frontier models—those advanced AI systems currently in development—the experts say. Data centers will draw as much power as grids can supply, leaving no surplus, the experts predict.

Physical AI moves the fastest

Physical AI refers to systems embedded within machines that sense and act in the world, including robots and autonomous vehicles. In the report, the category originally was called “hyper-automation and robotics,” a broad, software-driven process automation paired with physical machines. The Industry Advisory Board later folded it into the single physical AI megatrend.

The experts found that human-AI interaction has the shortest adoption horizon of any of the 30 scored technologies, at two to three years. They also say physical AI has the biggest chance of technical advancement of any megatrend during the next four years.

“The data makes clear that trust, safety, and human connection must guide every major breakthrough in the decade ahead.” —Mary Ellen Randall, 2026 IEEE president and CEO

Smarter, more efficient robots will substantially cut energy consumption in the next two years, the experts say.

Communications between humans and AI—whether in an android or another device—will shift from text-based commands to predominantly audio and video instructions within two to three years, the experts predict.

Androids used in factories will use tactile and haptic feedback within two to five years, letting them sense what they’re touching and adjust their grip or force in response, the experts say. Bio-inspired skin, tissues, and sensors will advance in the same timeframe, the experts predict, giving robots perception that better mirrors human touch.

Space is just getting started

Space technology, as the report defines it, covers more than exploration. It spans satellite communications, in-space manufacturing, launch technology, and other infrastructure. It is one of the lower-profile megatrends in the report, not for lack of promise but because of where the sector presently sits.

“It’s not lagging,” Milojicic says. “It’s the current state of maturity. People are talking about satellite communication right now because it’s very cool and practical. But it’s a small segment compared to the attention AI gets.”

He says he believes the space sector will be more prominent in the coming years: “This is an area of technology that will become increasingly important, especially as terrestrial communication and transportation push the boundaries for space as well.”

The report highlights two technologies on the near horizon: semiconductor manufacturing that uses parts built in space and more affordable reusable rockets. It also sees clean energy generation in space and the commercialization of orbital garbage collection as potential technologies to watch for in the next decade.

What’s next?

The report recommends that academia, industry, governments, and professional associations work together.

To build a well-trained workforce, the report says, industry should partner with professional organizations on training programs and revive apprenticeships in chemistry, physics, and other physical sciences. It also calls on businesses to treat AI infrastructure—computing, data, and energy—as a long-term strategic asset.

Other recommendations include expanding access to affordable, quality education in STEM coursework and AI competence.

Governments should lead on policy and implement reintegration programs for workers displaced by AI, the report says.

The experts say professional organizations including IEEE should develop new products and services using AI, strengthen safety and data governance standards, and serve as a collaboration platform for industry, academia, and policymakers.

The report also offers recommendations for end users, content creators, investors, and company leadership on strategies for succeeding in an AI-focused future.

The same thread runs through all the report’s recommendations: Don’t lose sight of the human-AI relationship.

“As a public charity dedicated to advancing technology for the benefit of humanity, IEEE believes progress must be measured by more than technology development and implementation speed,” Sophia A. Muirhead, IEEE executive director and chief operating officer, said in the news release. “Whether retraining the global workforce or integrating AI responsibly into daily life, the success of these megatrends depends on ensuring innovation strengthens human well-being and public trust.”

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!

When I changed careers at 30 to become a software engineer, I expected it to be hard. I expected to struggle to fit in, and I expected to bomb a lot of interviews (I did). What I didn’t expect was how few people would support me.

As a parent, I get it now.

A career change is a risk, and the people who care about you tend to project their fears onto you. They don’t want to watch you struggle or fail, so they discourage you or just don’t offer much enthusiasm.

Right now is a strange time to be in engineering, or knowledge work in general. Plenty of us are excited about what AI means for our jobs. More of us are nervous, and I think that fear is justified. Employment for entry-level software developers has fallen nearly 20 percent since late 2022, according to Stanford’s 2026 AI Index, and it can feel like the knowledge we spent our careers building is being commoditized.

Lately I’ve seen a flood of social media content telling software developers to go into the trades. YouTubers are telling you to become a plumber or a goat herder.

But before you do that, I want to warn you, there is no EASY path. Even if you want to become a goat herder.

I’ve changed careers once so far, but my business has forced me through several pivots that felt a lot like career changes—new audiences, new skills, new identity. Here’s what I’d tell anyone considering a change, whether you’re moving into engineering, leaving it, or finding a new speciality.

Find someone who’s done it and ask how

This is exactly what I told my oldest son, who’s getting ready to enter the job market full-time. Outside of the primary social media feeds, the internet is full of free and paid communities on Facebook, LinkedIn, and other sites. Reddit works, too, if you can stomach the trolls, because there’s valuable information buried in there.

Many of these spaces are flooded with people trying to break in, so look for the ones full of people who already do the work and talk openly about what is and isn’t going well. Then find one or two people in your own circle who’ve done it and ask what the job takes.

You’ll often find the degree matters less than you assumed. Indeed Hiring Lab found the share of U.S. postings requiring a bachelor’s degree fell from 20.4 percent in 2019 to 17.8 percent in early 2024, and more than half listed no education requirement at all.

Build proof of work, even if it’s unpaid

Yes, you’ll probably need to do some work for free, or at least far cheaper than you’re used to. I’ve had to do this in my business over and over. Nobody cares that I’ve been a software engineer for 12 years. They want to know whether I can do the thing they need.

When I started pitching businesses, I offered to build something for free as a case study, then used it to land clients who paid well. You can copy this in most fields. Say you want to get into marketing: Learn how Facebook ads work, find a friend or small business that needs help, and run the campaigns at a low price. Now you’ve picked up the jargon, and you have something concrete to show a hiring manager.

Stability is a myth

It may be corny, but change is the only constant. If you’re a software engineer, you could argue you’re in the middle of a career change already, whether you opted in or not. Programmers in the 1990s had the same experience when the web showed up and they had to quickly learn new tools just to stay relevant.

If you’re looking for stability, you’re in the wrong place, and I think it will get scarcer as AI and other forces reshape work. The best move is to build a strong foundation so you can adapt when the ground shifts. I wrote about the foundational skills to focus on if you’re interested.

—Brian

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For the first 10 years of my career, I worked in product management and data analytics by myself. I wrote database queries that pulled numbers out of corporate systems, built statistical models to predict what customers would buy, and shipped data pipelines that moved information between business systems.

I built and scaled analytics teams at Best Buy and Target, studying how customers shop and what stores should stock. Today I lead enterprise AI transformation at Lowe’s, the Fortune 100 home improvement retailer.

The goal is not to sell artificial intelligence; it is to use it to deliver useful expertise at the moment a customer needs it. In retail and other customer-facing industries, virtual assistants can help people address everyday questions—such as how to repair a leaky faucet—while guiding them toward relevant products, services, or next steps. As these capabilities become more common, technology roles are changing. The work is no longer limited to building AI systems; it also includes defining how they operate: which decisions they can make autonomously, when they must escalate to a person, and which actions must remain off-limits.

That shift—from building AI systems to governing them—is coming for anyone who is accountable for what such systems produce. Not the casual user typing into a chatbot but the engineers, product managers, analysts, and business operators who sign off on work a machine drafted.

It is the subject of the book I recently coauthored, The Enterprise Brain. I call the change the “governor shift,” from executing tasks yourself to setting the intent, principles, and boundaries within systems that execute them for you.

Business operators might not write code; they will decide which pricing exceptions an agent may approve and which it must escalate.

That is governing.

A 2025 report from MIT Media Lab’s Project NANDA found that, despite an estimated US $30 billion to $40 billion in enterprise generative-AI investment, the vast majority of organizations in its dataset had not yet demonstrated measurable profit-and-loss impact. The report estimated that only about 5 percent of integrated pilots were generating substantial value, underscoring how difficult it remains to move from experimentation to scaled business outcomes.

Researchers named the pattern the GenAI Divide, the term I adopted for the book.

The companies rarely lack technology; they use the same models as the 5 percent that are winners. But they lack people who can direct the systems and stand behind the results.

Guidelines to follow

Here are six guidelines.

• Recognize when you have become “human middleware.” In software, “middleware” is the code that sits between two systems and passes information back and forth. Many of us have become its human version. Take an honest look at your week. How much time is spent pulling data out of one tool, reformatting it, and routing it to another team? I call this the “administrator trap,” which is set by the architecture, not by the people caught in it.
  
  Relaying is what AI agents now do well. But they cannot judge which numbers deserve attention, which risks are real, or which compromises are worth making.

• Trade rules for principles. For many years, workers used rules to manage their work. Refunds for a product over a certain amount needed a signature from upper management, for example. Writing code needed two reviewers. Rules work at human speed. But rules break when a system makes thousands of decisions per hour and meets situations no rulebook anticipated, such as a complaint covered by three different policies. A rule says to do exactly this specific thing; a principle says to achieve the outcome without crossing certain lines.
  
  Governing AI means writing those principles in priority order so the system settles its own conflicts the way a well-led team does when the manager is not available. Never harm the customer. Tell the truth even if the company loses a sale. Protect the economics, and then move quickly. Underneath sits a question of decision rights: the formal authority over who or what may make a given call. Writing down the answers in what I call a “library of principles” is now core leadership work, whether you’re a technologist or a business owner.

• Write your culture into your code. Many companies have turned their values into posters that hang on office walls. But an AI agent cannot read the posters. Instead, write your governance as code. Include your values and policies as machine-readable instructions that the AI agent will follow automatically.
  
  Do so in three layers. The top is the constitution, which states the rules an agent may never break, and never state a fact it cannot support. The second layer is the doctrine: how the business competes and the acceptable trade-offs to get there, such as protecting a long-term relationship over a short-term sale. At the bottom sits the playbook, which has the tactics used for one task.

• Install a trust thermostat, not a trust switch. The question that stalls nearly every company’s AI deployment is some version of: “What if it tells our biggest customer something wrong, or quotes a price we will not honor?” It might. Treating trust as a switch leaves two bad options: an unsupervised system or a human reviewing every transaction—which would cost more than the automation would save.
  
  The alternative is a thermostat. Every decision an agent makes carries a confidence score measured against the principles set. Above an agreed threshold, it proceeds alone; below it, a human decides. That person’s answer is fed back into the learning loop so the next similar case clears the threshold on its own. Every decision stays transparent, auditable, and explainable—which is what I call a glass box.

• Fix context before you govern. You cannot govern a system that cannot see the whole picture. Ask your best employee about a project, and they will pull together the budget, the contract clause, and the customer’s last complaint because they know it all by heart. Most enterprise AI fails that test, because the information sits scattered across applications that store it in incompatible formats. I describe the full loop as Connections, Context, Reasoning, Actions, and Governance (CCRAG).
  Connections feed in raw information such as transactions and service records. Context weaves it into a context graph, which is a single connected picture of the business that gives agents something close to memory. Reasoning makes the decisions. Actions carry them back into the business systems. Governance keeps things aligned with the company’s intent.
  
  Most organizations obsess over the reasoning in the middle and underinvest in context and governance, which is exactly where humans play a role. Context compounds: Every interaction makes the graph richer and harder to reproduce.

• Learn to lead by exception. The most important change in habit comes last. Most of us have been trained to check every report and every number because we never knew where an error could surface. In a governed system, the machine tells you which cases it could not resolve confidently. Routine workflows go untouched, and your attention goes to the small portion that is ambiguous, unfamiliar, or high stakes.
  
  At first, that might feel like losing control, but it is the opposite. It is what makes a self-scaling enterprise possible, an organization whose output grows without its head count growing in proportion. People were not removed from the loop; they were raised above it.

The identity question

When I talk with people about the shift, their resistance is rarely about technical issues. More often it is about identity: If the AI does the doing, what do I do?

I have watched capable people freeze on that question. I’ve also asked myself the question.

Doing was never really the job, though. Judgment was. Doing was just how we expressed it.

AI has not made judgment less valuable. It has made it the scarcest resource in the organization because, for the first time, one person’s judgment, written down well, can guide thousands of decisions each day.

Judgment has a twin we talk less about: taste. Judgment tells you whether an answer is sound. Taste tells you whether the question was worth asking and which of a hundred defensible options to offer the customer. A machine will happily generate all 100 options, but it cannot tell you which is best.

The AI transition rewards instincts many IEEE members already have: systems thinking, precision about requirements, and honesty about failure modes. The tools have changed, but the discipline has not.

Governing is where taste and judgment stop being soft words and become the work itself.

The people who treat it that way, rather than as a step away from engineering, will define the profession in the age of AI.

Christos Cassandras has spent more than 40 years studying how computers and machines make decisions. Lately, his work has focused on a problem nearly every driver knows well: sitting at a red light with no cross traffic in sight, wondering why it takes so long for the light to turn green.

Cassandras, an IEEE Life Fellow, is a professor at Boston University, where he headed its systems engineering division, a graduate program he helped create.

Christos Cassandras

Employer

Boston University

Title

Professor

Member grade

Life Fellow

Alma maters

Yale, Stanford, and Harvard

He is the recipient of this year’s IEEE Intelligent Transportation Systems Society Outstanding Research Award for his work on autonomous vehicles and the systems that help them drive—work that could one day get rid of traffic lights altogether.

That idea—proving with mathematical models and intelligent algorithms using cars’ relative speeds, mass, and distance as inputs that self-driving cars will behave safely before they are ever let loose on the streets—has become the foundation of Cassandras’s career and the reason his name keeps coming up in conversations about transportation.

In the 1990s he theorized that a machine such as a car could be understood as two things at once: an object obeying the laws of physics and a computer processing information and making decisions. This led to what we now call cyber physical systems. That framework is still the standard way engineers describe a car’s mechanical behavior.

“Ultimately, the key word is safety,” Cassandras says. “You would not buy a self-driving car unless the manufacturer could guarantee that it’s safe for you.”

Growing up in Greece during a dictatorship

Born in Athens, Cassandras was a teenager during Greece’s military dictatorship, a period he says scarred him and many others but also taught him some hard lessons. His father was a partner in an insulation manufacturing company, and his mother was a homemaker.

His friends pulled Cassandras toward engineering. As a teenager, he ran with a group of classmates who read about philosophy, history, and science. They were especially struck by the 1969 Apollo 11 moon landing and the questions it raised about the technology that made it possible.

That same year also marked the first time a piece of data was sent from one computer to another—an early step toward the creation of the Internet. Several inspiring teachers pushed his curiosity about technology even further.

He was awarded a scholarship to study in the United States, and he enrolled at Yale, where he first studied physics and philosophy before switching to engineering. He earned his bachelor’s degree in engineering and applied science in 1977. At the time, Yale didn’t yet offer separate degrees in fields such as electrical or mechanical engineering.

From there, Cassandras went to Stanford, earning a master’s degree in electrical engineering in 1978. There, he became interested in game theory, the mathematical study of decision-making and strategy.

A mentor pointed him to Harvard, home to a leading game theory researcher at the time. His new mentor, Professor Yu-Chi “Larry” Ho, now an IEEE Life Fellow, advised him to abandon game theory, which Ho considered a scientific dead end at the time. Ho suggested a newer, less-explored area of research, that of emerging dynamic systems in modern technology whose behavior could be understood and managed through occurrences of discrete events.

Cassandras took the advice. He earned a second master’s degree from Harvard the following year and completed his Ph.D. in applied mathematics there in 1982. He and Ho remain friends and colleagues.

Fiat assembly line births of a new kind of engineering

Ask people in intelligent transportation systems circles why Cassandras’s name carries so much weight, and the answer traces back to a theoretical shift he helped pioneer starting in the 1980s. Cassandras recognized that human-made systems—computers, factory machines and, eventually, vehicles—operate on a different kind of logic than the one that governs the natural world.

The physical world runs on time-driven physics, the same math Isaac Newton used centuries ago. But machines built by people, he realized, are better understood as event-driven: They mostly sit still until something happens, like a button click or a part arriving on a conveyor belt.

That insight didn’t come from a textbook. It took shape during Cassandras’s early graduate work, when he was handed a real-world problem. He was assigned to figure out how to manage the buffers—the temporary holding areas for car parts—on an assembly line for Fiat, the Italian carmaker.

Solving that concrete, practical puzzle helped him see how event-driven thinking could describe an entire class of systems that classical physics-based math couldn’t handle well. This came to be known as discrete event systems.

By the early 1990s, that insight had matured into a formal framework: hybrid systems theory, now known as cyber physical systems theory. The idea is that a machine can be understood as an object obeying the laws of physics and as a computer obeying event-driven dynamics.

