Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

Climate change is the issue.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Privacy, which companies were once happy to claim was all about personal choice, is now primed to affect everyone around you even more than it already did.

Meta and Google’s smart glasses can record anything the owner is looking at or use artificial intelligence to describe it. Apple’s* Audio Intelligence feature turns every Apple Watch into a transcription machine that records or summarizes your day. Amazon’s Bee devices take that a step further and can record and transcribe everything. Dozens of other companies are introducing pendants, rings, and other products designed to record, transcribe, or summarize everything we say and do all day long.

Privacy has always been a team sport. The actions you take with communication apps, collaborative software, or your cloud backup solutions, have long impacted everyone else you’re interacting with. But moving those choices deeper into the physical world where strangers, colleagues, family, and friends are all now a part of your privacy choices is drastically changing the game.

Tech companies want us to accept a reality where one person’s privacy choices (or lack thereof) means you suddenly need to be an expert in consumer tech who understands the capabilities of many devices on the market, how they work, how and if they signal when they’re recording, and what platforms they do or do not connect to. They believe these ambient surveillance devices that work in the background and are designed to look as normal as possible are the future, while they also expect the owners of these devices to use them responsibly and to notify those around them of their capabilities to act within whatever laws their states may have. 

This is not reasonable, nor how consent works in any practical sense.

Let’s break this down by looking at some of the biggest players currently in the game: Apple and Amazon, who have two different takes on transcribing audio throughout the day, as well as Meta and Google, who both have slapped AI and cameras onto eyeglasses. 

How to Think About the Technical Promises Companies Make

Ask Apple about privacy concerns and they’ll point to a white paper that explains all the technical barriers between them as a company and the data generated by the watch. Ask Meta and they’ll talk about all the ways you can customize the privacy controls or how certain details, like faces, may be blurred out when you interact with Meta AI through the glasses (but not during standard recording). Google does this too. Amazon will also talk about how you’re always in control, and no audio is stored (though it’ll also let you enable Amazon store integration, so your conversations can directly result in ads for products). And so on across all the companies making these products.

Some of these solutions do a better job than others at cutting out potential law enforcement or company access to this data. There will be a time to dive into these details more deeply, but the technical privacy promises these companies focus on only cover the consumer and user of the device. They ignore every bystander you interact with and their inability to actually opt out. There is no technical protection for you as a non-user of these devices, the person who finds yourself captured by them. 

Still, we hope to look deeper into the technical differences between this category of devices soon.

There Must Be Some Laws to Consider, Right?

With something like Apple’s Audio Intelligence and Amazon’s Bee devices, it may be best to treat them similar to an always-recording microphone, even if companies try to claim there’s “technically” no audio recording. These tools still often use a microphone to capture raw audio from conversations and convert it into summaries or transcripts before discarding the audio. Google's blog post notes that it has whole responsible use part of their Terms of Service where users have to agree not to record others without permission or in sensitive places. 

But it’s not just the question of the legality from the point of view of the user. Law enforcement will eventually try to compel access to the data that these products create (either real-time audio or the summaries and transcripts they create). And while some products, like Apple’s Audio Intelligence, are engineered to make that difficult, not every product will be. We know from history that when data exists, governments will do everything they can to access it. There’s no reason to think something would change today.

No Tech and Legal Solutions Will Reduce Social Issues

There is a problem humming along in the background that is impossible to address with technical or legal solutions. If secretly documenting conversations becomes the norm, it makes every private conversation less spontaneous and open. It breaks a longstanding social norm that you will not record and document every conversation with friends, family, colleagues, or strangers. 

The companies that make these sorts of products like to claim they’re no different from tools we already have. You can easily record everything right now with a phone or a camera. Heck, you can do so with a camcorder or cassette recorder if you want. And you can transcribe conversations with pen and paper, too. But those are actionable steps we take that both require effort from the user, and are clear to others around us. This new wave of passive surveillance tech is designed to encourage continuous monitoring and hide its use. 

Even taken at face value, none of these products can understand the complex social context we all navigate every day. We intrinsically know there are different social agreements we all make when we’re standing on a street corner, at a protest, in a bathroom, at a doctor’s office, at the gym, or in a restaurant. Taking notes in a meeting at work is completely normal. Getting a summary of a conversation with your best friend about their divorce is not. Always-on monitoring of our conversations is an unacceptable burden on our conversational privacy. 

Google seems to all but admit this problem in a conversation with CNN when it described the company’s experiments with a notification system that would alert people’s phones when someone was recording nearby: 

“And just alerting bystanders doesn’t do much to help, [Juston Payne, senior director of product management at Google] said. “It can actually be weirdly disempowering to you in that you’re like, ‘What am I meant to do now?’” he said.”