Building “event-driven” systems

After completing his doctorate, Cassandras wanted to test himself outside of academia. He spent about a year and a half as a systems engineer at Information and Technology for Production (ITP), a small manufacturing-automation startup in the Boston area. The experience convinced him that the “real world” wasn’t so different from academic life, and he returned to research while continuing to consult for the company for roughly a decade. He has kept up the habit of staying connected to industry throughout his career.

In 1984 he joined the University of Massachusetts Amherst as a faculty member in the electrical and computer engineering department. Building on the ideas born out of the Fiat project, he helped pioneer research into event-driven systems.

That was a shift away from the traditional math used to describe the physical world toward systems built around discrete events. The transition, driven by the rise of computers, became the backbone of Cassandras’s research for the next three decades.

In 1997, after years of commuting weekly between Amherst and Boston for his consulting work, he joined Boston University as a tenured full professor in what was then its manufacturing engineering department. As the field evolved in the early 2000s with the rise of the Internet and sensor technology, Cassandras helped establish the university’s systems engineering division. In 2008 he became the research program’s first director, a role he held until stepping down two months ago.

Using math to show that self-driving cars are safe

Cassandras’s recent research has focused on mathematically proving vehicle safety before a car is road-tested. He has developed models built around measurable quantities: a vehicle’s position, speed, and distance to nearby vehicles. That lets connected and automated vehicles cooperate during tricky maneuvers such as changing lanes on a highway in traffic.

He has extended the approach to a bigger idea: eliminating traffic lights. One of his more striking projects involves a chronically congested intersection near his office in Boston, where Commonwealth Avenue meets the Boston University Bridge. Using computer simulations, he and his team modeled what would happen if they removed all the traffic lights, and cars simply coordinated with each other instead. The results, he says, showed improvements not only in how smoothly traffic would flow but also in safety and energy efficiency.

Cassandras acknowledges that widespread adoption is still far off. For now, only a small percentage of vehicles on the road are capable of that kind of communication. But he’s testing similar coordination systems using small robots in his BU lab, working to bring the idea closer to reality and to exploit the intelligence of cooperating autonomous vehicles even when they are only a small fraction of the actual cars on the road.

He says his perspective has evolved in recent years from viewing humans as obstacles to smooth automation toward designing technology that actually works for people.

Through projects with Boston on smart city initiatives, he learned that not everyone can afford or access new technology—a lesson that reshaped how he approaches his research.

“It’s not just about technology,” he says. “It’s really about technology and people.”

A legacy passed on through his students

His influence in the field has been amplified by some of his students. Several of his Ph.D. students have developed safety algorithms at companies including Aptiv and Zoox, both of which build technology for autonomous and connected vehicles. He wrote a book, titled Safe Autonomy with Control Barrier Functions: Theory and Applications, with one of his former students. It lays out the mathematical foundations of autonomous-vehicle safety, with specific applications to intelligent transportation.

A long relationship with IEEE

Cassandras joined IEEE as a student member while he was a graduate student. He was drawn in by the discounted student conference registration fees, he says.

That early, practical decision grew into something bigger: a professional community with which he has stayed connected for more than 40 years.

He has served as president of the IEEE Control Systems Society, his primary professional home within IEEE. He has served on the conference publications committee and is currently involved with IEEE Publishing Ethics.

IEEE’s greatest value to his career, he says, has come through the relationships he has built at its conferences and serving on committees. Connections with colleagues and IEEE leadership have shaped his thinking and opened doors throughout his career, he says.

Looking back on more than four decades of work, Cassandras says the goal has never really been about the technology for its own sake.

“After all,” he says, “we’re doing all this to make society better and facilitate the comfortable lives of all of humanity.”

Building useful robots starts with understanding the people who use them. For Charlie Kemp, cofounder and chief technology officer of Hello Robot, that means developing assistive robots that can help people with everyday tasks and support greater independence.

In this Robots Guide profile, Kemp shares his path from studying artificial intelligence at MIT to building Stretch, explains how working with people with disabilities has shaped his approach, and offers advice for aspiring roboticists. Read the full profile on IEEE’s Robots Guide.

IEEE TryEngineering is dedicated to inspiring intellectual curiosity in children.

The technologies shaping our world, including in the realms of artificial intelligence, electric vehicles, and ocean exploration, are evolving rapidly. Helping young learners understand the concepts is essential to preparing the next generation of problem-solvers, creators, and engineers.

TryEngineering has introduced a STEM book series for youngsters ages 8 to 12 through the Lerner Publishing Group.

The series, Tomorrow’s Technology With TryEngineering, Powered by IEEE, makes complex topics more approachable and engaging, with each book combining age-appropriate explanations, real-world examples, and design challenges that encourage curiosity and critical thinking. The series is based on ebooks and videos available at tryengineering.org.

For the series, TryEngineering partnered with several other IEEE groups including the Communications, Computer, and Oceanic Engineering societies and the Transportation Electrification Council.

Whether used in the classroom, a library, or at home, the books can help pupils connect STEM concepts to the technologies they encounter every day, including computers and smartphones.

Six topics in the collection

Here are the books in the new collection:

Artificial Intelligence: The Future of Smart Technology explores the systems behind streaming services, search engines, and health care. Readers learn how AI works while exploring ethical concerns such as bias, deepfakes, hallucinations, and privacy. Pupils can better understand one of the most influential technologies of our time as it evolves.

Communication Technology: From Morse Code to Smartphones teaches readers about smoke signals, semaphore towers, telephones, and wireless networks. The pupils can gain an understanding of how engineers are changing communications technology through innovations such as 6G and space-based networks. The book highlights career opportunities in the aerospace and telecommunications fields.

Electric Vehicles: Powering the Future of Transportation covers how e-cars, e-bikes, e-scooters, and electrified trains are transforming the way people travel. Readers can discover how hybrid and fully electric vehicles operate and how the batteries that power them work. They also can learn about the roles engineers play in developing smarter transit systems.

Ocean Engineering: Protecting Our Ocean Environments highlights the vital ecosystem role played by the world’s oceans, which require careful stewardship. play in our ecosystem. Readers can learn how engineers study the underwater world using submersibles and floats, how they address pollution, and how they protect marine environments.

Semiconductors: The Building Blocks of Modern Electronics focuses on the technology behind nearly every electronic tool we use. Microchips power smartphones, computers, and countless other products, all thanks to semiconductors. Readers can learn about insulators and conductors, how microchips are manufactured, and why semiconductor engineering is a fertile field for innovation.

Signal Power: The Hidden Waves Behind Modern Tech explores how engineers analyze and manipulate signals to make technologies work more effectively. Whether it is a phone call reaching the correct person despite background noise or a medical device monitoring a patient, signal processing plays crucial roles in modern life. The book introduces different wave types, the signal processing workflow, and careers in the field.

The book series can help children understand the technologies shaping the world around them while encouraging them to think like engineers and innovators. By connecting STEM concepts to real-world applications, the series can make learning more meaningful and engaging.

The Tomorrow’s Technology With TryEngineering series is available through Amazon, Bookshop, and Lerner. More about the collection may be found here.

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!

If you have kids, they’re probably back in school right now after the summer break. Mine are too.

My kids range from 9 to 20 years old, and lately I’ve been thinking a lot about what their future careers are going to look like.

Some schools are embracing AI in the classroom. Others are banning it completely. I’m not sure either side has figured out the right answer yet—I’m not sure any of us have.

What I do know is that AI is already changing how engineers, and nearly every other knowledge worker, does their job. So I’ve been thinking: What skills do I actually want my kids to develop for an AI-augmented workforce?

1. Learn to write

The more I use AI, the more convinced I am that it’s a multiplier, not a substitute.

Give a great researcher AI tools and they can accelerate their research. Give an experienced software engineer the same tools and they can build at an incredible rate. But put those tools in the hands of someone without foundational knowledge and you often get something else you’re used to seeing way too much of: slop.

We’ve all seen the vibe-coded applications that barely work. Our inboxes are filled with emails that sound suspiciously identical. Teachers are reading papers that sound like they were all written by the same person. That “person” has the last name GPT.

When everyone has access to the same tools, having an actual voice becomes a differentiator.

Ironically, strong writing skills might be MORE important because of AI, not less. Text is still the primary way we communicate with these models, so being able to clearly articulate what you want improves what you get back.

But more importantly, writing teaches you to develop ideas of your own.

AI can help you express your opinion. It shouldn’t manufacture one for you.

2. Learn to speak

Like written communication, speaking skills are now at a premium—but for a different reason.

We live in a strange moment. We’re more digitally connected than ever, while often feeling increasingly isolated from one another. At the same time, we’re seeing renewed interest in conferences, meetups, communities, and in-person experiences.

When human interaction feels scarce, communication becomes more valuable.

So learn how to explain an idea in front of a room. Learn how to disagree without being disagreeable. Learn how to tell a story. Learn how to listen. Learn how to persuade someone.

There’s a timeless book, originally published 90 years ago, that teaches these interpersonal skills, and it’s probably more valuable for engineers than another white paper on how neural networks work: How to Win Friends and Influence People.

An AI can generate a presentation for you, but it can’t convincingly deliver it to a skeptical audience. That takes emotional intelligence.

3. Learn to be bored

This might be the hardest one.

We have engineered boredom almost completely out of our lives. There’s always a podcast to listen to, a notification to check, a video to watch, a feed to scroll, or now an AI to talk to.

Go for a walk without headphones. Eat without looking at a phone. Sit in the car without immediately reaching for something to fill the silence, and let your mind wander.

Because boredom isn’t wasted time. It’s a breeding ground for original ideas.

The danger I worry about isn’t that AI becomes too intelligent, but that we become too willing to outsource the uncomfortable parts of thinking.

The students going back to school today will enter a workforce filled with technology that I couldn’t have imagined when I was their age. I have no idea what the dominant AI model will be by then or even what interacting with a computer will look like.

That’s exactly why I don’t want to optimize their education around today’s tools. I want them to learn the skills that will outlive the tools.

Write clearly. Speak confidently. Think independently.

And every once in a while, embrace boredom.

—Brian

AI Slop Is Changing How Engineers Review Code

Software engineers have entered a new era of code review. The strategies they’re now testing will determine whether AI can actually provide code that’s faster and more reliable when you factor in the review process. If it can, what does that mean for the entry-level engineers who are still learning to code on their own?

Read more here.

U.S. Tech Firms Change Strategies to Hire International Talent

In response to a barrage of actions by the U.S. federal government to limit legal immigration, tech companies are adapting their search for top talent. IEEE Spectrum looked into the responses to proposed changes, like higher fees for H-1B visa applications.

Read more here.

What It Takes to Be an Adaptable Engineer

As AI changes the job market and day-to-day work of engineers, young professionals are often told they need to be adaptable. But what does adaptability actually look like in practice? The skill has different definitions depending on who you ask, but with the right mindset and support from leadership, adaptability can help keep you afloat.

Read more here.

Many youngsters fascinated by exploring outer space dream of becoming an astronaut, but few do. One who had the right stuff is Pedro Duque, who was Spain’s first astronaut. The aeronautics engineer flew aboard the space shuttle Discovery and the International Space Station.

After retiring as an astronaut, he headed Spain’s Ministry of Science, Innovation, and Universities. Today he is the chairman of HispaSat, a Spanish satellite company.

Pedro Duque

Employer

HispaSat in Madrid

Title

President and chairman of the board

Member grade

Honorary member

Alma mater

The Polytechnic University of Madrid

Nearly 30 years after his first mission, Duque is still Spain’s most famous astronaut. Three public schools have been named after him, and he has received numerous awards. This year, the IEEE Board of Directors made him an IEEE honorary member for “contributions to space exploration, leadership in collaborative science and technology programs, and serving as a role model for younger generations.”

He was unable to attend the 24 April ceremony in New York City, but he expressed his gratitude in recorded acceptance remarks in his award presentation shown during the event.

“We engineers of all specialties recognize the leadership of your institute—the largest and most important engineering society in the world,” he says in the video. “What an honor it is to belong now to an organization whose purpose it is to foster technological innovation and excellence for the benefit of humanity.”

Inspired by Apollo 11

Duque says he knew he wanted to be an engineer from a young age. It’s not surprising that he became interested in aeronautics, as his father was an air traffic controller who explained to him how airplanes worked.

In 1969, when he was 6 years old, he was inspired to become an astronaut after watching the Apollo 11 moon landing.

“I didn’t know anyone who wasn’t attracted to space exploration,” after the moon landing, he says, laughing. “It was presented in such an epic manner, with declarations about its impact on society. The landing made us aware that humanity was exploring new places, and I was keen on knowing more about them.”

His dream of becoming an astronaut was unrealistic at the time, he says, because the country had no space program. Spain was ruled by Francisco Franco, who spent little to no money on scientific innovation, Duque says.

That changed after Franco died in 1975. The country transitioned to a democratic constitutional monarchy and began participating in research and development programs, particularly with the European Space Agency (ESA).

Astronaut duties

Duque earned an aeronautical engineering degree in 1986 from the aeronautical and space engineering school at the Polytechnic University of Madrid.

His first job was as an engineer in the flight dynamics group at GMV, a space and technology company based in Madrid. He was a member of the orbit determination group and worked at ESA’s European Space Operations Centre, in Darmstadt, Germany. He helped develop algorithms, orbit computational software, and computer models.

He was also a member of the space agency’s flight control team for the European Remote Sensing-1 satellite, launched in 1991, and the European Retrievable Carrier, launched in 1992 on the space shuttle Atlantis’s STS-46 mission.

In 1990, ESA recruited candidates for its astronaut program—which Duque says rarely happens. He and several colleagues applied.

“Why not?” he says. “What we thought we wanted to be when we were little was now possible.”

After a considerable selection process, Duque was chosen in 1992 to join the agency’s Astronaut Corps. He trained at the European Astronaut Centre and at the Russian Cosmonaut Training Center (now known as the Gagarin Research and Test Cosmonaut Training Center).

In 1994, he served as the prime crew interface coordinator on the ESA–Russian EuroMir space station. He managed communication between the astronauts on the Mir station and the European scientists to help ensure orbital experiments and operations ran smoothly.

NASA selected Duque to be an alternate payload specialist on the ground for space shuttle Columbia’s STS-78 mission in 1996. In that role, he was trained to operate and manage scientific experiments, equipment, and cargo during a crewed mission. He also supported the flight from the ground as a crew interface coordinator.

His first flight into space was in 1998 as a mission specialist representing the ESA on the space shuttle Discovery’s STS-95 mission. He managed in-orbit tasks, experiments, spacewalks, and equipment operations.

He served as a flight engineer in 2003 onboard the Soyuz TMA-3 Cervantes, a joint mission between Russia and Spain to the International Space Station. He operated the station systems, managed daily maintenance tasks, and performed scientific experiments assisted by the mission commander.

Memories of flying with John Glenn

The years of training and preparation to become an astronaut were rigorous, he says, but the experience was fulfilling. Even though he spent only about 10 days on each mission, he says, “they were very rewarding times because all the preparation paid off, and we got the results we needed.”

Some of his favorite memories were viewing Earth during the day and night for the first time, and watching it pass from one phase to the other. Another was seeing the moon flattened to almost a sliver during the few seconds before it set. Experiencing microgravity was a thrill as well, he says.

“I also cherish the companionship of the people who worked alongside me in space and on the ground,” he adds.

One was John H. Glenn, the first American to orbit the Earth. Duque flew with Glenn on Discovery. They talked about the selection criteria for astronauts. Glenn told him those on board the Apollo and Gemini missions were chosen because they were test pilots, who were thought to be most likely to handle problems or failures effectively and proactively.

“The technology in today’s missions is advanced enough that astronauts won’t be needed to handle probable catastrophic equipment failures,” Duque says. “Instead, they will perform experiments and update or fix the devices used in space capsules. But flights to the moon and even Mars will use new types of spacecraft that might not necessarily work as planned, so those astronauts will have to know how to fix them and possibly solve critical problems on their own.

“Also, future astronauts will have to live with others in a confined space for months, and not everyone can do that.”

The price of sudden fame

As Spain’s first astronaut, Duque became a celebrity. Among his honors are Russia’s Order of Friendship and Spain’s Great Cross of Aeronautical Merit and Princess of Asturias Award. He also received NASA’s Space Flight Medal, which is given to an astronaut who flies aboard a U.S. space mission.

Learning to navigate sudden fame and being treated like a celebrity was challenging, Duque says. Like many engineers, he was accustomed to working behind the scenes and out of the public eye.

“Being famous, both in the profession and the public, was quite difficult in the beginning,” he says. “Being a celebrity doesn’t come easily to me, but after so many years, somehow I learned how to deal with it.”