What We Can Do About It

It’s easy to buy into the tech company line that these products are inevitable, but we’ve seen so many vaporware products over the years that we should take those claims of success with a grain of salt. We’ve seen entire product categories come and go within just a few years, and if the public doesn’t want this stuff, the tech companies will have to throw in the towel. 

Of course, that doesn’t mean we all just have to sit around and hope that’s what happens. For its part, Apple launched its Audio Intelligence features in beta specifically to get feedback, and we think it’s worth taking the time to fill out their feedback forms (or in the Feedback Assistant app if you’re in the beta) and let them know what you think.

Products from the likes of Meta, Google, Amazon, and others might be harder to push back on, but politely and clearly engaging with those who use them in ways that make you uncomfortable is a good place to start. And if you see abusive videos on social networks where these sorts of products are used, report them. If you find these products useful for yourself, make it clear to those around you when you’re using them, and only do so with consent and in social moments where it makes sense to do so.

*Apple is an EFF donor. EFF donors do not influence our agenda or our positions.

This essay is adapted from a keynote speech delivered at the DEF CON 34 conference in Las Vegas on August 7, 2026. The essay originally appeared in CyberScoop.

I want to talk about AI hacking. There are three aspects: people hacking AI systems, people using AI to hack other systems, and AIs hacking systems on their own, not even maliciously. The last two matter more than the first. The third is the weirdest and potentially the most destabilizing.

I started writing about this in 2021 and in my 2022 book, A Hacker’s Mind. Back then I said it was mostly still science fiction, but not stupid science fiction. We’re seeing it more and more, especially in the past year. The trends I’m describing don’t require a major breakthrough in AI. They’re starting to happen now, and they’ll happen more often as AIs get better at autonomous problem-solving. And it will involve far more than computer code...

As people age, they may sleep for shorter periods and wake up more frequently during the night. In addition to those disruptions, MIT researchers have now discovered that a process responsible for flushing waste from the brain during sleep is commonly impaired in older people.

The researchers found that waves of cerebrospinal fluid (CSF), which help remove debris from the brain, are significantly reduced in older adults. This impairment is linked to a decrease in the slow brain waves that normally occur during deep sleep, the researchers found, and is not just a result of getting less sleep.

The researchers now hope to explore whether the disruption of CSF flow increases the risk of neurodegenerative disorders, and if any of the sleep patterns they observed might offer a way to predict an individual’s risk.

“We know that people age really differently. Some people age very successfully with great cognitive outcomes, and other people develop cognitive decline. If we can figure out who has great trajectories of aging and what are the factors that are associated with that, we might be able to use that to predict future outcomes,” says Laura Lewis, the Athinoula A. Martinos Associate Professor of Electrical Engineering and Computer Science, a member of MIT’s Institute for Medical Engineering and Science and the Research Laboratory of Electronics, and an associate member of the Picower Institute for Learning and Memory.

Her lab also plans to investigate whether there might be any interventions that could improve CSF flow in older adults.

Lewis is the senior author of the study, which appears today in Nature Aging. Sydney Bailes, a postdoc in the MIT Laboratory for Information and Decision Systems, and Stephanie Williams, a former visiting student at MIT who earned her PhD from Boston University in 2026, are the lead authors of the paper.

Smaller waves

During the deepest stages of sleep, also called non-REM sleep, high-amplitude, low-frequency waves contribute to physical recovery, memory consolidation, and many other functions. In a 2019 study, Lewis found that these slow waves are also critical for generating waves of CSF that help flush waste from the brain.

That finding led her to wonder whether reductions in slow waves in older people might also reduce CSF flow. Previous studies of aging have yielded conflicting evidence; some found that CSF flow goes down in older adults, while others found it goes up, but these studies did not monitor flow during sleep.

In the latest study, the researchers recruited volunteers in two age groups: 18 to 39 and 60 to 84. The tests lasted for about two hours and began late at night, so that participants would be sleepy. 

The researchers used traditional fMRI to measure participants’ blood flow in the brain, along with a modified MRI technique, developed by Lewis, that measures CSF flow. While the subjects were in the scanner, the researchers also measured their brain waves using EEG. 

Many of the participants fell asleep in the scanner, allowing the researchers to compare their brain activity while awake and while sleeping. 

As they had seen in earlier work, the researchers found that when the younger adults fell into non-REM sleep, their EEG slow waves became stronger, and their CSF waves became larger. However, in the older group, the average CSF waves were significantly smaller than those seen in the younger group. 

Impaired flow

In additional experiments done while subjects were fully awake, the researchers tried to figure out what was causing the age-related declines in CSF flow.

First, they explored the possibility that changes in a phenomenon known as neurovascular coupling might play a role. When localized brain activity increases the demand for oxygen, nearby blood vessels dilate and deliver more oxygen and nutrients. That increase in blood flow can be measured with fMRI.

The researchers tested each subject’s neurovascular coupling by measuring blood flow while the subjects looked at a flickering checkerboard image — a task that typically drives an increase in neural activity, along with an increase in blood volume.

In many of the older subjects, while their neural activity did go up, the blood flow didn’t increase much, suggesting that the blood vessels’ ability to respond to increased oxygen demand has diminished.

During another task, the researchers tested whether the brain’s vascular system might be impaired in the older subjects. They asked the subjects to hold their breath, then measured what happened inside the brain. Normally, this leads to an increase in carbon dioxide levels in the blood, which triggers an increase in cerebral blood flow. However, in the older participants, the average blood flow increase was smaller than the increase seen in the younger subjects.

These two factors — reduced neurovascular coupling and diminished blood flow — accounted for much of the decline in CSF flow but could not explain all of it, according to a statistical model developed by the researchers. This suggests that there may be other age-related factors that contribute to the reduction in CSF flow, they say.

“Aging is so complex, and so many different things change, that it’s really hard to disentangle the individual factors,” Bailes says. “We try to account for some of the mechanisms, but there definitely are a lot of other factors related to vascular health and lifestyle. If you were able to measure all of them, you might be able to break it apart into different explanations for why an individual’s CSF flow might be more or less impaired.” 

Enhancing CSF flow

Previous studies have found that sleep disturbances are linked to a greater risk of neurodegenerative disorders such as Alzheimer’s and other forms of dementia.

None of the subjects in this study had been diagnosed with a neurodegenerative disorder, but the researchers found that some of the older adults showed signs of gray matter atrophy, which was correlated with a greater decrease in CSF flow. They hope to continue studying some of the participants in hopes of discovering whether changes to CSF flow are associated with a higher risk of developing a neurodegenerative disorder.

They also hope to test possible interventions that might help increase CSF flow. In a recent study, Lewis’ lab found that short bursts of “pink noise” during sleep could increase CSF flow in younger adults, but they have not yet tested that intervention in older adults. 

In other work, they plan to study why some older adults don’t experience a dropoff in CSF flow. In this study, the researchers found that while as a group, older adults showed weaker CSF waves during sleep, some individuals in the group maintained normal CSF waves.

“It’s really intriguing that for some of these older adults, their CSF fell right in the range of the healthy young adults,” Lewis says. “We are really interested in what’s different in those people and how that might be related to future health outcomes.”

The research was funded by the National Institutes of Health, the Simons Collaboration on Plasticity in the Aging Brain, a McKnight Scholar Award, a Pew Biomedical Scholar Award, an MIT EECS Transformative Research Award, the Picower Institute Neurological Disorder Discovery Research Fund, a Sloan Fellowship, and the Department of Defense National Defense Science and Engineering Graduate Fellowship Program.

The use of algorithms to design structures has led to new parts optimized for ultralight aircraft, high-bandwidth antennas, and F1 racing cars.

But the best algorithmic design techniques still require lots of computation and time: Users only see the final design after it’s complete, after which they often must start the design process all over again to make changes. That process also makes it harder to trust that the final designs are structurally sound.

MIT researchers are making algorithmic designs more collaborative with a system that acts like an AI copilot. As the algorithm is generating the structure, it presents regions to the human designer for feedback.

In an open-access Communications Engineering paper describing the system, the researchers showed it led to more easily manufacturable structures with higher buckling loads while adding only 15 seconds to the total design time compared to all-algorithmic approaches.

The researchers believe the approach, which uses a design technique known as topology optimization, could make algorithmic design more accessible and manufacturable for a broader class of design engineers.

“Right now, the human doesn’t have any agency or control over the design process as it’s playing out,” explains lead author Dat Ha PhD ’26, a recent MIT civil and environmental engineering doctoral student who is now a structural designer at LeMessurier. “For these really complex designs, we wanted to also tap into our brain, which is the most complex machine there is, to solve these design problems in a way that’s more practical to industry.”

Joining Ha on the paper are Ferdous Alam, a former MIT postdoc who is now an assistant professor at Georgia Tech; Markus Buehler, the MIT Jerry McAfee (1940) Professor of Engineering; Faez Ahmed, MIT associate professor of mechanical engineering; and senior author Josephine Carstensen, MIT associate professor of civil and environmental engineering.