People might assume that an astronaut’s leadership skills come effortlessly, Duque says, but that’s not always the case.

“Everybody gives so much importance to your opinion, and sometimes I was surprised by that,” he says. “Being an astronaut, you tend to have a certain kind of leadership style because it’s what you have done for years without knowing it.”

Minister of science and other leading roles

His leadership style has served him well. After he retired from the ESA in 2018, he was appointed as Spain’s minister of science, a role he held until 2021. He oversaw the government’s policies on scientific research, technological development, innovation, space programs, and higher education. During his term, Spain committed to contributing US $800 million (€701 million) between 2020 and 2026 to the ESA—which at the time was the largest overall investment in the agency’s history.

After retiring as an astronaut, Pedro Duque headed Spain’s Ministry of Science, Innovation, and Universities. Today, the IEEE honorary member is the chairman of HispaSat, a Spanish satellite company.Isaac Buj/AP

In 2022, he joined Destinus Spain, a European defense manufacturer that develops technologies for aircraft propulsion and auxiliary systems. He advised its strategic committee.

The following year, Spain’s government appointed him president and chairman of the HispaSat board. Headquartered in Madrid, the satellite operator provides broadcasting and broadband services for Europe, North Africa, and the Americas. The public-private partnership was in its origin a joint initiative between the Spanish government and private telecom companies.

The IEEE honor was a surprise

Duque says he was surprised to learn that IEEE added him to its membership ranks. He was aware that a colleague had nominated him, but he deemed it unlikely the nomination would be supported.

“When I started looking into who its members were, I wondered why they selected me,” he says. “Obviously, electrical engineering is not my branch, but it could have been, because growing up, I was just as interested in telecommunications as I was in aeronautics.

“Most engineers know IEEE for its standards and the work it does in achieving consensus in standards development.”

After discussing with several colleagues who were IEEE members about the significance of the IEEE honorary membership—which is bestowed for a significant achievement and impact on society— Duque feels the award is a significant honor.

“I’m still in the early phase of figuring out how I can contribute to IEEE,” he says, “and what I can do for the many hundreds of thousands of members, all whom have impressive qualities.”

Throughout her career, roboticist Barbara Mazzolai has turned to nature for inspiration. Now she wants to ensure the technology she builds gives back to the environment, too.

After starting her career as a biologist, a chance opportunity saw Mazzolai switch streams to engineering and become an early pioneer of bioinspired robotics. Building on her knowledge of biology’s ability to solve a diverse set of problems, she has developed robots based on octopuses, plant roots, and even seeds. “I’ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature, selected by the evolutionary process,” she says.

Barbara Mazzolai

Employer:

Italian Institute of Technology

Occupation:

Associate director for robotics; director of the Bioinspired Soft Robotics Laboratory

Education:

Master’s degree in biology, University of Pisa; master’s degree in eco-management and audit schemes, Scuola Superiore Sant’Anna; Ph.D. in microsystems engineering, University of Rome Tor Vergata

But Mazzolai, now the associate director for robotics at the Italian Institute of Technology, in Genoa, also believes engineering needs to reckon with its own impact on the natural world. That’s why she is advocating for a new field of research she calls “sustainability robotics.”

In a manifesto published in Nature Machine Intelligence in July, she and her collaborators outline a vision for a new approach to designing robots that’s meant to improve the relationship between nature, humanity, and technology.

“We need to reduce the footprint of our technology,” she says. “It’s really about thinking in a different way to open new possibilities for robotics [and] for society.” In this new mode of thinking, Mazzolai considers sustainability a core component of the design.

A child of nature

Mazzolai traces her fascination with the living world back to her childhood growing up on Italy’s Tuscan coast, close to the port city Livorno. Her father was a public-health inspector and a professional mycologist, and the family spent a lot of time exploring forests and learning about the local fungi and plants.

After toying with the prospect of pursuing art, her other major passion, Mazzolai ultimately decided to enroll at the University of Pisa in 1987 to study biology. She was particularly drawn to marine biology, but shortly before graduating with a master’s degree in 1995, she secured a research position at the Italian National Research Council’s Institute of Biophysics studying the cycles of heavy metals like mercury through both living and nonliving parts of the environment.

This involved collecting and analyzing samples from water, soil, vegetables, and even humans to understand the impact these metals have on health and the environment. She balanced this work with studying environmental management at the Scuola Superiore Sant’Anna, in Pisa, graduating with a master’s degree in 1998.

During that time, however, she learned that the university was recruiting biologists to help design new devices for environmental monitoring. She applied for and got the job in 1999 and began working as a research assistant under renowned bioroboticist Paolo Dario, first developing sensors and then robots meant to monitor air, water, and soil.

Even before entering a doctoral program, Mazzolai was promoted to assistant professor in 2004 and shortly afterward made her first foray into bioinspired robotics. In collaboration with colleagues at Sant’Anna, she helped design a soft robot inspired by the octopus. “We proposed it as a paradigm for launching this idea of soft robotics: demonstrating that [robots] can be soft, but at the same time apply strong force to the environment, like the animal does,” she says.

Back to school

In 2007 Mazzolai enrolled in a Ph.D. in microsystems engineering at Tor Vergata University of Rome, which she balanced with her role at Sant’Anna. She was already relying heavily on microfabrication techniques to develop sensors for her robots, and she was keen to push that part of the field forward.

While robots frequently feature sensors designed for perception, such as tactile or proprioceptive sensors, these systems typically focus on understanding the robot’s position in its environment, she says. “But there are few robots that integrate physical or chemical sensors to really understand the environment they move in,” she adds.

“I’ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature.”

Mazzolai was appointed as a team leader at the Center for Micro-BioRobotics of the Italian Institute of Technology in 2009, where she continued her work on the emerging field of bioinspired robotics. Two years later, she completed her Ph.D. and was promoted to director of the center.

Planting the seeds

Around this time Mazzolai says she became interested in using plants as a model for new kinds of robots, expanding bioinspiration beyond just animals. In particular, she was captivated by the ability of roots to efficiently explore the underground environment, and she imagined machines with the same deftness could have applications in both environmental modeling and precision agriculture.

While many bioinspired robots mimic animals, plants also serve as a muse for Mazzolai. This tendril-like bot can coil around other structures like a vine. Italian Institute of Technology

When she first proposed the idea, colleagues were somewhat skeptical of robots based on seemingly static organisms. But in reality, she says, plants move nonstop through a process known as indeterminate growth. “They really grow for their entire life,” she says. “They adapt their morphology, their behavior to the external environment; they repair, they sense, they communicate.”

Trying to mimic a system that operates on such different principles to conventional robotics required some serious thinking, however. Mazzolai says that working in bioinspired robotics sometimes requires you to have “two separate brains”—one of a biologist and one of an engineer.

The process often involves deep study of the target organism to learn the underlying principles that shape how it operates before trying to engineer a robot capable of mimicking them. “It’s not a copy of natural organisms,” says Mazzolai, because a living organism is both difficult to replicate and has different goals.

In the case of plant roots, what makes them so efficient at exploring the soil is that they reduce friction by growing only at the very fine tip of the structure, while the thicker base of the root remains static. This significantly reduces the amount of energy required to push through the earth compared to that of a more conventional drill, which must push the entire structure from above.

To realize this principle in a robot, her team developed a miniaturized 3D printer that sits at the machine’s tip and feeds thermoplastic filament through a heated nozzle to build a snakelike body behind it. This allows the robot to push through the soil efficiently. The tip also contains sensors that allow it to avoid obstacles and detect nearby nutrients or water.

Making robotics sustainable

After spending so much of her career borrowing from nature, Mazzolai is now eager to return the favor. Many modern technologies, including plastics and car batteries, have been developed with little thought about how they will affect the environment at the end of their life cycles, she says.

She wants to ensure that robotics doesn’t follow the same path. This is the inspiration for what she and collaborators now call sustainability robotics. The approach has three central pillars: ensuring that robots have minimal impact on the environment; that they’re available to people from across the world and all socioeconomic backgrounds; and that they’re “symbiotic,” providing benefits to both humans and nature.

More concretely, Mazzolai would like to incorporate the concept of a life cycle into the design of robots, so that at the end of their useful life these machines can be reused, recycled, or even biodegraded.

While that might sound ambitious, she’s confident that all the ingredients to make it a reality are in place. And it’s a vision that she is certain will inspire future roboticists. “There are younger people who want to really work in this field because this is the future, their future,” she says. Facing the threat of ongoing environmental damage, “they want to develop something that can help.”

Transitioning from years of working in a senior technical or executive role to a leadership position is one of the most challenging phases of a STEM career. It requires moving away from making decisions on your own to mentoring others, making strategic decisions for the organization, and collaborating with coworkers from different generations.

The shift in mindset is known as “legacy leadership,” a philosophy whereby success is no longer measured by personal achievements but by how effectively a senior leader empowers others.

To help seasoned professionals and senior experts navigate the transition, the inaugural IEEE International Leadership Conference (ILC) will provide attendees practical advice on cultivating collaborations and guiding emerging talent.

“Early in our STEM careers, we measure success by what we have achieved,” says Jeewika Ranaweera, cochair of the IEEE ILC program committee. “Later, we should measure success by what we enable, how many people we mentor, how much knowledge we transfer, and how many doors we open for the next generation.”

The ILC is scheduled for 3 and 4 October in Budapest. Registration is open.

Letting go of the “expert” identity

For decades, seasoned technologists have been valued primarily for their technical expertise. Shifting from that identity can feel uncomfortable, but legacy leadership requires measuring success by different standards. They include a leader’s influence on the staff, the ability to uphold the company’s mission, and empowering others to lead and succeed.

The transition requires leaders to find purpose outside their corporate titles, shifting their focus to the long-term sustainability of their teams, their organization, and the broader technical community.

“A professional legacy is not measured only by what we have achieved but also by sharing our knowledge, experience, and opportunities with others,” says Sudhanshu S. Jamuar, another program committee cochair. “The real transition from expert to a legacy builder happens when we stop asking, ‘What more can I accomplish myself?’ and start asking, ‘How many others can I enable to accomplish more?’”

Neeli Rashmi Prasad, IEEE ILC treasurer and sponsorship cochair, adds that the transition transforms a lifetime of technical work into a platform for future innovation.

“The true value of experience is not in how much knowledge we accumulate but in how intentionally we transfer it,” Prasad says. “When we partner with, mentor, and create space for others to lead, our expertise becomes a foundation for progress far beyond our own careers.”

Moving beyond advice-giving

True knowledge transfer requires coaching rather than advising, and mastering the art of active listening. To build deep trust with early-career colleagues and ensure a seamless transfer of leadership to the next generation, senior leaders must avoid offering unsolicited or outdated anecdotes. Effective mentorship is a collaborative loop in which senior experts contribute hard-won industry wisdom while remaining curious and learning from the fresh, cutting-edge perspectives of talented coworkers.

“Knowledge transfer is most powerful when it is a two-way bridge: experience flows from one generation to the next, while new ideas and perspectives flow back,” says Sudeendra Koushik, president of the IEEE Technology and Engineering Management Society and an ILC keynote speaker. “This approach transforms mentorship from simply passing on information into one where the next generation can question, experiment, innovate, and ultimately surpass what came before them.”

Seasoned professionals should encourage independent, disruptive thinkers rather than carbon copies of themselves, Prasad says.

“Legacy leadership is about building continuity,” she says. “We should not simply prepare the next generation to follow the paths we created; we should give them the confidence, knowledge, and networks to challenge those paths, create new ones, and take technology further than we imagined.”

Designing your next chapter

Leadership does not need to stop when one’s job ends; it can evolve. Seasoned, retired professionals can continue contributing meaningful service through pathways that align with their personal passions. They include:

• Advisory boards: steering corporate, technical, or community organizations.
• Civic engagement: applying engineering methodologies to solve community challenges.
• Volunteerism: mentoring the next generation of grassroots innovators through professional networks including IEEE.

Those pathways offer experienced professionals an opportunity to redefine success, not in terms of position, authority, or personal achievement but in terms of sustained impact.

“Retirement from a job should never mean retirement from purpose,” Koushik says. “Our experience becomes even more valuable when we use it in service of the profession, society, and the generation that follows.”

At the same time, the collaborative continuum relies on a proactive younger generation. Emerging leaders need to take responsibility for building their professional connections, Prasad says, advising: “Be bold, stay curious, and build your network early!”

A space for continuity

The conference is designed to combine a drive to cultivate emerging innovators with a deep reservoir of industry stewardship and strategic perspective. Rather than a single classroom session, the conference will feature a dedicated career-readiness and mentorship track where attendees can explore practical frameworks for building strategic professional networks, connecting with peer advocates, and establishing reciprocal knowledge-sharing opportunities across generations.

By participating, seasoned professionals can ensure their decades of expertise continue to yield dividends for generations to come.

“Our professional legacy is not the technology we build, the titles we earn, or the awards we receive,” Ranaweera says. “It is the knowledge we share, the lives we influence, and the future we help others create.”

You can view the agenda, read speaker biographies, and secure your seat at the ILC online.

Over the last 19 months, the U.S. federal government has created a series of high-profile barriers to legal immigration by highly-skilled students and workers: Cancelling visa extensions, cutting interview opportunities, and attempting to charge US $100,000 or more for certain immigrant visas.

“Whether they go into effect or not, it’s really an unrelenting barrage of government-sponsored propaganda against highly-skilled workers in the U.S.,” says former congressional immigration policy advisor David Bier, now an immigration scholar at the Cato Institute, a libertarian think tank.

The effect is that U.S. tech companies are adapting their search for top talent in part by both building offices and hiring workers abroad to get around the onslaught of new rules. And they’re doing so without wanting to talk about it: IEEE Spectrum contacted 25 U.S. tech companies to learn how they were adapting to the shifting environment. All of them either did not respond to a request to comment or declined to participate in this story.

New H-1B Rules Create Uncertainty, Impose Costs

By July 2025, the new U.S. presidential administration tightened visa interview requirements and imposed social media checks on new visa applicants, and international students were already showing less interest in studying in the U.S., which IEEE Spectrum reported at the time.

Then, in September 2025, U.S. President Donald Trump proclaimed a US$100,000 tax on applications for new H-1B visas, up from around $5,000. H-1B visas allow certain skilled workers to temporarily live and work in the U.S. and serve as a common bridge to permanent residency. One category, for workers at universities and affiliated hospitals and other non-profit institutions, is uncapped, but for-profit companies together can only hire up to 85,000 H-1B workers a year across the U.S., assigned through an annual lottery.

Tech giants including Apple, Google, Microsoft, and Walmart advised some of their visa-holding employees not to travel abroad for fear of having trouble re-entering the country. Two days after the proclamation the federal government narrowed it to applications from outside the U.S., which comprised less than half the applications for H-1B visas in the previous year.

In fact, most companies that might have hired H-1B workers decided against applying for visas for candidates subject to the $100,000 tax, a March court filing revealed. The Department of Homeland Security (DHS) recorded collecting fees for only 85 qualifying H-1B applications between the presidential proclamation in September 2025 and 15 February 2026. The department also reported processing 87 percent fewer applications for the H-1B visas subject to the tax than the year before. The government ended up earning about $28 million less than the previous year on H-1Bs, because so few companies were willing to pay the new price for hiring H-1B workers from abroad.

“Some of those jobs might just be relocated abroad, or maybe companies will make more efforts to recruit U.S. workers,” says sociologist Julia Gelatt, an immigration demography scholar at the Migration Policy Institute in Washington, D.C. “Some immigrant workers may have found a different pathway, like the L-1 visa for transfers inside multinational companies, or the highly-skilled may have been able to get O-1 visas for extraordinary ability.”

“Whether they go into effect or not, it’s really an unrelenting barrage of government-sponsored propaganda against highly-skilled workers in the U.S.” —David Bier, Cato Institute

The U.S. government also continued targeting foreign students. In July, it set a fixed 4-year time limit on student visas. In the past, those visas lasted as long as the student’s course of study. The federal government is also studying a proposal to charge students on such visas between US $70,000 and $100,000 to stay for 1 to 3 years after graduation—something that they can currently do for free. “If there is a $100,000 fee for that, it’s going to cut the talent pipeline,” Gelatt says.

A non-peer-reviewed discussion paper by economist Michael A. Clemens of Johns Hopkins University’s School of Government and Policy in Washington, D.C., estimates that the proposal to put time limits on student visas could cost U.S. universities between hundreds of millions and several billion dollars a year. That’s without getting into the longer-term cost to the wider economy: Many foreign students who remain in the U.S. eventually apply for H-1B visas. “For many talented individuals, the H-1B is the only way to stay after studying here and eventually getting a green card,” Gelatt says.