Bringing a human in the loop

Topology optimization is one of the most advanced and promising algorithmic design techniques. It uses formal optimization techniques to iteratively fill in the space given for a design in a way that maximally achieves the stated goals. For most applications, the process requires a lot of computation and time. When it’s finally done, the designs can be impractical to build or have other issues the engineer didn’t consider. For instance, some designs might have eight or more beam connections at a single point, which would be difficult to make in the real world.

“Engineers are always very skeptical of computer programs, especially if they give you very weird-looking or unexpected designs,” Ha says. “On paper, topology optimization is really powerful and can achieve great, high-performing designs, but its black box nature has made it less widely adopted in industry.”

Topology optimization has gotten traction in the automotive and aerospace industries, but it’s been used less to design buildings or bridges, partly because design engineers need to know they can trust the output.

“A big limiting factor has been that the design engineer has very little influence over how the design evolves and therefore can’t control undesirable features,” Carstensen explains. “The lack of control and lack of ownership also inhibits its use.”

To address those problems, the researchers made topology optimization more collaborative in an approach that Carstensen compares to having a design copilot.

Prior attempts at adding human judgment slowed things down dramatically. The researchers’ new system uses machine learning to quickly identify the most likely regions of an algorithmic design that a human would want to change. To train the machine-learning model, the researchers created a synthetic dataset of probable human feedback to thousands of designs that critiqued things like aesthetics, performance criteria, and manufacturability.

“We thought if we could create a record of where humans have made changes to these programs, then perhaps there’s a pattern to those choices, which is not very different from other types of machine-learning approaches,” Ha says.

Carstensen sees the system as bringing together the best of algorithmic and human design approaches.

“The idea is to tap into the brains of the designers and get them to infuse their domain knowledge as the design evolves,” Carstensen says.

In demonstrations, the researchers showed their human-in-the-loop approach improved manufacturability and linear buckling load by 39 percent while only modestly increasing the total design time.

Right now, the approach only allows users to make structures within identified regions thicker or thinner, but they hope to add other options for modification in future versions.

“The hope is that by addressing these hurdles, we open up topology optimization to a much wider audience, not only in industry but also to researchers and students in academia,” Carstensen says.

From designs to structures

The researchers see their approach complementing AI only-approaches to design.

“We are arguing there’s room for both,” Carstensen says. “If you are in a discipline that has a lot of computational resources, you can use the other approach. But if you really need the human input, you can use this collaborative approach.”

Adding human input also serves to increase trust.

“In some settings, the humans need to make the final decisions before advancing the final design,” Carstensen says. “By involving humans, we can get higher-performing designs to the real world to create higher-performing structures. Because it’s not just about designing good artifacts; it’s about creating good artifacts that humans will actually accept and make.”

The work was supported, in part, by the MathWorks Engineering Fellowship Fund, TATA Steel, and the MIT Generative AI Impact Consortium.

Despite fears about robots replacing humans at work, there are still plenty of things humans can do far better than machines. That’s fine with Robust AI, a startup building robots designed to complement what humans do best.

The company’s mobile robot, named Carter, resembles a utility cart on wheels and is designed to fit seamlessly alongside humans in fast-changing warehouse and factory environments. Carter features a force-sensitive handlebar for easy guidance, an AI-powered vision system, and holonomic wheels for instant movement in any direction.

As a result, humans can cut Carter off, push and pull it in any direction with one hand, and direct it with gestures or the onboard touch screen.

The company’s robots are currently deployed in logistics warehouses and on factory floors, where they help humans while picking or stocking shelves and moving items across facilities autonomously.

“In these environments, what really determines success is how the overall system performs,” explains Anthony Jules ’92, SM ’92, who co-founded the company with MIT Professor Emeritus Rodney Brooks. “The robot does some of the work, the people do some of the work, and the backend systems do some of the work. It’s that combination that determines how well your system performs. We believe it’s really important that people have an appropriate level of agency and input so that they’re always moving the system forward.”

Collaborative robots

Brooks was a faculty member at MIT from 1984 until 2010 and served as the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) from 1997 until 2007. While at MIT, he co-founded iRobot, which invented the popular Roomba vacuum and went public in 2005. Later, he co-founded the company Rethink Robotics, which also designed collaborative robots.

Jules met Brooks as a graduate student at CSAIL in the 1990s. The two never worked together, but their labs were on the same floor. The pair kept in touch after Jules graduated and co-founded the company Sapient, which went public before being acquired by Publicis in 2015.

In 2019, Brooks bumped into Jules at a robotics conference. He told Jules he was thinking of starting a robotics software company, which he compared to a video game engine for training robots. Robust AI was founded later that year.

The Covid-19 pandemic caused the founders to shift to creating robots that could autonomously disinfect surfaces, but when it was discovered that Covid-19 was not transmitted via surfaces, they had to pivot again.

“That forced us to decide how to use this technology we had been building, which was really about helping robots understand the world more fully,” Jules recalls. “Long story short, I said to Rodney, ‘I have an idea. You’re going to love it and you’re going to hate it. You’re going to love it because it’s solving problems for customers that we’ve been hearing about, and you’re going to hate it because we have to build hardware.”

The founders had been interviewing potential customers for years through Robust AI’s early pivots. They finally decided on logistics and manufacturing as the industries to target early on.

“When people think about automation in factories, they think about pictures of the 5 percent that are fully automated with $50 to $200 million dollar systems,” says Jules, who now serves as Robust AI’s chief innovation officer. “They don’t think about the vast majority of factories that aren’t automated at all. These factories don’t even have a conveyor belt. They are not going to tear everything out and replace it with a $200 million system.”

Robust AI’s robots can be phased into operations as needed and offer several workflows for customers. In one workflow, the robot can guide human workers to specific spaces to retrieve items. In another it can follow humans to important spots for loading. The primary workflows involve Carter robots moving things from one place to another, often in large warehouses or factories, where they say human workers can walk on average 30,000 steps a day.

“These are huge spaces, so a person might load the robot with things and then need to bring them to another station, but that could be 400 feet away,” says, Brooks, Robust AI’s chief technology officer. “Now the person can just say ‘done’ and the robot brings it instead. How the robots are integrated into the workspace is entirely up to the customer.”

Getting work done

Leila Takayama, Robust AI’s vice president of human robot interaction, recently discovered a peer-reviewed study of robots in hospitals, which were used to do things like gather dishes, move sheets, and transport other equipment. The researchers discovered that, in many hospitals, a large number of robots were turned off and against the walls. The nurses said they broke.

“What really happened was the nurses were rushing with a patient on a gurney, and the robot sees them in the hallway and stops dead, and they couldn’t get the gurney around them,” Brooks says. “The robots were getting in the way of nurses’ primary tasks.”

Robust AI’s approach to technology development is to understand workers’ primary tasks deeply and build features that accelerate those tasks, whether a human or a robot is doing them.

“We let humans do what humans are good at in the physical world, which we’ve had millions of years of evolution to get good at, and have the digital system respond to that and provide us with all the things that it is good at,” Brooks explains.

Nature Climate Change, Published online: 13 November 2026; doi:10.1038/s41558-026-02795-7

The authors show that, for a tropical seabird model species, winter–spring phytoplankton blooms kept a stable mean timing but became more variable between years. Breeding synchrony weakened and reproductive success declined and became less consistent over the same two decades.

As humans expand their ambitions in deep space, energy-intensive missions can’t afford to rely solely on combustion or solar power. They will need nuclear energy for propulsion and surface habitation.

Jake Hecla ’17, who holds a dual appointment as assistant professor in the Department of Nuclear Science and Engineering (NSE) and Charles Stark Draper Career Development Professor of Aeronautics and Astronautics (AeroAstro), is researching ways to address one of the principal heat transfer challenges in such deployments of nuclear power in space: a lack of atmosphere. He is also working on technologies to enable the deployment of aerospace reactors, observe their operational signatures from long range, and create the technical basis for future arms-control agreements limiting the weaponization of these systems.

Thermal management in space

The next generation of space nuclear reactors demand considerably higher power than past missions, requiring the conversion of heat to power, typically using a Brayton or Rankine cycle. Such conversions deliver efficiencies of around 30-40 percent in terrestrial applications — and a lot of heat that needs to be removed. Because there is no atmosphere to rely on for convection, engineers use radiators instead. But traditional solid metal or composite radiator panels are mass-intensive, a significant impediment for space applications.

One of Hecla’s research projects at MIT addresses the limitations of an alternate solution: the liquid droplet radiator (LDR). First proposed by researchers at the University of Washington in the late 1970s, the LDR could be just as effective as conventional radiators, especially because their considerably lower weight is an advantage, helping radiators constitute just a fraction of the spacecraft’s mass.

In place of solid panels, LDRs use sheets of liquid droplets to radiate heat away. The LDR works by spraying coolant from an emitter to a collector as sheets of tiny droplets. As these droplets travel through space, they radiate and cool. They then impact a collector, which then pumps the coolant back to the heat source in a cyclical operation.

The principle sounds straightforward, but droplet retrieval is often complicated by drifts introduced by charging, splashing at the collector, and emitter misalignment. Engineers can’t afford to lose too many of them — acceptable loss rates could be one droplet in every million for a 10-year mission — because that will increase the coolant load that will need to be transported to space. (The rate might be small, but the radiator cycles the coolant every few seconds, meaning even a minuscule loss rate adds up over time.) Hecla is investigating a variety of collection techniques including electromagnetic collectors that can improve retrieval efficiency, and even allow maneuverability while the radiator is in operation.