For a few weeks earlier this year, things were looking better for H-1B applicants: In July, the U.S. Court of Appeals for the First Circuit (covering Maine, Massachusetts, New Hampshire, Puerto Rico, and Rhode Island) upheld a lower court ruling that the September 2025 H-1B tax was illegal, removing that particular barrier. But a few weeks later, on 25 August, the administration responded with a proposed rule that the Department of Homeland Security (DHS) charge $103,265 for the 85,000 H-1B visas it issues annually to for-profit companies. If it goes into effect, the rule would apply to a far larger number of people than the overturned 2025 proclamation, because even applicants already in the country would need to pay.

“This is a pivot to find another way to enact a policy that was ruled illegal in federal court,” Clemens says.

The proposed rule is open for public comments through 24 September, and DHS must consider those comments before issuing a final rule. Plenty of commenters welcome any barrier to the H-1B program, which they blame for taking jobs from U.S. workers, despite a consensus among economists that foreign skilled workers in the U.S. enable their American colleagues to patent more and earn more.

Other commenters point out flaws in the rule’s legality. As one commenter puts it: “It exceeds DHS’s statutory authority, functions as an unlegislated tax that bypasses Congress, rests on an unsupported revenue projection, and will drive high-skilled talent out of the United States.”

Companies Seek H-1B Workarounds

While few U.S. tech companies have issued public pronouncements on U.S. immigration policy, their staff are talking about it at a pragmatic level: For instance, the Society for Human Resource Management organized a webinar on navigating H-1B policy soon after the 2025 presidential proclamation. The society recommended employers plan for longer hiring timelines, especially from countries such as China, India, and the Philippines, from which many immigrants are hired. It also notes that companies may want to focus on retention of existing talent by helping them switch from temporary visas to permanent residency.

Changes to immigration policy are also reshaping the flow of future workers. As of March, U.S. universities were reporting 20 percent fewer international enrollments in bachelor’s programs than the year before, according to a survey by Studyportals, NAFSA, and Oxford Test of English. It was worse for master’s degrees: a 24 percent annual decline. 84 percent of the participating universities listed government policy as a significant obstacle to enrollment.

Many U.S. tech companies have historically hired foreign students to work summer internships or part-time while still on their student visas, or in the immediate years after they graduate. Experts say that is good for both workers and companies. “Companies don’t want to have to learn how to recruit their talent from abroad. They want to hire the young smart worker who’s right in front of them,” Gelatt says.

Companies also do not appear eager to provoke the federal authorities on whom they depend for visas. Of the 25 U.S. companies IEEE Spectrum contacted for comment, 20 are among the top 100 users of H-1B visas. Only two replied. They both declined to participate in this story.

In unpublished, preliminary research, Clemens says, he and other economists have found clues in online job sites that fewer high-skill Indian tech workers are moving to the U.S. than in the past. “People around the world have very clearly gotten the message,” Clemens says.

This story was updated on 15 September.

Franklin “Frank” Kuo

Codeveloper of ALOHAnet

Fellow, 91; died 14 April

Kuo helped develop ALOHAnet, a pioneering computer system at the University of Hawaii at Mānoa, in Honolulu. The system went online in 1971 and represented the first public demonstration of a wireless packet data network. It was an inspiration for Robert Metcalfe’s development of Ethernet a couple of years later. In 2020 ALOHAnet was designated as an IEEE Milestone.

Kuo earned bachelor’s, master’s, and doctoral degrees in electrical engineering from the University of Illinois, Urbana-Champaign. After earning his Ph.D. in 1960, he joined Bell Labs in Murray Hill, N.J., where he conducted research in computer communications.

After six years at the company, Kuo left to become a professor of electrical engineering at the University of Hawaii. From 1968 to 1971 he and one of his colleagues, IEEE Life Fellow Norman Abramson, developed ALOHAnet. The network connected computers on Hawaiian islands using ultrahigh-frequency radio, transmitting information over radio waves instead of cables.

ALOHAnet became the foundation for modern networks. Kuo pioneered the concept of a random-access protocol, or sharing a single channel without central coordination—which led to the packet-switching principles that underpin modern Wi-Fi and mobile networks.

Kuo authored or coauthored several books including Computer Communication Networks. Published in 1972, it was one of the earliest textbooks on the subject.

He served as director of the university’s Cosine committee, a project funded by the U.S. National Science Foundation to develop computer engineering courses.

He took a sabbatical from 1975 to 1977 to work at the U.S. Pentagon as director of information systems in the defense secretary’s office. He oversaw computer communications applications used in command, control, and intelligence programs.

During the 1980s and ’90s, he helped develop China’s Internet. In 1982 he joined SRI International (formerly the Stanford Research Institute), in Menlo Park, Calif., as a researcher. He also was a consulting professor in Stanford’s electrical engineering department and taught computer networking at Shanghai Jiao Tong University.

As a UNESCO lecturer in Beijing in 1994, he helped Peking University, Tsinghua University, and the Chinese Academy of Sciences connect to the Internet. He also worked with Tsinghua University to develop CERNET, the country’s first nationwide education and research computer network, which was managed by the Chinese Ministry of Education. For his work, he received an honorary degree from Shanghai Jiao Tong University.

In the mid-1990s, Kuo helped found General Wireless Communications, a developer of mobile phone messaging services and games that was renamed Mtone Wireless.

Muhammad Rezaul Karim

Bell Labs researcher

Life senior member, 86; died 18 May

Karim was a distinguished member of the technical staff at Bell Labs in Murray Hill, N.J. His work was instrumental in the development of modern cellular communications technology.

He joined Bell Labs in 1972 and worked in its mobile telecommunications laboratory as part of the team tasked with creating one of the earliest cellular networks.

In 1975 Illinois Bell Telephone petitioned the U.S. Federal Communications Commission to develop and test a cellular system. The FCC, which now regulates radio, TV, telephone, Internet, satellite, and wireless services, authorized the project in March 1977. Karim and his team helped develop key elements of the technology, including the Bell Labs logic that controlled the cellular system, turning the concept into a working one. They also built radio receivers, transmitters, control systems, and cell-site equipment used in the first trial of the cellular system.

The following year, Bell Labs and Illinois Bell deployed the Advanced Mobile Phone Service system across Chicago, with its switching office located in Oak Park, Ill. The initial test used approximately 100 mobile phones to work through hardware, software, and system-design problems.

A subsequent test in 1979 involved 2,500 mobile users, providing a demonstration of the cellular technology in practice.

The trials in Illinois helped establish the technical foundation for the commercial cellular networks that followed.

Later in his career, Karim worked on the asynchronous transfer mode (ATM) technique, a high-speed networking technology crucial to the transition from traditional telephone networks to broadband and digital ones.

In 2000 he published ATM Networks: Application, Systems, and Design, a textbook that served as a guide for designing and implementing ATM-based services.

Karim received a bachelor’s degree in electrical engineering from the Bangladesh University of Engineering and Technology, in Dhaka. He then earned a master’s degree in EE from the University of Manchester, England, and a Ph.D. in EE from Stevens Institute of Technology, in Hoboken, N.J.

Harry Bostic

Former IEEE Region 4 director

Life senior member, 86; died 18 March

Bostic was an active IEEE volunteer who served as the 1998–1999 director of IEEE Region 4. In 2007 he received a lifetime achievement award from the IEEE Central Indiana Section for “outstanding commitment and dedicated service as regional advisor to the volunteers and members of Region 4 and the Institute.”

He was an engineer for 30 years at U.S. Navy’s avionics facility, a research, development, and manufacturing concern in Indianapolis. He worked on flight control, navigation, and weapons systems there. (The facility closed in 1996.)

Edwin C. Jones Jr.

Professor

Life Fellow, 91; died 10 March

Jones was widely recognized for his contributions to engineering education, curriculum development, and accreditation through decades of service to IEEE, ABET, and the American Society for Engineering Education.

He earned a bachelor’s degree in electrical engineering in 1955 from West Virginia University in Morgantown. The following year he earned a diploma of membership (equivalent to a master’s degree) from Imperial College, London. He went on to serve in the U.S. Army Signal Corps for two years. After his service ended, he studied engineering education at the University of Illinois, Urbana-Champaign, earning a Ph.D. in 1961. Jones then joined the university’s faculty.

The following year, he left Illinois to join Iowa State University, in Ames, as an assistant professor. He was promoted to professor in 1995. Two years later he became associate chair of the electrical and computer engineering department and served in that position until 2001, when he retired and was named professor emeritus. In recognition of his commitment to students, Iowa State established a scholarship in his honor.

In 2006 he accepted a part-time position as an adjunct professor in Minnesota at the University of St. Thomas, in St. Paul. He advised graduate students and helped develop the university’s systems engineering program.

An active IEEE volunteer, he served as 1975–1976 president of the IEEE Education Society. He was a member of the IEEE Educational Activities Board, helping strengthen the relationship among engineering education, professional practice, and accreditation organizations. He received an IEEE Centennial Medal in 1984 and the EAB Meritorious Achievement Award in Accreditation Activities in 1986. The IEEE Education Society later named its Meritorious Service Award in his honor.

Jones was elected a Fellow of ABET in 1986. During his years of service as a program evaluator and leader, he helped advance the quality of engineering education and accreditation programs. ABET recognized him with its Grinter Distinguished Service Award, its highest honor.

Alexander Robert Spitzer

Clinical neurology researcher

Life senior member, 70; died 27 February

Spitzer was a neurologist for 40 years at the Wayne State University School of Medicine, in Detroit, where he also was a director of the electromyography laboratory at Harper University Hospital. The lab studied patients’ brain and spinal cord activity in response to sensory stimuli. The evaluations assessed nerve pathway integrity to help diagnose multiple sclerosis, spinal cord injuries, and other conditions.

After earning his medical degree from the Einstein College of Medicine, in New York City, Spitzer completed a fellowship at the U.S. National Institutes of Health, in Bethesda, Md. He then joined Wayne State as a clinical neurology researcher. His pioneering research in applying neural network analysis to electromyography and clinical neurophysiology resulted in peer-reviewed publications, grants, and several U.S. patents.

He mentored generations of neurologists in electrodiagnostic medicine, a medical specialty that uses nerve-conduction and electromyography tests to evaluate and diagnose muscle and nerve disorders.

In 2020 he founded Mackinac Neurology, a telemedicine-based practice that treated pa­tients virtually during the COVID-19 pandemic.

A longtime IEEE volunteer, he held numerous roles on the IEEE Regional Activities Board, now known as the Member and Geographic Activities Board. He was a member of the IEEE Ethics and Member Conduct and Nominations and Appointments committees, as well as the IEEE Educational Activities and IEEE-USA boards. He served as 1977–1979 director of the IEEE Central Indiana Section.

Donald Leo Dietmeyer

Professor

Life Fellow, 93; died 13 February

Dietmeyer was a professor of electrical and computer engineering for 40 years at the University of Wisconsin-Madison.

He developed a lifelong interest in radio and electronics at high school in Wausau, Wisc., and earned a Ph.D. in electrical engineering in 1959 from the University of Wisconsin. He’d joined the university’s electrical engineering faculty as a professor in 1958 while pursuing his doctorate.

Dietmeyer’s research focused on computer-aided design in the areas of switching theory, hardware description languages, and the decomposition of Boolean functions. His research contributed to the development of automation tools for integrated circuit design.

He worked with Jim Duley, a former student, to pioneer the use of the digital system design language. He wrote the textbook Logic Design of Digital Systems, published in 1978.

In the early 1980s, Dietmeyer worked with researchers to develop ConLan, a language-construction method that combined hardware description languages in one underlying framework.

He served as associate dean of the University of Wisconsin’s electrical and computer engineering department from 1983 to 1995. In 1998 he retired and was named professor emeritus.

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!

I lost a cushy engineering management position at the same time I purchased a business, with a mortgage, three kids, and every other financial obligation of being an adult. It was one of the most stressful stretches of my life. If something similar has happened to you, I feel your pain.

You lose more than the income

For many of us, our work became our identity, so we don’t just think, “I don’t have a job anymore.” We start thinking, “I’m not an engineer anymore.”

An apple tree in winter is still an apple tree. A car parked in a driveway is still a car. You’re still an engineer, the same way you were still one every evening you clocked out, and the same way you’ll be one at the next job. Your skills took years to build and won’t evaporate because a company stopped paying for them. This feeling can be particularly rough if part of your identity was tied to a recognizable employer.

However, this can also be a chance to catch up on what you’ve been putting off: working out, being present with your kids, writing, whatever hobby got shelved for a deadline three years ago.

Once you’ve caught your breath, here are some tips for when the actual work starts:

1. Don’t sprint on day one

I started applying for jobs the next morning after the layoff. It felt productive, but it gave me no time to settle my nervous system or consider what I actually wanted next. I took the first offer that came along, at a company I already knew wasn’t right, and quit after exactly 30 days. Give yourself a few days before deciding anything. Plans built out of desperation rarely work out well.

2. Audit your spending

Go through every subscription and recurring charge and cut what isn’t essential. Every dollar you stop bleeding buys you patience instead of forcing a bad offer out of fear.

3. Build a list wider than LinkedIn

Start with former colleagues and vendors, or businesses with a relationship to your previous employer. They already know you or your company, which gives you the halo effect: Some of the trust from your employer carries over to you automatically.

LinkedIn is table stakes and is the most popular place to find work, but that doesn’t mean your search should stop there. Try Facebook, Instagram, and other social media channels too, where plenty of people who aren’t on LinkedIn might have leads for open roles. I found my first job years ago from a Facebook post, and both roles I landed after being laid off came from startup job boards and recruiters I found entirely outside LinkedIn. Your mileage may vary, but LinkedIn isn’t the only game in town.

Don’t skip your inner circle either: Text your family and friends. Good leads rarely come from someone you know directly, but they do come from someone that person knows. Work this list daily and track who you’ve contacted.

4. Eight hours is a long time

With no work to fill your day, you may default to treating the search like an eight-hour shift. You can’t apply productively for 8 hours straight, and that’s why people burn out. Use a focused morning block for the list in step 3, then spend the rest of your day on activities you’ve been putting off.

5. Catalog your wins before you study interview questions

Cataloging your wins is a higher-leverage move than drilling practice questions this early. Can you explain the most impactful project you worked on in the last year? Probably not.

Most people skip this step, then default to generic answers when a recruiter asks about their last role. Write down specific stories showing leadership, technical ability, and grace under pressure. They’ll come up once you’re in interviews, and cataloging them rebuilds your confidence along the way too. Save the deep prep for once an interview is on the calendar.

Ask yourself daily whether today’s work is generating interest in you, or leading you to someone who might hire you. If not, consider skipping it.

One last thing

A layoff rarely reflects your skills. It’s usually a company protecting revenue—nothing more personal than that. Knowing that doesn’t make it easier, but hopefully this gets you back on your feet faster.

—Brian

IEEE Global Careers Fair: September 23-24

Looking for a job? For the first time, IEEE is taking its Career Fair worldwide. The inaugural IEEE Global Virtual Career Fair runs September 23 (5:00 PM EST) through September 24 (8:00 PM EST), following the sun across every region to connect engineering and technology professionals with employers around the world.

Read more here.

United States Invests in Industry Partnerships for Ph.D. Training

While most engineering Ph.D. grads end up in jobs at commercial companies, academia and industry often operate in their own bubbles. Now, the U.S. National Science Foundation is investing in a program to integrate industry experience into STEM doctoral programs and help bridge the gap. Modeled after similar programs in other countries, students in the I-PhD will spend at least one year working on research at an industry site and receive a combination of funding from the company, NSF, and the university.

Read more here.

AI Efficiency Could Cost Us the Next Generation of Experts

When systems engineer Richard Mitchell designed a digitally-controlled nuclear plant, he made a counterintuitive decision: including manual steps for the human operator that a machine could execute on its own. The strategy was meant to keep the operator sharp, and it’s one that could help address one of the biggest issues facing the workforce today: What happens to human expertise when AI does the work that used to build it?

Read more here.

AI coding tools can now generate thousands of lines of code in minutes, helping companies build features, run tests, and fix issues faster. But the flood of AI-generated code still has to be reviewed. Large language models can produce code that looks clean on the surface but conceals sloppy mistakes such as faulty assumptions, security vulnerabilities, or subtle errors that emerge only after deployment. Fixing those problems could erase the productivity gains AI promises.

Companies are responding to the onslaught of AI code slop by rethinking how they review code. New strategies are emerging. Among other approaches, engineers are scrutinizing plans before AI begins coding, deploying specialized AI agents to catch routine flaws, sending risky changes to human reviewers, or requiring developers to defend the code their agents produce.