If successful, this could reduce the mass of the cooling system for a future nuclear electric power space reactor by an order of magnitude. The work will vastly improve the potential of nuclear power in space and potentially expand its utility to include low-Earth orbit projects as well.

An early interest in radiation detection — at MIT

With his appointment as an assistant professor, Hecla’s career has come full circle since his undergraduate days as a student at NSE.

Early on, he was especially interested in radiation detection techniques because of their use in many practical applications.

That specific interest was seeded during a formative childhood trip to New Mexico, which included a visit to a shop selling rocks and minerals. Hecla remembers being fascinated by the goods, which included uranium ore. He was hooked. Hecla researched the field and bought and built a do-it-yourself Geiger counter. “Throughout high school I was buying and repairing radiation equipment in my home. I collected high-voltage equipment, built a homemade X-ray system, and it was all downhill from there,” Hecla says.

That passion for measuring radiation spilled over to MIT. One of Hecla’s undergraduate research projects involved developing a prototype of a photocathode-driven X-ray unit, in collaboration with Massachusetts General Hospital. The research team he worked with formed a startup called Advanced Tomography Systems. As part of another undergraduate research opportunities program (UROP), Hecla worked with Areg Danagoulian, associate professor at NSE, on a novel approach for treaty verification.

A visit to Chernobyl

While at MIT, another formative event would cement Hecla’s deep commitment to measuring radiation: a friend, Carl Willis, now a professor at the University of New Mexico, invited Hecla to travel to Chernobyl. “It was to better understand the realities of living and working in a contaminated environment,” Hecla says.

The experience shook him — and he visited several times more during his undergraduate years. Since 2017, Hecla has served as scientific adviser to Clean Futures Fund, a U.S.-based nonprofit that ensures activities in the exclusion zone and at the power plant are as well supplied as they can be, providing basic essentials, personal protective equipment, and detection equipment.

“Chernobyl has a lot to teach us, both about the necessity of a dedication to safety in nuclear power and about managing the consequences of radiation release,” Hecla says. “It’s an enormously challenging environment where a small amount of well-informed work can have an outsized impact.”

Chernobyl also provided the context for Hecla’s graduate studies at the University of California at Berkeley, where he studied radiation imaging techniques. For his master’s, the goal was to use a free-moving radiation detector to reconstruct a three-dimensional image that would inform workers about radiation intensity so they could be judicious about exposure.

Another area of radiation detection, to detect antineutrinos as an indicator of nuclear fission, anchored his doctoral research — Hecla’s PhD dissertation was titled “Optical Diagnostics for Antineutrino Detector Media” — advised by Kai Vetter. The goal was to use the particles as an essentially unshieldable signature of reactor operation.

Guarding against the misuse of nuclear energy

As Hecla’s graduate research showed, using radiation signatures is an effective way to track when nuclear energy is being misused.

Hecla’s work on aerospace applications of nuclear power extends to both improving the viability of space reactors and improving our ability to monitor orbital and sub-orbital applications of nuclear energy.

His current research focuses on understanding and tracking air-breathing nuclear propulsion, which is a technique in which heat from a reactor is used for propulsion. Hecla became especially interested in the project when, in 2018, the Russian government announced that they were developing a nuclear-powered, nuclear-armed missile, the 9M730 Burevestnik, that would pose a direct security threat to Europe.

The missile was said to be powered by an air-breathing nuclear engine, enabling extreme duration flight. In 2019, a mishap with the system killed at least five scientists, and generated a plume of radionuclides that drifted across the White Sea, indirectly confirming that the Russians were serious about developing the missile.

Between 2023-25, as a Stanton Postdoctoral Fellow at MIT’s Laboratory for Nuclear Security and Policy, working with R. Scott Kemp, Hecla developed techniques through which he could understand the performance envelope and flight signatures of the nuclear propulsion system in the missile. That information could be used to limit or ban use of such military systems, or contribute to new countermeasures.

Along with colleagues at MIT, Hecla is devising techniques to detect the presence of nuclear weapons by studying the gamma-induced neutral field from satellites, which can share information about the presence of fissile material. Such a system is especially relevant given the Kosmos 2553, a satellite launched by Russia in 2022 that is believed by American intelligence to contain components of a nuclear weapon. 

The threat of nuclear weapons in space is immense. “Someone may find it advantageous to destroy most assets in low and medium Earth orbit if it gives them an edge during combat,” Hecla says. As part of this work, researchers are looking to develop a highly sensitive compact neutron telescope to detect and locate the source of neutrons as a way to detect nuclear activity.

Most of Hecla’s work, including the space thermal management projects, sits at the intersection of nuclear and the aerospace or aeronautical fields.

Hecla is delighted to be back at MIT where it all started. “We have the right combination of talent here that is strong and growing,” he says. “And between world-class facilities like the MITR-II (MIT Nuclear Research Reactor), the PSFC (Plasma Science Fusion Center), the people, this is absolutely the best place for research,” Hecla says.

The MIT Global Seed Funds (GSF) are designed to do more than launch research projects — they build international collaborations that make ambitious science possible. By supporting early-stage collaborations with researchers around the world, the program helps faculty test new ideas, exchange expertise, and lay the groundwork for discoveries that often grow far beyond the initial award. 

Matt Shoulders, professor of chemistry and department head, has experienced that impact firsthand. Since his first GSF grant in 2013, the collaborations these grants enabled have led to major research programs in protein engineering, photosynthesis, collagen biology, and gene-editing technologies, as well as subsequent funding. 

The GSF help Institute faculty and principle investigators create exciting new connections with colleagues abroad. GSF supports early-stage, open and publishable research collaborations with researchers at academic universities and public research institutions around the world. Administered by the Center for International Studies, GSF has awarded more than $30 million to over 1,300 faculty research projects since its inception in 2008. The current call for proposals from MIT faculty and researchers with principal investigator status is open until Dec. 3. 

Q: What makes the Global Seed Funds unique?

A: This has to be one of the standout ways to develop international collaborations that any school has anywhere. It's an extraordinary way to build serious relationships with people who have science that you can work on together, get to synergistic and exciting outcomes, and start projects that you could never get going otherwise.

I've found it to be an enormously valuable bridge-building mechanism for creating opportunities to work with other groups.

I started as an assistant professor in 2012 and received my first Global Seed Funds grant in 2013, probably within eight months of starting. That opportunity let me get to know an expert whose field of research was exactly what we needed. We published only one paper together, but that collaboration led directly to my first major NIH [National Institutes of Health] funding — a Director's New Innovator Award that brought nearly more than $2 million to the lab. It became the basis of what's still one of our lab's major research projects.

We never would have built that relationship otherwise. We visited their lab, they visited ours, our students exchanged visits, and together we built a whole platform for protein engineering that we still use all the time. Fourteen years later, that first GSF grant is still shaping the work we do.

That story has repeated itself over and over again. The collaborations have led us in lots of directions and created opportunities for different science along the way. Looking back, I think the GSF funding we've received can be directly connected to roughly $18 to $20 million in outside research funding. Of course, it wasn't only the GSF grants, but without them much of that later funding probably wouldn't have happened.

Q: Can you share a few examples of research that grew out of those collaborations?

A: One project is about engineering photosynthesis. The world needs more food, the climate is warming, fertilizer is becoming harder to obtain, water is increasingly scarce, and yet we need to grow more crops than ever. We need step-function improvements in agriculture, not just gradual ones.

We've focused on an enzyme called Rubisco, which takes carbon dioxide from the air and turns it into the sugars that build plants — and ultimately, all of us. Improving Rubisco has been a dream in agriculture for decades, but it's also been viewed as almost impossible.

One of the first pieces of funding we received for this project was a Global Seed Funds grant that allowed us to build a collaboration with a lab in Australia. They had technical assay expertise that existed in only a handful of laboratories worldwide. They came here, we went there, and we brought those capabilities to MIT. Now MIT is one of the few places that can perform those assays, and they've become part of the engine behind our entire Rubisco project.

That collaboration has since led to major foundation funding, publications, patents, and a great deal of exciting science. Whether we ultimately succeed in making Rubisco in a plant significantly better or not, we've already learned an enormous amount.

Another example involves collagen, the most abundant protein in the human body. There are dozens of diseases caused by collagen dysfunction, and we still can't treat them.

I met a researcher from Australia at a conference, and we realized our expertise complemented each other. The Global Seed Funds gave us the opportunity to build that collaboration. Since then, we received funding from the Mathers Foundation and multiple NIH grants. Today we've developed cartilage organoids that model these diseases, allowing us to study them and screen potential drug therapies. That entire research program exists because we were able to build that partnership.

The third example goes back to my very first Global Seed Funds grant. We wanted to develop a new way of doing directed evolution inside human cells, rather than bacteria. We had the idea, but we didn't have the adenovirus expertise to make it work.

That GSF-enabled collaboration gave us exactly the knowledge we needed. We recently published the first paper demonstrating CRISPR-Cas9 evolution inside human cells using the resulting platform. That work traces directly back to the GSF grant we received in 2013.