The shift comes as the surge in AI-generated code puts new pressure on engineering teams. In a survey of more than 1,100 developers by Sonar, an AI code-verification startup, respondents estimated that AI contributed 42 percent of the code they added to shared codebases. Yet while developers found AI useful for explaining and prototyping code, 96 percent did not fully trust its output to work correctly.

Investors see an opportunity in closing that gap. In August, for example, AI code-review startup CodeRabbit raised US $143 million at a $1.5 billion valuation, while claiming it performs more than 2 million reviews a week for 17,000 customers, including Nvidia, Indeed, and BMW Group.

The new era of code review will determine whether AI can ever provide code that is both faster and more reliable. It also has some software engineers thinking about the future of their profession: If entry-level engineers spend less time writing code themselves, how will they learn to judge it?

AI Code Review Bottlenecks

AI-written code is shifting the bottleneck from generating software to reviewing it. According to the Sonar study, 38 percent of developers said “more effort” is required to review AI-generated code than code written by their colleagues. Sixty-one percent of them said AI often produced code that looked correct but was “unreliable.”

For Synthesia, an AI video-generation platform, code review has become essential to its engineering workflow. In November 2025, Synthesia’s 118 engineers went all-in on AI coding tools like Claude Code. According to Peter Hill, Synthesia’s chief technology officer, the result has been a massive surge in code volume.

“I don’t know if we ever get to the point where you can truly trust the agentic generation of code.” —Peter Hill, Synthesia

That code demands close examination. As of August, the number of pull requests, or proposed changes to a codebase submitted for review, had risen 120 percent year over year, Hill says. Ninety-five percent of those requests contain AI-generated code.

One recurring problem is duplication. Hill says AI tools may not recognize that code for a task already exists, and they’ll write another version because they have limited context. Synthesia has found as many as 10 versions of the same function, leaving engineers to identify and remove redundant functions. Once that’s done, engineers retrain the AI agent so that doesn’t happen again. At the company’s scale, Hill describes getting the AI to produce the intended output an “enormous amount of work.”

AI Agents in Code Review Workflows

Some teams are trying to prevent review problems before AI generates a single line of code.

McLaren Stanley, a senior principal engineer at Amazon Stores, says he is using AI to modernize 17 years of code underlying Amazon’s mobile shopping app. His 70-person team supports more than 1,000 developers by maintaining the architectural backbone they need to build features. With AI writing the code, Stanley said, engineers spend more time deciding what it should do before generation begins.

Much of that work involves writing a “specification,” which is a detailed plan for what the AI agent should build and how. Preventing recurring mistakes before generating code can save engineers time later.

Stanley recalls how a missing instruction once caused an agent to generate 25,000 lines in the wrong version of the programming language Swift. Switching versions produced 600 errors it could not fix at once. Stanley discarded the code, updated the specification, and restarted the agent. Fifteen minutes later, it regenerated the code correctly.

Once the code exists, specialized AI agents can handle the first round of checks before a person steps in.

David Yanacek, a senior principal engineer at Amazon Web Services (AWS), says the company uses agents to test whether code works, check it against the original plan, and look for security flaws before a person reviews it.

That first pass becomes more important as AI-generated code volume increases. At Bonterra, a nonprofit software provider with about 290 engineers, proposed changes tripled within three months of adopting AI, according to Tanuja Korlepra, the CTO. Code entering review rose tenfold and review times tripled, making it impractical for engineers to inspect every line.

“We refuse to let code review become a dumping ground for unchecked model outputs.” —Samar Abbas, Temporal

Bonterra’s agents compare code with the approved design, security rules, coding standards, and accessibility requirements, then report their confidence in the result. A low score or flagged problem sends the change to a person. Code involving payments, personal data, or other sensitive systems always receives human review.

“Agents do the reading and humans do the judging,” Korlepra says.

Synthesia also uses AI agents to decide where human review is necessary. Criteria set by engineers direct more scrutiny toward higher-risk changes. Altering an error message carries less risk than code that handles customer data or core business rules. Even so, fewer than 5 percent of changes bypass human review.

“I don’t know if we ever get to the point where you can truly trust the agentic generation of code,” Hill says.

Automated review does not change who is responsible for the resulting code.

When machines produce more code than engineers can closely read, human approval can become “theater approval,” according to JD Raimondi, chief AI architect at the software consultancy Making Sense. In other words, an engineer might confirm that the feature works, skim the code, and approve it, all without understanding the choices underneath.

Temporal, an open-sourced developer platform, puts the burden back on the person submitting the code. CEO Samar Abbas says code volume and review time have increased with AI. Under its “Send Back” policy, Temporal’s engineers must explain in their own words the agent’s design choices and how the code handles unusual conditions. Otherwise, the reviewer rejects it.

“We refuse to let code review become a dumping ground for unchecked model outputs,” Abbas said.

Training Junior Engineers on AI

As AI shifts engineering work from writing code toward judging it, companies are reconsidering how entry-level engineers gain experience.

Junior engineers at Making Sense have seen some of the largest productivity gains from AI, Raimondi says, raising concerns about what they no longer learn by doing. The consultancy keeps juniors involved in deciding why a customer needs a feature and how it should work, rather than limiting them to checking AI output.

IBM is using AI to give new engineers harder assignments sooner. Neel Sundaresan, IBM’s general manager of automation and AI, says recent graduates now work on product features and projects once reserved for senior level engineers. AI helps implement and test the code, but if it fails, juniors assess what went wrong and fix the issues before the work is passed to senior developers for final approval. Sundaresan estimates that AI can help junior engineers perform 70 to 80 percent of some tasks that once required a senior engineer.

Synthesia primarily hires mid- and senior-level engineers. Its less-experienced employees work with both a senior colleague and an AI agent, taking responsibility for parts of projects while learning to define what successful code should do.

At Bonterra, agents now perform many of the well-defined coding tasks that once trained new engineers. Juniors instead own outcomes alongside experienced colleagues, learning to direct agents, question their output, and remain responsible for the result. She says this approach can help junior engineers build the skills and knowledge needed to advance in their careers.

“If the industry stops hiring juniors, the industry stops producing seniors,” Korlepra said.

Many researchers and students in Kenya, Rwanda, and Uganda struggle to access and publish scientific and technical articles because of financial barriers including publishing fees and subscriptions to research libraries. To help, IEEE has made its Xplore Digital Library more accessible by offering discounts on subscriptions and lowering fees to publish articles. But the number of papers published by technologists in the three nations still lags behind those from other developing countries.

It might be that many researchers haven’t received training in methodology, been instructed on how to write academic articles, or fully understand the process for publishing in scientific journals.

Staff from the IEEE Publication and Information Products group and IEEE volunteers held educational workshops this year in the three countries. The sessions covered the publishing process, IEEE publication outlets, ways to ensure the integrity of research papers, and tips for making better use of IEEE Xplore.

“We want to make sure those in this region are on par with other research communities and ensure they have the support and knowledge they need to make informed publishing decisions,” says Kristopher Zakrzewski, the IEEE area manager for Europe, the Middle East, Africa, and parts of Central Asia. “Our goal is to give them the tools they need to increase visibility and allow them to participate in global conversations in the technology space.”

Workshops on the publishing process

More than 120 participants attended the workshops, which were held in February at the Novotel Nairobi Westlands hotel, the University of Rwanda, and Makerere University, in Kampala, Uganda.

IEEE volunteers who are also authors showed attendees how to prepare, submit, and publish papers. They covered the peer-review process and the benefits of working with IEEE, which publishes about 30 percent of the world’s technical literature on electrical engineering and computer science.

IEEE Senior Member Nelson Ijumba presented at the session in Rwanda. Member Kennedy Ronoh led the Nairobi workshop. Sheila N. Mugala spoke to attendees in Kampala.

“The great thing about these sessions,” Zakrzewski says, “is that each had a local author who presented tips and best practices to ensure that new and returning authors have the information they need to prepare their paper for submission, determine where best to publish their article, and find the right journal or conference that would be the best fit for their research.”

One of the facilitators at the Uganda session was IEEE Senior Member Mayur Kumar Chhipa, head of engineering at the International Business, Science, and Technology University in Kampala and vice chair of the IEEE Uganda Section. The university has about 200 engineering students and about 50 researchers.

More than 100 people attended Chhipa’s session, where he shared practical guidance on conducting literature reviews and identifying high-impact research.

“Researchers in Uganda typically present their paper at an IEEE conference, and that’s it,” he says. “What we’re trying to do is encourage them to take the next step and get their paper published in an IEEE journal.”

He encourages his students to submit a summary of their thesis to an IEEE conference, he says.

“Otherwise,” he says, “their thesis sits in the university’s library or collects dust on a bookshelf.

“When you publish your research, the world knows you are a scholar who has done good work. Having a paper published at a conference or in a journal can help you get into a master’s program globally.”

IEEE Xplore access

Attendees were given an overview of the features of their IEEE Xplore subscription. The digital library contains more than 7 million technical documents from industry-leading journals, conferences, ebooks, and eLearning courses, as well as partner content.

IEEE provides access to the library to more than 50 universities in Kenya through a subscription agreement with the country’s Library and Information Services Consortium, which includes university and public libraries and research institutions. Sixteen universities in Uganda and one institution in Rwanda receive discounted subscriptions.

“It was really important to establish the direct correlation between having access to the technical literature and the publishing output from their university and the region as a whole,” Zakrzewski says.

Many publishing options

The workshops covered publishing options offered by IEEE. That includes both traditional and open access journals, with more than 200 periodicals in total.

There are approximately 180 hybrid journals, which contain a mix of subscription-based and open-access articles, and 30 gold open access journals.

Open access is a publishing model that makes scholarly research and literature freely available online to everyone. Instead of institutions paying for subscriptions, authors or funders typically pay an article processing charge (APC) of between US $2,160 and $2,800 to have their piece published. IEEE offers authors in Kenya a 50 percent discount off the APC rate, and authors from Rwanda and Uganda can publish in IEEE open access journals for free.

The open access program provides authors with greater visibility for their research and enhances discoverability, Zakrzewski says, leading to an increased number of references and citations.

Publishing with IEEE opens additional opportunities including scholarship awards, research assistant job offers, networking opportunities, and speaking engagements.” —IEEE Senior Member Mayur Kumar Chhipa

IEEE Xplore contains more than 200,000 open access articles, he notes. More than 109,000 articles have been published in IEEE Access, a multidisciplinary open access megajournal.

“IEEE supports author choice,” Zakrzewski says. “We really want to make sure that an author has the option to publish the research that will meet any consortium, funder, university, or coauthor requirements—which is why we’re focusing on growing our open access program to complement our traditional publishing program and offer more options to authors.”

The sessions are having an impact

Participants at the Uganda session told Chhipa that they appreciated the IEEE Xplore Digital Library demonstrations and found the guidance on academic publishing valuable.

“Many attendees mentioned that the session helped them better understand how to search for relevant literature, evaluate the quality of research papers, and write stronger manuscripts for publication,” he says.

“I have observed increased interest among students and faculty in using IEEE Xplore as their primary research resource,” he adds. “Researchers are also more aware of ethical publishing practices and are developing stronger research proposals and manuscripts.

“The program contributes to building a stronger research culture by encouraging evidence-based research, international collaboration, and higher-quality publications, which will ultimately enhance the global visibility of research from Uganda and Africa.”

Publishing has its privileges

Chhipa says getting your research published has many benefits, and IEEE staff and members agree.

IEEE and several of its societies offer student grants to help cover the expense of traveling to conferences and presenting papers. The money typically covers airfare and a hotel room. Some grants also pay for conference registration fees, Chhipa says.

Chhipa assists students at his university with writing and submitting research papers to IEEE journals and conferences. Students gain confidence when their paper gets accepted, he says. One who attended the recent IEEE session was informed that his paper was accepted by an IEEE conference—which Chhipa says he was excited about.

He encouraged that student to apply for a travel grant.

“Maybe he’ll get it. Maybe he won’t. But at least he learned how to write a paper, apply for a visa to attend the conference, and book an airplane ticket,” Chhipa says. “It will help him grow personally and professionally.”

Presenting a paper at an IEEE conference can be life-changing, he says.

“It opens additional opportunities,” he says, “including scholarship awards, research assistant job offers, networking opportunities, and speaking engagements. This is how publishing a research paper in the IEEE Xplore Digital Library can directly, positively impact the life of students and scholars from Africa, especially Uganda, Rwanda, and Kenya.”

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Asking someone to be your mentor is weird.

Walking up to someone and asking, “Will you be my mentor?” has always seemed to me like the adult version of a kid walking up to another kid at a party and asking, “Will you be my friend?”

What you’re really asking is: “Will you commit some amount of unpaid time to guiding my career for an indefinite period?”

Framed that way, of course some people hesitate to say yes.

But formal mentorship isn’t the only way to benefit from the wisdom of those who came before. I’ve never formally asked anyone to mentor me. And yet I’ve had dozens of unofficial mentors.

The Copy-Paste Method

One way to learn from others is by copying what you observe.

Sometimes this means reading books or blogs from engineers you respect and directly applying their ideas to your work.

I’ve also been fortunate to work alongside some extremely talented engineers, and I shamelessly copied the things they did well.

When I meet one of these engineers, I try to figure out what they’re doing differently: How do they approach a problem? What do they read? How do they communicate in meetings? What do they know that I don’t?

Then I steal whatever seems useful and apply it to my own career.

Great artists steal. Engineers should too.

Curiosity Compounds

Still, just observing has its limits. Asking questions can get you even farther.

I’ve asked managers how they approached difficult conversations, and I’ve asked engineers what their process was for solving problems I thought were impossible.

If someone seems unusually knowledgeable: “What are you reading right now?” If I respect someone’s work: “What’s something you think I could do better?”

These aren’t profound questions. They don’t need to be. You get one useful piece of information, apply it, and move on.

And if you don’t work around exceptional engineers, you can still do this. The only real requirement is curiosity. When you encounter something you don’t understand, make it a rule to investigate instead of moving past it.

You don’t need one person willing to guide your career. You need a collection of people who know things you don’t.

Pay attention to them. Ask questions. And shamelessly copy the good parts.

Ask me!

If you have a career question you’re struggling with, like an upcoming decision, a problem at work, an interview, whatever—submit it here: https://docs.google.com/forms/d/e/1FAIpQLSdj_2BZIhrGF__7BCLH33zJ9NMv8C7Vsg9NNusASrYj7-9Idw/viewform. You can include your name or remain anonymous.

I’ll be reading through them and answering some in future articles. Consider it mentorship without the awkward “will you be my mentor?” conversation.

—Brian

ICYMI: The Institute June 2026 issue

IEEE members have a wealth of experience and knowledge to draw from. In the most recent issue of The Institute, several members share their career advice for engineers, from engineers. You can also learn about other IEEE programs and courses.

Read more here.

A little over a decade ago, I led the controls design for a first-of-its-kind full digital-control system for a U.S. nuclear plant. It was, on paper, a beautiful machine—engineered to run itself the way a modern airliner does, with operators watching over a system that rarely needed them. And we made a decision that, to an efficiency-minded observer, looked backward: We deliberately left manual steps inside sequences the system could execute on its own.

We were solving a specific problem. An operator who only ever supervises automation slowly stops being an operator. The hands go cold. The mental model of what the plant is actually doing gets fuzzy. Then comes the day the automation hands control back. It’s always the worst day, because automation only quits when it’s confused or in trouble. But by then, you have a person in the chair who hasn’t truly operated the thing in years. The manual steps were there to keep the human current. It was inefficient by design, on purpose.

That plant, as it happened, was never built. It was shelved amid the politics and economics that surround nuclear power in this country, for reasons that had nothing to do with the engineering. But the design instinct outlived the project, and I’ve come to believe it’s the most useful idea I can offer to the argument now consuming every boardroom: What happens to human expertise when AI does the work that used to build it?

AI Is Disrupting the Engineering Career Ladder

The data has gotten hard to wave away. A Harvard University working paper covering some 65 million workers at more than 280,000 U.S. firms found that after companies adopted generative AI, junior employment fell roughly 9 percent within six quarters relative to nonadopters, while senior employment kept right on growing. A Stanford analysis of ADP payroll records points the same way: The youngest workers in the most AI-exposed occupations lost ground after late 2022 while their more-experienced colleagues held theirs. The Stanford researchers found that the losses concentrate where AI automates the work; where it merely augments, junior employment holds steady or rises.

The causal story is still contested, and honesty requires saying so. Researchers at the New York Fed attribute much of the rise in young-graduate unemployment not to AI but to remote work, arguing that firms are reluctant to hire inexperienced people whom they cannot train and mentor at a distance. But notice what the explanations share. Whether a model is absorbing the formative work or distance is severing the mentorship around it, both describe the same broken mechanism: the apprenticeship channel through which expertise passes from senior to junior. Either way, “entry-level” has quietly come to mean “three years of experience required.”

Strip away the noise and you’re left with one deceptively simple problem: You cannot become a senior engineer without first being a junior one. Expertise is not downloaded. It is earned through failed builds, dead-end debugging sessions, and the “why on earth did that work” moments that a capable AI will now happily spare the newcomer. Spare them enough of those and you produce a cohort that can supervise a model on paper but never developed the gut sense to know when the model is confidently, catastrophically wrong.

Most of the commentary stops at the diagnosis, or reaches for policy solutions that treat the loss of junior jobs as an economic problem. Yet it’s also an engineering problem, and safety-critical fields have already spent decades learning how to solve it.

Aviation’s Lessons About the Automation Paradox

My own career started at the sharp end of automation. My first job out of school was verifying and validating the software in the digital jet-engine controller that decides, faster than any pilot could, how a fighter plane’s engine responds. Even then, in the late 1980s, the central tension was visible: The machine outperforms the human in routine cases, but the human is all that stands between the aircraft and disaster in the cases the machine didn’t anticipate. This tension is known as the automation paradox, in which increasingly capable automation gives human operators less practice, while leaving them only the most difficult situations.

Aviation learned, repeatedly and expensively, what happens when human skills atrophy inside that gap. The canonical example is Air France flight 447, which fell into the Atlantic in 2009. The proximate cause was mundane. Iced-over airspeed sensors fed the autopilot bad data, and it did what it is designed to do: It disconnected and handed control of the airplane back to the crew. What followed was not a hardware failure. It was a competence failure. A recoverable situation became an unrecoverable one because the pilots, conditioned by thousands of hours of watching the automation fly, could not read a high-altitude aerodynamic stall and hand-fly their way out of it. The airplane was working. The training the automation had quietly eroded was not.

The industry’s response is instructive, and it’s the same move we made in that nuclear control room. It did not rip out the autopilot. It built deliberate manual practice back in. In 2017 the FAA issued Safety Alert for Operators 17007, “Manual Flight Operations Proficiency,” declaring that “manual flight is the foundation upon which other technical flying skills are built.” The alert formally recognized skill decay as a hazard in its own right. Some airlines amended their procedures to encourage hand-flying both the initial climb and initial descent in benign conditions, knowingly trading a sliver of fuel efficiency to keep the crew’s raw flying skills alive. That trade is the whole point. A perfectly optimized system that produces incompetent operators is not optimized at all. It has simply moved its failure mode somewhere the spreadsheet can’t see it.

Manual Gates Could Preserve Engineering Skills

Put the aviation lesson and the nuclear instinct side by side and they point to one design pattern we now need in AI-augmented work: the deliberate “manual gate.”

A manual gate is a point in a workflow where a human takes the controls, not because it is the fastest way to get the task done, and not only as a safety interlock, but specifically to exercise and preserve a skill that would otherwise decay. The distinguishing feature is that it is chosen. You decide, as a matter of design, which competencies your organization must keep alive in human beings because those are the ones you will need on the bad day. Then you engineer the friction required to keep them warm.

Picture how this might work on a software team that leans on AI for most of its code. The team places a manual gate around the skill it can least afford to lose: debugging. When a defect surfaces in a critical module, the assigned engineer—deliberately, often a junior one—must first reproduce the failure, trace it to root cause, and write an automated test that captures the bug, all with the AI assistant switched off. Only after the engineer commits to a diagnosis does the model come back on, to propose the fix, generate alternatives, and sweep the code base for similar bugs. The engineer then compares their diagnosis against the model’s. When the two disagree, that’s the design working, surfacing the disagreement before the bad day instead of during it.

This approach reframes the junior engineer entirely. The instinct today is to let AI do the entry-level work because it is faster and cheaper. But some of that work is not overhead to be eliminated. It is the training apparatus of your future senior staff, and you should protect it the way you’d protect any other piece of critical infrastructure. It may not be efficient this quarter, but dismantling it quietly mortgages your capability a decade out.

Why Companies Must Keep Training Junior Engineers

None of this is free, and pretending otherwise would insult the people who have to sign the budgets. A deliberate manual gate is, by construction, less efficient in the near term than full automation. Keeping juniors doing formative work and running the manual sequences costs something now to protect something later.

That’s a hard sell in a market that judges most leaders on quarterly results. A hired executive who carries “unnecessary” humans that AI could replace will hear about it from the board long before the payoff arrives. The math only works for someone insulated from that pressure: a founder with control, a private company, an institution with a genuinely long horizon, or a regulator willing to require workers to demonstrate their skills regularly, as pilots must. Which means the organizations most likely to preserve their own expertise are the ones structurally able to spend short-term margin on long-term capability; everyone else will need that outside push.

So here is the argument, in one line: Deliberate inefficiency is not waste. In safety-critical engineering we have always known it as insurance, and we buy it on purpose. As AI takes over the work where expertise is forged, the smart move is not to resist the automation. It is to keep our hands on the controls by design—so that when the automation fails, as it always eventually does, there is still someone in the chair who knows how to fly.

For many high school students, summer vacation is a time to unplug. For Ruchira Shree, a rising sophomore at West Windsor–Plainsboro High School South, in New Jersey, the break allows her to ramp up her extracurricular pursuits.

Much of her time is spent assisting with IEEE Princeton Central Jersey Section activities. She got involved with the PCJS because of her mother, IEEE Senior Member Shubha Bommalingaiahnapallya, a principal engineer at Intel.

“I started going to the IEEE meetings when I was little,” Shree says. “I used to go with my mom and just sit in the back of the room.”

This summer she says she’s focusing on improving her mathematics skills by attending the Program in Algorithmic and Combinatorial Thinking summer course on math and computer science. She wants to qualify for the American Invitational Mathematics Examination, an event for the top American Mathematics Competitions scorers. She earned a place on the AMC 8 honor roll—a recognition awarded to the top 5 percent of participants in the national competition—when she was in seventh grade.

Shree’s IEEE involvement and her advanced math skills caught the attention of an internship recruiter for the Alliance for Indigenous Math Circles, a group dedicated to expanding STEM opportunities for Native American students. The AIMC organizes and sponsors weeklong overnight camps. Interns assist with activities and teach some of the sessions. Shree met a recruiter at one of the section’s events, and she interned at one of the camps last year.

The IEEE-math camp connection

Shree’s involvement with the PCJS evolved naturally as she got older, she says, along the way preparing name badges and tackling similar assignments. She met Francis O’Connell, an IEEE life senior member and founder of FXO, in Plainsboro, N.J. O’Connell is the treasurer of the IEEE Integrated STEM in Education Conference (ISEC).

He has been a mentor to Shree for the past two years, he says.

At last year’s ISEC, she assisted at the registration desk and met Harini Frederickson, an AIMC intern recruiter for New Jersey.

Frederickson invited Shree, along with nine other students, to volunteer at an upcoming camp being held in Santa Fe, N.M.

“Ruchira is a real go-getter,” Frederickson says. “When she has an idea, she follows through and doesn’t get easily discouraged.”

The AIMC was created to address an important need, says math teacher Donna Fernandez, codirector of the organization. U.S. Indigenous students have the lowest rate of pursuing STEM studies across all demographics, according to the U.S. National Science Foundation. Systemic barriers such as a lack of role models in STEM fields, socioeconomic inequities, and Eurocentric teaching frameworks are some of the reasons, Rechel Shrisunder and Dwight Figueiredo wrote in a chapter of Minorities: New Challenges and Horizons, a book edited by John R. Hermann.

Indigenous people have a long tradition of mathematics, Fernandez says. She cites the Navajo code talkers from World War II as examples. The Navajo, along with 14 other Indigenous tribes, used their native languages to code and transmit critical messages for the U.S. military during the war.

There was a student at camp whose grandfather was a code talker, Shree says.

Navajo people also use math to build hogans: conical dwellings that require precise calculations to construct. Native communities have used math when building the structures for centuries, Fernandez says.

Fernandez believes typical classroom math curricula overlook the importance of mathematics in Indigenous cultures. Combining STEM activities with cultural elements helps Indigenous students better understand their ancestors’ role as mathematicians, she says.

That, in turn, helps the students see themselves in those careers, she adds.

The AIMC was built upon a program already in place: the Navajo Nation Math Circles, founded in 2012 by three university professors. Their goal was to provide the Navajo Nation’s students with tools to overcome barriers to STEM education.

To expand the math circle program, the AIMC was added to reach Indigenous students in the Four Corners area of Arizona, Colorado, New Mexico, and Utah.

Since 2017, the organization has run two camps every year at the Navajo Preparatory School in Farmington, N.M. In 2025 one camp was moved to the Santa Fe Indian School.

During each weeklong event, students and interns work in math circles. It’s a cooperative way to solve problems creatively, organizers say. Students collaborate on STEM-focused projects and learn from Indigenous STEM professionals. Interns also get the opportunity to experience an off-site cultural event.

The camps are free for students, thanks to sponsorships and donations. Teachers and interns cover their own travel expenses. Shree secured a US $1,500 sponsorship grant through the PCJS.

Building relationships through STEM activities

Relationships are an influential part of the week, Fernandez says: “One of the best things we see at the camp is that students return the following year and ask, ‘Is so-and-so intern coming back this year?’ They remember the relationships they developed, especially the cultural exchanges they had.

“Those exchanges go both ways, benefiting the interns too.”

Students spend mornings at camp working in math circles, then gather for a wrangle, during which each team defends its math circle answer and challenges other teams’ solutions. Shree and the other interns are on hand to answer questions and observe the teams as they work through the math circle problems.

“Math problems typically have very binary answers,” she says. “But in math circles, you focus more on talking through your answers to open-ended questions and learning from each other.”

Students spend afternoons working on projects. In one, the students used household items to create a replica of the Batmobile, Shree says. The car was required to be self-propelled without an engine. Balloons were a popular alternative.

Another activity focused on the Indigenous tradition of basket weaving. Students learned the cultural meaning behind traditional designs while understanding how geometry concepts influenced the finished product.

These Native American middle school students work on solving a mathematical pattern-matching game, one of the activities held at the summer camp.Ruchira Shree

Role models inspire students

“Because there’s a lack of Indigenous STEM role models, many Native American students don’t see themselves in mathematics or science,” Shree says.

To bridge that gap, Fernandez ensures Indigenous role models are part of the camp. Some of the people who spoke with students during Shree’s internship were Jessica Benally, a Ph.D. student in the learning sciences and human development program at the University of California, Berkeley, and engineers from the New Mexico Mathematics, Engineering, and Science Achievement program, which supports underrepresented preuniversity students.

“I believe the students were very inspired,” Shree says, “because they could see how they themselves could pursue STEM careers. They had people to look up to in the field who had come from backgrounds just like theirs.”

Interns in action

The interns’ primary responsibility was leading a two-hour, after-dinner Radio Weaves session. They taught students about a popular communication technology that doesn’t require the Internet or cell towers.

Ham radio, also known as amateur radio, is a communication method that uses designated frequencies. In the United States, anyone can listen to amateur radio transmissions; to legally transmit on the frequencies, though, a user needs a Federal Communications Commission license. The Radio Weaves project is designed to prepare students to pass the FCC technician license exam.

To make that happen, the interns customized Gimkit, a learning game, loading it with radio-specific content that mirrored topics that could appear on the test.

Each intern worked with two or three students to complete the Gimkit materials.

Frederickson, who was on hand for the camp, says the aim was to send students home with something tangible that demonstrated their STEM accomplishments.

Nearly all the students passed the exam on the first try, she says, and she worked with those who didn’t to retake the test.

All the students ultimately received their license, she says.

Inspiration comes in several forms

The interns took an afternoon off to attend a Pueblo Feast Day, a celebration filled with music and dance that culminated in visits with nearby families, with whom they shared dinner.

“The tradition is very generous and community-based,” Shree says. “It represents that every home in the village will welcome any guest to have a meal.”

The feast was the highlight of Shree’s week, she says: “I got to really experience Native American culture firsthand.”

The students inspired her, she says.

“Seeing the joy on their faces when they passed the technician exam or when they got a math problem correct showed me how much joy they find in learning,” she says. “It made me realize that I want to help provide more opportunities for them to learn and challenge themselves.”

“Because there’s a lack of Indigenous STEM role models, many Native American students don’t see themselves in mathematics or science.” —Ruchira Shree

That realization spurred her idea for a new initiative. After she returned home, she founded Rukie Cookie to create “safe, inclusive, and inspiring spaces where youths explore STEAM [and] build curiosity, strategic thinking, and innovation—empowering them to become confident leaders and active contributors to a more just and equitable society,” according to the project’s website.

Baking is one of Shree’s hobbies, and she sees it as a way to fulfill a financial need she observed at camp.

“I noticed that at lunch breaks, they [camp students] used to play chess on the side, but they couldn’t actually participate in tournaments because that requires a U.S. Chess Federation (USCF) membership fee, which they couldn’t afford,” she says. Shree bakes cookies and sells them at PCJS events. Proceeds go toward youth chess classes and USCF memberships for children in underrepresented communities.

She has raised enough money to sponsor six USCF memberships, five of whom are camp attendees, she says.

“I hope that the students I have gotten a membership for will continue growing their passion for chess,” she says, “but also that it will encourage them to challenge themselves with difficult problems.”

What’s next?

Shree planned to attend an AIMC camp this year, she says, but it was canceled due to resourcing issues. She says she intends to return next year with goals of adding a formal chess component to the schedule and continuing to help more students overcome financial hurdles to join the USCF.

She’s also writing a novel about Alzheimer’s disease and identity loss, and she’s conducting independent research on cognitive decline at the New Jersey Institute of Technology. Watching her great-grandmother struggle with the condition sparked her interest in the subject, she says.

She is confident STEM will be part of her future, she says. Math and cognitive science are areas of interest she plans to study, but she’s still undecided about a major. Her interest in Alzheimer’s research and a desire to apply AI to health care will influence her decision, she says.

She adds that she plans to join IEEE once she’s eligible.

This article was updated on 8 September 2026.

While walking to school as a child, Jernej Barbič would marvel at how beautiful his home was. He was born and raised in a picturesque village in northwestern Slovenia (formerly Yugoslavia), located in the European Alps. Surrounded by alpine and beech trees, Barbic dreamed of replicating their swaying in the wind for others to enjoy.

At the time, he didn’t have the tools or the knowledge to create a system that could do that, but it sparked his interest in computer graphics, he says.

Jernej Barbič

Employer

University of Southern California, in Los Angeles

Title

Professor of computer science

Member grade

Senior member

Alma maters

University of Ljubljana, in Slovenia; Carnegie Mellon

Twenty years later, in 2016, Barbič, a professor of computer science at the University of Southern California, in Los Angeles, made his mark. His Ziva VFX software system allows for the creation of realistic muscle, fat, and skin simulations for 3D digital humans and creatures.

The technology was launched in 2016 by a startup he helped found, Ziva Dynamics, headquartered in Vancouver. It was acquired in 2021 by Unity Technologies of San Francisco.

Ziva VFX has been used in more than 60 movies including Aquaman and the Lost Kingdom; Godzilla x Kong: The New Empire; and Venom: Let There Be Carnage.

For the design and development of Ziva VFX, Barbič, an IEEE senior member, received a 2025 technical achievement Academy Award. It was a “tremendous honor,” he says, as the award recognizes technologies that have had a significant impact on motion picture production.

“Computer graphics and simulation can sometimes feel like a specialized technical field,” he says, “but the award showed that these ideas affect not just science but also art and how stories are told on screen.

“The digital characters enabled by mathematics become important parts of people’s lives.”

Sparking an interest in computer graphics

Barbič says he was inspired to pursue engineering by his father, an engineer who headed a cement factory’s research department and invented a technology that uses magnetic resonance imaging to test the integrity of cement. His father’s work showed him that “mathematics and physics are beautiful on their own, but engineering lets you build something that other people can use,” he says.

It was Barbič’s mother, an elementary school teacher, who introduced him to computer science. When he was 8 years old, the school his mother taught at bought a ZX Spectrum computer. With permission from the principal, she brought it home for her son to play on for two weeks. But he didn’t just play games; he created his own game using the BASIC programming language.

The machine came with a booklet that contained instructions on how to write a computer program, he says.

“At first,” he says, “I copied them verbatim without understanding what they did. But then I started realizing there is structure, and I modified the instructions.”

Of all the creatures brought to life using his technology, Barbič is particularly enamored with King Kong from 2024’s Godzilla vs. Kong.DNEG/Warner Bros. Entertainment Inc./Legendary

By the end of the two weeks, he’d developed a computer game where players guided a snowman along a winding road. It shifted unpredictably to the left or right, and players accumulated points by remaining on the road for as long as possible.

Barbič went on to earn a bachelor’s degree in mathematics in 2000 from the University of Ljubljana, in Slovenia. The following year, he moved to the United States to begin a doctoral program in computer science at Carnegie Mellon. It was a major turning point in his life, he says.

His doctoral research focused on developing simulation methods for objects that can change their shape when an outside force is applied to them, known as “deformable objects.”

That project shaped much of his later research, he says: “I became interested not only in making simulations accurate but also in making them practical: fast enough, robust enough, and controllable enough to be used in real applications.”

After earning his Ph.D. in computer science in 2007, he worked as a postdoctoral researcher at MIT. Two years later, he joined USC as an assistant professor.

Making movie magic possible

It was at USC that Barbič merged his passion for computer science with film. He developed Vega FEM, an open-source software program that allowed people to animate realistic 3D deformable objects. But Vega FEM was narrow in scope and not exactly what filmmakers needed, he says, so he started exploring how to create a version suitable for the movie industry.

“A major theme of my career has been the translation of research ideas into practical tools,” he says. “Academic research often produces beautiful algorithms, but it can be difficult to make those algorithms usable by artists, engineers, or production teams. I have always been interested in that bridge: taking rigorous computational methods and turning them into systems that people can actually use.”

In 2011 he attended an Association for Computing Machinery conference presented by its Special Interest Group on Computer Graphics and Interactive Techniques (SIGGraph). There he met James Jacobs, the creature supervisor at visual effects company Weta FX of Wellington, New Zealand. The company is behind the effects in the Lord of the Rings and Hobbit trilogies and other movies. Jacobs used Barbič’s software to create animals and fantastical creatures.

Two years later, Weta FX offered Barbič a summerlong research position in New Zealand. He accepted and spent the time studying the process of creating visual effects and learning what roadblocks existed in the film industry, he says.

At the time, the technology to create realistic soft-tissue and anatomical simulation for digital characters didn’t exist.

“The visual effects industry had reached a point where surface-level realism was not enough,” Barbič says. “A creature could have beautiful skin textures and detailed geometry, but if the bones, muscles, and fat underneath did not move correctly, the illusion would break.

“The problem was especially difficult for creatures and characters that need to feel alive: animals, monsters, fantasy creatures, or digital doubles. Their bodies may have unfamiliar anatomy, but the audience still anticipates them to move in a way that matches real-world expectations. Muscles should bulge and contract, skin should stretch and slide, fat should have inertia, and tissue should respond to motion and impact.”

In an effort to solve the problem Jacobs in 2014 approached Barbič about founding a startup. In 2015 they launched Ziva Dynamics, where they began what is now Ziva VFX.

The software uses physics-based simulations to model the internal anatomy of a character. It numerically solves the partial differential equations of nonlinear elasticity for musculoskeletal human and creature tissues, Barbič says. The equations describe how muscles, fat, skin, and connective tissue deform, interact with bones, and connect, and how muscles activate. Instead of animating only the outside surface, artists can create a model with underlying muscles, bones, soft tissue, and fat. Each component is assigned material properties, constraints, attachments, and activations. The simulator then computes how they deform and interact over time.

The technology uses ideas from computational mechanics, finite element methods, numerical optimization, contact handling, and computer graphics, Barbič says. Finite element simulation, a method used to predict how a product or structure reacts to heat and other real-world forces, provides a way to model deformable materials volumetrically, not just as surfaces, he says. The tool computes internal elastic forces and solves the equations of motion so the character’s tissues respond plausibly to animation, pose changes, muscle activation, and dynamic motion.

But the system had to be designed for artists, Barbič says. In production, he says, the goal is not only physical realism but also controllable realism.

“Artists need to direct the result, iterate, and fit the simulation into a larger animation pipeline,” he says. “So the technology had to combine scientific simulation with practical controls, robustness, and integration with visual effects workflows.”

Barbič says Ziva VFX has been used in more than 60 movies. Of all the creatures brought to life using his technology, he is particularly enamored with King Kong from 2024’s Godzilla vs. Kong.

“When King Kong is walking, you can see the muscles, how they’re very pronounced, and how they influence the shape of the skin. You can really feel the strength of King Kong,” he says. “And this was made through my software, so I think it’s amazing.”

After Ziva Dynamics was acquired by Unity, Barbič consulted for the company for almost two years.

In 2024 DNEG, a London-based visual effects and computer animation company, acquired the exclusive license to Ziva VFX.

Animating the human hand

Barbič strives to improve visual effects as an entrepreneur and an academic. His most recent research, funded by the U.S. National Science Foundation, focused on the modeling, simulation, and animation of human hands. The goal is to create computer models of hands that can be used to design tools, medical prosthetics, and robotic hands.

“The hand is a fascinating and difficult system,” Barbič says. “It contains many small bones, muscles, tendons, ligaments, skin, fat, and other soft tissues, all packed into a compact structure and interacting mechanically in complex ways.”

He and his team built a digital twin of the human hand.

He aimed to move toward “anatomically meaningful simulation,” he says. He used medical imaging, geometric modeling, finite element methods, and multibody simulation to represent the internal structures of the hand and its motions.

“IEEE lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.”

He worked with Bohan Wang, who at the time was a USC doctoral candidate, and George Matcuk, an associate professor of radiology. Wang is now an assistant professor of computer science at the National University of Singapore.

Barbič, Wang, and Matcuk scanned four people’s hands with an MRI machine. The two men and two women would position their hands in 12 poses, which allowed the team to gather data about how the bones, muscles, and fat move with each pose.

The data sets are available for anyone to use in their own studies.

“This project can help medical doctors learn more about how the hand is moving,” Barbič says. “It’s also great for roboticists to better understand how the human hand actually works, so [the movements] can be replicated.”

IEEE: Integral in interdisciplinary research

Barbič joined IEEE in 2008, when he published his research paper on simulation methods for deformable objects in the inaugural issue of the IEEE Transactions on Haptics. He has since published several papers in the IEEE Transactions on Visualization and Computer Graphics, which he says connected his work to a wider community interested in visual computing and computational methods. You can find his research in the IEEE Xplore Digital Library.

“IEEE recognizes the engineering side of computer science,” he says. “My work is often presented as computer graphics, but at its core, it is also simulation, mechanics, numerical methods, haptics, visualization, and software systems.

“IEEE is a community where that broader identity makes sense. It lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.”

He believes the organization is key in supporting a healthy interdisciplinary research ecosystem at a global scale—which, he says, is why he has served as an associate editor for Transactions on Visualization and Computer Graphics and Transactions on Haptics.

Being a member has made it easier for Barbič to connect with engineers in different fields, he says.

“My research often lives between categories: It is mathematical but also practical; visual but also mechanical; artistic but also engineering-driven,” he says. “IEEE is one of the professional communities where that mixture is understood.”

The IEEE–Eta Kappa Nu (IEEE-HKN) honor society is preparing to host the Innovating the Future event on 6 November.

The inaugural one-day, in-person event is designed to provide a forum for IEEE and IEEE-HKN undergraduate and graduate student authors to present their original research papers. A keynote address and thematic presentation sessions are planned as well. Student attendees can network with their peers and gain firsthand experience with the academic publishing process.

To present at the conference, students had to submit an abstract of their research before 1 May. Students whose work was accepted were assigned a volunteer IEEE member to mentor them and guide them through the research writing process, including presenting and publishing their original work.

Those whose paper was accepted by 1 August were invited to present at the conference. The conference proceedings will be submitted for publication in the IEEE Xplore Digital Library.

Upholding research integrity in a changing landscape

IEEE Life Fellow Manuel Castro, the conference’s technical program chair, oversees IEEE-HKN’s Innovating the Future program committee. It manages the review process, organizes logistics, and handles the mentoring component.

“This new conference is important to IEEE, as well as to IEEE-HKN,” Castro says, “because it allows student authors to grow in their skills and competencies, and be supported while turning their technical activities into publications.”

“The conference offers me a chance to learn how to communicate my research to a broader audience, gain feedback from other student researchers beyond my institution, and see how my work can be made more accessible.” —David Kwabi-Addo

IEEE Life Fellow Sorel Reisman, a California State University professor emeritus and an IEEE-HKN governor-at-large, says that because the academic research landscape is rapidly shifting, the conference is timely.

“As AI increasingly threatens the integrity of research papers being published in leading journals and conference proceedings, it is essential that future scholars—many of them current IEEE-HKN students—grasp the established standards of legitimate, peer-reviewed research publishing,” Reisman says.

Perspectives from mentors and students

A cornerstone of the conference is its rigorous mentorship initiative, which pairs each author of an accepted abstract with an experienced IEEE volunteer. The mentors provide personalized guidance on organizing the students’ technical content into the correct format for publishing. They also discuss navigating the peer review process, structuring presentations, and preparing the final manuscript for publication.

The impact of the guided process can be valuable for both the mentors and their mentees. IEEE Member Wafa Elmannai, associate professor and chair of the electrical and computer engineering department at Manhattan University, in Riverdale, N.Y., and faculty advisor to the IEEE-HKN Gamma Alpha chapter, serves as a mentor.

“Research is essential to advancing technology and driving innovation,” Elmannai says.

She volunteered to be a mentor, she says, because she has seen how conducting research can transform a student’s future by building their confidence, curiosity, and critical thinking skills.

“Mentoring encourages students to step outside their comfort zones and develop innovative solutions that contribute to society,” she says.

For the students, the conference can be a critical stepping stone. David Kwabi-Addo, an IEEE graduate student member who is researching computational biology at MIT, is president of the IEEE-HKN Beta Theta chapter. He says he views the program as an opportunity to gain experience in producing academic scholarship.

“I submitted an abstract of my research paper because I see the conference as a chance to produce what could become my first conference publication,” Kwabi-Addo says. “The conference offers me a chance to learn how to communicate my research to a broader audience, gain feedback from other student researchers beyond my institution, and see how my work can be made more accessible.”

He says he hopes his participation will highlight the diverse breadth of research that future conferences can showcase.

Workshops on the publishing process

Conference organizers are holding a series of workshops to guide students through every step of the academic publishing process. The workshops are open to anyone and available on the IEEE-HKN YouTube channel.

Topics previously covered are:

• The Art of Crafting a Compelling Abstract (13 March).
• Identifying When a Project Is Mature Enough for Publication (15 May).
• The Mechanics of Writing a Technical Paper (19 June).
• Surviving the Review Cycle and Dealing With Criticism (14 August).

Registration is open to all for this upcoming workshop:

• From Pen to Voice: Adapting a Paper Into a Compelling Conference Talk (2 October).

A launchpad for the next generation

The Innovating the Future program is designed not only to improve the quality of submissions but also to foster long-term professional development and research communication skills to develop the next generation of IEEE authors. The conference is more than a venue for presenting research; it is a launchpad for innovators committed to advancing technology for humanity.

More than 74 percent of engineering doctoral graduates in the United States in 2024 went into industry, according to the most recent data from the U.S. National Science Foundation’s Survey of Earned Doctorates. The percentage is smaller across all science and engineering fields, but more than half of these graduates have pursued industry positions since 2019. Despite this reality, academia and industry often operate in their own bubbles, and even highly skilled doctoral students may not be immediately prepared to work at a company.

A new NSF-funded pilot program aims to help bridge the gap. Announced in late July, the Industry-Integrated Ph.D. Scholar Program, or I-PhD, will integrate industry experience into STEM doctoral training with partnerships between individual companies and universities. The pilot includes four years of funding for 250 students, beginning in the 2026–27 academic year, and will be led by the University-Industry Demonstration Partnership (UIDP), a nonprofit that identifies issues affecting relationships between academia and industry in order to improve collaboration.

A New Ph.D. Funding Structure

The idea for the program started around January 2021, says Anthony Boccanfuso, president and CEO of UIDP. During a town hall, industry members told the organization that supporting Ph.D. students in the United States was not as appealing as it was in other countries, where industry Ph.D.s are already an option. The program draws on those similar models in Canada, the European Union, the United Kingdom, and elsewhere. It’s not intended as a replacement for existing Ph.D. pathways but as an additional option, Boccanfuso says.

“We created a program that increases the [return on investment] for companies by reducing the investment that they have to make financially, but increasing their investment in nonfinancial ways, by having them mentor a student who spends a year at their site,” Boccanfuso says. At launch, 30 university-industry partnerships had submitted letters of interest—the first step toward a formal application—and he says many more have since expressed interest in joining.

In this new model, the first year of a graduate student’s study is funded by the university. Then, for years two through four, funding comes from both the industry partner and the NSF. The federal agency will invest a total of US $47 million over the five-year pilot.

The announcement comes amidst a slew of recent funding changes at the NSF: The agency has clawed back around $500 million of already distributed funding for some research, Nature reported in July, while it invests heavily in other initiatives such as independent research organizations it calls “X-Labs.” According to a 17 August press release, the NSF announced another $1.5 billion in funding opportunities for foundational research across science and engineering, while it is reportedly set to issue the lowest number of new grants in four decades.

NSF Program Requirements and Flexibility

To participate, students must complete at least one year (in aggregate) of their dissertation research at an industry site; universities must fund the first year of study; and industry partners must invest $100,000 per student. The program also includes structured professional development, and UIDP is creating a standardized certificate for industry preparedness.

But beyond these key requirements, UIDP won’t determine many details. For instance, universities and companies are responsible for forming partnerships, selecting students, and determining projects. Intellectual property rights and publication policies are also handled case by case by each partnership.

“Computing engineering thinks differently about intellectual property than biomedical engineering,” Boccanfuso says, noting that many companies already have research agreements that outline how to handle IP. “The [research] community is really smart. Let them figure out what works for their situation.”

Although students can continue their studies longer, I-PhD only includes four years of funding. Beyond that, universities are responsible for funding students, and most U.S. STEM students take five years or longer to complete a Ph.D.

“I’m curious if this signals to students that four years is the amount of time the program should take, and if it is really enough time,” says Gabriella Coloyan Fleming, founder of Oerlikon Research & Consulting, a research-based consulting firm focused on bridging the gap between STEM industry and education through workforce strategy and educational-program design. One of the main goals of a Ph.D. is to learn research skills, she says, so she would want to make sure that this remains a focus as industry stakeholders become involved in student training. “How can you make sure that the Ph.D. is still a research degree, rather than work experience?”

Boccanfuso acknowledges that Ph.D. length is a “contentious issue,” but says that it’s outside of UIDP’s scope.

Changing Mindsets in Academia

Apart from its length, Fleming says the pilot could also signal to students that working in industry is a valid option. Even though most STEM Ph.D. graduates end up in industry jobs, “the industry career pathway can be a taboo topic in academia—sometimes professors think it’s academia or bust,” she says. The program could help break the barrier and provide perspectives and mentorship students might not otherwise have access to.

Professors who are already involved in university programs with industry partners are also excited about the pilot’s potential as a new option for students.

“This is a welcome addition to the federal funding landscape for research,” says Diana Marculescu, chair of the electrical and computer engineering department at the University of Texas at Austin. “I’m really interested to see more, and I’m looking forward to seeing how we can participate.”

At UT Austin, partnerships with local companies are already in place, in part through a part-time Ph.D. program that allows employees to pursue a doctorate while at a company, so many faculty members are willing to work with students doing research in industry. Some students also gain industry experience during internships, which Marculescu sees as an integral part of engineering Ph.D. programs. Many STEM faculty members, however, frown upon internships because they usually take place during summers—a time when students can spend more time on academic research.

Working with industry to co-advise a student will require a shift in mindset, she says. “If universities or institutions of higher education are not familiar or willing to work in that framework, it’s going to take some time to get buy-in.”

Eric Fossum, a professor for emerging technologies at Dartmouth College who directs its long-standing Ph.D. Innovation program, also encourages students to spend summers at company internships. For Fossum, practical hands-on experience in industry is a natural part of Ph.D. training. Dartmouth is among the first group of universities who have expressed interest in joining the I-PhD program, which he describes as “a great step forward, in terms of bridging the gap between industry and academia.”

Dartmouth’s own program includes up to six months at an industry internship and coursework to teach the business skills necessary to commercialize research. In Fossum’s experience, students love learning about the business side of technology. Historically, many faculty members have viewed applied research as having a lower standing than purely academic work, but Fossum says more academics are now also becoming interested in the practical uses of their research—something for which his work inventing the CMOS image sensor is a “poster child,” he says.

“It’s been so rewarding to me to have that industry experience and the academic experience, research experience, all at the same time. I really believe it’s important…to integrate that process as much as we can,” Fossum says. Still, “it’s going to be a challenge for both parties to be able to speak the same language and come to a common cultural understanding about what’s expected and what might be achieved.”

The pilot includes an external evaluation component to determine the future of the program after the initial period, and UIDP’s Boccanfuso says industry need will eventually determine its size. “Could I see thousands of students [enrolled] 10 years from now? Absolutely,” he says. “I’m biased, I think this is going to be a great addition. But let’s see what the data says.”

For most of my career, my IEEE membership sat quietly in the background—a line on my résumé, a discount code for a conference registration, and access to the IEEE Xplore digital library, which I underutilized. I didn’t think much about the grade of membership available above that of the regular member. I assumed senior membership was reserved for people further along in their career than I was. They published more papers, had more gray hair, and had worked longer in the field.

I was wrong on all three counts. The misunderstanding cost me an important validation of my skills and professional competency.

I suspect a lot of other qualified members are where I was one year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential.

The myths that almost stopped me

Here are a few of the misconceptions about senior membership:

It’s mostly for academics and longtime IEEE volunteers. It isn’t. The grade is explicitly built around a person’s professional engineering experience. Plenty of successful applicants have never published a paper. Industry experience counts for a lot.

You need a graduate degree. You don’t. A bachelor’s degree plus enough years of qualifying experience is sufficient on its own. An advanced degree simply offsets some of the required years of experience.

If I’m not well-known in my field, I won’t qualify. Senior membership isn’t a popularity contest. Rather, it hinges on whether you meet specific experience metrics. The requirement is “sustained, significant technical contribution,” not “known beyond your organization.”

I should wait until I have more significant achievements to point to. I believed this for longer than I should have. If you meet the 10-year experience threshold with five years of significant performance, you’re already eligible. Waiting doesn’t strengthen a qualifying application; it just delays getting a credential you’ve already earned.

Why I applied for senior membership

The push to apply came from a practical need. As a senior data scientist at Apple in Austin, Texas, I work in applied machine learning, building large-scale systems that affect customer-support operations. I already had started taking on more peer-review work—checking papers for journals including Neural Networks and IEEE Transactions on Knowledge and Data Engineering, mentoring at Apple, and writing on public platforms such as Medium and SimpleTalk.

I wanted a credential that reflected that shift from “engineer who codes” to “engineer who helps shape the field.”

The IEEE senior member grade turned out to be the validation of my work I was looking for. It’s not an award for a single achievement. You have to apply for it, and it’s a peer-evaluated process that confirms you’ve sustained a meaningful level of professional contributions over time.

That distinction matters. Having a research paper published or being granted a patent proves a moment in time. Senior membership reflects a pattern of continuous contributions.

The benefits to my career happened faster than I expected. It strengthened how search committees, IEEE conference organizers, and IEEE awards panels viewed me. Only senior members can hold certain IEEE leadership positions.

The senior grade also opened doors to editorial and reviewer roles I hadn’t even pursued before. Journal editors and conference organizers often look for reviewers with a track record they can verify quickly, and senior membership gives them that signal without extra vetting on their end. It also gave me a credential I could point to in professional contexts, including, in my case, supporting documentation for a U.S. employment-based immigration petition, where third-party peer recognition carries real evidentiary weight.

Navigating the process

The process for applying for senior membership is easier than the title might suggest. To qualify, you need a combination of professional and academic experience in an IEEE-designated field: engineering, computer science, information technology, physical sciences, mathematics, or technical communications. The two must total at least 10 years, with at least five of them showing significant performance. Crucially, experience isn’t limited to job titles. Graduate research, technical leadership, and progressively responsible engineering work all count toward the total number of years. I’d been quietly accumulating qualifying years without ever framing them that way.

“I suspect a lot of other qualified members are exactly where I was a year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential.”

You submit your application through IEEE’s member portal, mapped against the experience requirement, along with three references from current IEEE members—at least two of whom must be senior members or IEEE Fellows who can vouch for the credibility of your work.

The IEEE member grade evaluation committee reviews applications and renders decisions.

How to find references

The part everyone underestimates is references. Applications can stall at this point. References must be IEEE members in good standing, and at least two need to be IEEE senior members—which means you can’t necessarily ask people who know you best. You need to find references who are both willing to vouch for you and are grade-eligible.

My advice is to identify and confirm all three references before you submit your application. It might be difficult to add or swap a reference during the process, and a stalled reference could delay your file.

Where to find references is the part I worried most about. But it turned out to be far easier than I expected.

Here are several sources:

• IEEE Collabratec. This is IEEE’s professional networking platform and, in my opinion, is an underused resource. You can search by technical interest, geography, or society membership and message members directly. I found several of my eventual references this way—colleagues I’d never have thought to ask simply because we hadn’t worked together directly, but ones who knew my technical work through shared communities or conference circles.
• Coworkers and colleagues, current and former. If you’ve worked alongside IEEE members—especially ones senior to you—they’re often the most natural fit because they can speak specifically to your day-to-day technical contributions.
• Former professors. If you did graduate work, your advisor or committee members are usually IEEE members and are well positioned to speak to your research contributions, even years later.
• LinkedIn. A surprising number of my qualifying references came from reconnecting with people on LinkedIn I’d lost touch with professionally. A short, specific, polite message explaining what you’re applying for and why you thought of the person can go a long way.

A pattern I noticed when looking for references is that people are generally glad to be asked. Serving as a reference is a small lift for them and a meaningful one for you. Most senior engineers remember someone doing the same for them and are happy to pay it forward.

If you’re on the fence

If you’ve been in the field for a decade or more, doing real technical work, and IEEE membership has been sitting quietly in the background of your career the way it did in mine, it’s worth 10 minutes to check the eligibility criteria against your history. You might find, as I did, that you qualified for the membership upgrade a while ago.

The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice?

Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver, an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this.

“We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it,” says Brunhaver, who received a National Science Foundation award in 2020 to study how to foster greater workplace adaptability among young engineers. For this ongoing project, she has interviewed engineering managers, early career employees, and undergraduates about their experiences.

Part of the problem, she says, is that every employer has its own idea of what to be adaptable means. Generally, Brunhaver defines adaptability as “the ability to recognize that a change or uncertainty is occurring, and then respond effectively to that change.” But the skill is context-dependent. In software engineering, that might mean responding to turnover in the tools you use on a daily basis, while aerospace or biomedical engineers may need to keep track of changing procedures and regulations. “Managers are all saying adaptability is important,” Brunhaver says, “but defining it in different ways.”

At the same time, engineers are all contending with changes beyond these industry-specific expectations. Jobs in the technology, media, and telecom sectors are experiencing the fastest pace of skill turnover, according to a June 2026 report on the effects of AI from the professional services network PwC. And the World Economic Forum’s most recent Future of Jobs Report, published in 2025, found that employers across all sectors expect 39 percent of workers’ core skills to change by 2030. This uncertainty can be uncomfortable. But with the right mind-set and support from leadership, adaptability can help keep you afloat.

How to Cultivate Adaptability

The AI transition is a big shift—but not an unprecedented one, says Jenna Butler, a research scientist at Microsoft who studies developer well-being and productivity.

During this type of paradigm shift, there is often a “chaos period” when a new normal is being established, Butler says. In AI’s case, it challenges the understanding of what a computer can do. “I think we’re still in this in-between, difficult period that we’ve seen before, but [it] is maybe moving faster than it has historically.” Software engineers—in one of the fields most affected by AI—are now facing a significant increase in code review. “If you ask 20 developers, you get 23 different ways of working with it. Everyone is trying to sort it out,” says Butler, who describes this period as “the uncomfortable middle.”

“We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it.”– Samantha Brunhaver, Arizona State University

Brunhaver says one way educators can help prepare students before they enter the workforce is by offering a diversity of real-world experiences, such as internships, team-based projects, community service, and leadership roles. Each of these teach students to adapt to different challenges, easing their transition from school to work.

It’s also important to encourage reflection, Brunhaver adds, noting that metacognition helps individuals use the skill more effectively. “In order to adapt, you have to think that you have agency and the ability to get through a situation.” Ultimately, it comes down to three steps: Perceive a need to adapt, evaluate your options, and act.

For those already in the workforce, that action may mean taking the time to learn new tools and ways of working. Software engineering, for instance, may soon rely more on prompting models and managing agents than coding line by line. “I think people who went into software because they like solving problems are going to have a lot of fun, and people who just enjoy the art of writing code are not,” Butler says.

The More Things Change…

Although the tools engineers use on a daily basis are evolving, the core responsibilities of the job are more stable than they may seem, says Andy Hunt, a software developer who coauthored The Pragmatic Programmer (Addison-Wesley Professional) in 1999. The book outlines practical coding principles, and has been taught in many computer science classrooms. When Hunt was working on the 20th anniversary edition of the book, he was surprised by how much of the advice still applies. And now, seven years later, he maintains that belief.

“The fundamental part of the job is problem solving and communication, and that’s always going to be there,” he says.

Hunt emphasizes the importance of developing systems thinking over particular tools. To him, identifying as a Java programmer, for instance, is “like a carpenter saying, ‘I’m a hammer user,’ or ‘I specialize in cordless drills.’ ”

He acknowledges that today’s hiring process, in which companies often filter résumés for certain languages or years of experience, makes it harder to embrace a more expansive way of relating to your job. Employers, he says, should recognize that “the tech’s not the hard part, and it never has been. Understanding information theory, understanding systems thinking, understanding what constraints you’re up to—that’s still the hard part.”

With this type of misalignment between employers and employees, AI is also intensifying an old source of tension: How can engineers slow down enough to adapt and learn new tools when the pressure to become more productive keeps mounting?

Who’s Responsible for Enabling Change?

Young engineers need to embrace change. However, educators and employers also play a role in building a successful workforce. From the educator’s perspective, Brunhaver says “we need to be more explicit about what [adaptability] means and why it’s important.” Managers, meanwhile, should invest in their employees’ professional development.

Microsoft research scientist Butler often encourages leadership to set aside intentional time for continuous learning for their engineers—even just an hour a week—without any expectation that they will produce code or progress in their daily work. “I realize that’s difficult,” says Butler. “I would encourage people to do it on their own, but I would really encourage organizations and leaders to do it, because you’re not going to get this sudden change in your people if they don’t have time and space to learn how to work differently.”

This also means providing enough instruction, Butler adds. When developers aren’t given enough guidance on adopting something new, while being pressured to increase productivity, they risk doubling down on the tools they already know and burning out.

“I do imagine the next number of years could be challenging,” Butler says. Engineers will have to adapt to find their place in an evolving workforce—but they also have a say in shaping that future.

“Being adaptable sort of implies that you’re going to change based on what’s happening around you, and I would really like people to realize the change that’s happening is somewhat up to us,” she says. All individuals have a choice in how they use AI, for instance, and which models they use. “We need to be adaptable and go with the flow to a degree, but we also need to be directing that flow. The future with AI is absolutely not predetermined.”

This article appears in the September 2026 print issue as “The Adaptable Engineer.”

EPICS in IEEE, a service learning program for university students supported by IEEE Educational Activities, offers students opportunities to engage with engineering professionals and mentors, local organizations, and technological innovation to address community-based issues.

The following two environmentally focused projects demonstrate the value of teamwork and direct involvement with project stakeholders. One uses smart biodigesters to better manage waste in Colombia’s rural areas. The other is focused on helping Turkish olive farmers protect their trees from climate change effects by providing them with a warning system that can identify growing problems.

No time to waste in rural Colombia

Proper waste management is critical to a community’s living conditions. In rural La Vega, Colombia, the lack of an effective system has led to contaminated soil and water, an especially concerning issue because the town’s economy relies heavily on agriculture.

The Smart Biodigesters for a Better Environment in Rural Areas project brought students together to devise a solution.

Vivian Estefanía Beltrán, a Ph.D. student at the Universidad del Rosario in Bogotá, addressed the problem by building a low-cost anaerobic digester that uses an instrumentation system to break down microorganisms into biodegradable material. It reduces the amount of solid waste, and the digesters can produce biogas, which can be used to generate electricity.

“Anaerobic digestion is a natural biological process that converts organic matter into two valuable products: biogas and nutrient-rich soil amendments in the form of digestate,” Beltrán says. “As a by-product of our digester’s operation, digestate is organic matter that can’t be transferred into biogas but can be used as a soil amendment for our farmers’ crops, such as coffee.

“While it may sound easy, the process is influenced by a lot of variables. The support we’ve received from EPICS in IEEE is important because it enables us to measure these variables, such as pH levels, temperature of the reactor, and biogas composition [methane and hydrogen sulfide]. The system allows us to make informed decisions that enhance the safety, quality, and efficiency of the process for the benefit of the community.”

The project was a collaborative effort among Universidad del Rosario students, a team of engineering students from Escuela Tecnológica Instituto Técnico Central, Professor Carlos Felipe Vergara, and members of Junta de Acción Comunal (Vereda La Granja), which aims to help residents improve their community.

“It’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community.” —Vivian Estefanía Beltrán

Beltrán worked closely with eight undergraduate students and three instructors—Maria Fernanda Gómez, Andrés Pérez Gordillo (the instrumentation group leader), and Carlos Felipe Vergara-Ramirez—as well as IEEE Graduate Student Member Nicolás Castiblanco (the instrumentation group coordinator).

The team constructed and installed their anaerobic digester system in an experimental station in La Vega, a town located roughly 53 kilometers northwest of Bogotá.

“This digester is an important innovation for the residents of La Vega, as it will hopefully offer a productive way to utilize the residual biomass they produce to improve quality of life and boost the economy,” Beltrán says. Soon, she adds, the system will be expanded to incorporate high-tech sensors that automatically monitor biogas production and the digestion process.

“For our students and team members, it’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community. It enables all of us to apply our classroom skills to reality,” she says. “The funding we’ve received from EPICS in IEEE has been crucial to designing, proving, and installing the system.”

The project also aims to support the development of a circular economy, which reuses materials to enhance the community’s sustainability and self-sufficiency.

Protecting olive groves in Türkiye

Türkiye is one of the world’s leading producers of olives, but the industry has been challenged in recent years by unprecedented floods, droughts, and other destructive forces of nature resulting from climate change. To help farmers in the western part of the country monitor the health of their olive trees, a team of students from Istanbul Technical University developed an early-warning system to identify irregularities including abnormal growth.

“Almost no olives were produced last year using traditional methods, due to climate conditions and unusual weather patterns,” says Tayfun Akgül, project leader of the Smart Monitoring of Fruit Trees in Western Türkiye initiative.

“Our system will give farmers feedback from each tree so that actions can be taken in advance to improve the yield,” says Akgül, an IEEE senior member and a professor in the university’s electronics and communication engineering department.

“We’re developing deep-learning techniques to detect changes in olive trees and their fruit so that farmers and landowners can take all necessary measures to avoid a low or damaged harvest,” says project coordinator Melike Girgin, a Ph.D. student at the university and an IEEE graduate student member.

Using drones outfitted with 360-degree optical and thermal cameras, the team collects optical, thermal, and hyperspectral imaging data through aerial methods. The information is fed into a cloud-based, open-source database system.

Akgül leads the project and teaches the team skills including signal and image processing and data collection. He says regular communication with community-based stakeholders has been critical to the project’s success.

“There are several farmers in the village who have helped us direct our drone activities to the right locations,” he says. “Their involvement in the project has been instrumental in helping us refine our process for greater effectiveness.

“For students, classroom instruction is straightforward, then they take an exam at the end. But through our EPICS project, students are continuously interacting with farmers in a hands-on, practical way and can see the results of their efforts in real time.”

Looking ahead, the team is excited about expanding the project to encompass other fruits besides olives. The team also intends to apply for a travel grant from IEEE in hopes of presenting its work at a conference.

“We’re so grateful to EPICS in IEEE for this opportunity,” Girgin says. “Our project and some of the technology we required wouldn’t have been possible without the funding we received.”

A purpose-driven partnership

The IEEE Standards Association sponsored both of the proactive environmental projects.

“Technical projects play a crucial role in advancing innovation and ensuring interoperability across various industries,” says Munir Mohammed, IEEE SA senior manager of product development and market engagement. “These projects not only align with our technical standards but also drive technological progress, enhance global collaboration, and ultimately improve the quality of life for communities worldwide.”

For more information on the program or to participate in service-learning projects, visit EPICS in IEEE.

On 7 November, this article was updated from an earlier version.