Q: How have MIT students benefited from these collaborations?

A: Most of the projects have involved graduate students, and they've really been the biggest beneficiaries. The collaborations often become the basis for their papers and dissertations.

When we've had the right undergraduate with the right interests, it's also been tremendously valuable, because they can spend an entire summer abroad supported by MISTI [MIT International Science and Technology Initiatives]. Michelle Huang is probably the flagship example. She spent several months in Australia, learning techniques that she brought back to MIT. She's a superstar who has now earned her PhD from Stanford, supported by an NSF [National Science Foundation] Graduate Fellowship awarded during her senior year at MIT.

Another great example is Julie McDonald, a superb MIT biology graduate student who worked on our Rubisco collaboration in Australia. She helped get that project going and has been tremendously successful. She's now a postdoctoral researcher at Rockefeller.

For our graduate students, these collaborations have been scientifically invaluable because they directly led to publications and launched major parts of their research careers.

Q: Have there been benefits beyond the research itself?

A: There are definitely personal benefits. Both the students and I have had opportunities to spend time in countries we otherwise wouldn't have visited. We've been exposed to different cultures and different ways of approaching science. India was a particularly memorable experience for me, in that respect.

I also think building partnerships with researchers in other countries has value beyond any individual project. On the scale of the Global Seed Funds program as a whole, strengthening those international relationships really matters.

Q: What would you tell other faculty members who are considering applying for Global Seed Funds?

A: If you've really got a plan and you're committed to building the collaboration, you should apply. The program can truly make the difference between an idea and a lasting research partnership. The resulting support can turn those partnerships — and the ideas behind them — into reality.

While rising global prosperity increases energy demand and resulting greenhouse gas emissions, promising low-carbon technologies offer reason for optimism. 

Eric Toone, Breakthrough Energy’s chief technical officer, gave this assessment in his keynote address at the recent Energizing@MIT, the annual research conference of the MIT Energy Initiative (MITEI).

“Energy is how we make things … build and heat our homes … grow and prepare our food. So, energy is prosperity,” said Toone.

Toone oversees the technological roadmap and evaluation at Breakthrough, which is a network of organizations founded by Microsoft’s Bill Gates to accelerate clean energy innovation. Toone also serves as a managing partner of Breakthrough Energy Ventures, the venture capital arm of Breakthrough Energy.

“This correlation between energy use and prosperity persists across a huge group of countries, and importantly, there are no high-wealth/low energy-use countries,” said Toone. Worldwide income parallels electricity consumption as they both climb, according to data shared by Toone. And in the past five decades, the middle class has been expanding worldwide, so prosperity has been increasing. 

Given that 80 percent of global energy use is energy from fossil fuels and 35 percent of electricity comes from coal, that prosperity results in emissions, declared Toone. Moreover, a rise in prosperity induces a set of behaviors that contribute to the production of emissions. We build more, drive more, eat more, and so on. Still, we need to decarbonize, and we know how to do it, Toone said.

“Of course we can decarbonize. It’s not hard to do. The question is: How much will it cost?” One approach is to think about how much it would cost to replace each of our business-as-usual products with equivalent zero-carbon products. Considering all products worldwide, and how much extra we’d pay per ton of carbon eliminated, we can calculate the total cost to the global economy to decarbonize.

But, he said, what it would cost is only half the question. The other half is, what are people willing to pay? While wealthy people in the developed world will say some percent, observations of their behavior — “when nobody’s looking” — show that they won’t pay anything at all. Toone added, “And that’s in the OECD world, where we can actually afford to do it!” OECD is the Organization for Economic Cooperation and Development, a world trade organization.

Breakthrough Energy and its collaborators enlisted artificial intelligence to analyze what it would cost to decarbonize the global economy, and AI estimated it would cost about $7.5 trillion. While that’s a big number, global GDP is about $130 trillion, so it’s about 5 percent of global GDP. But Toone also noted that people — even wealthy people — aren’t willing to pay. So the challenge is figuring out how to do it with zero “green premium,” that is, with no extra charge to use a zero-carbon product.

Breakthrough Energy was created in 2015 to do exactly that. Breakthrough started with a $1 billion venture fund, then raised about $5 billion more. Breakthrough has deployed about $3 billion of that into about 140 companies. Toone described the technologies of some of the companies Breakthrough Energy Ventures has invested in to demonstrate the difference they are making.

• Luxwall has developed and is now manufacturing novel windows that are as insulating as walls. The windows are sized to fit into conventional frames. One commercial building recorded a 30 percent energy savings as a result of installing the new windows. Toone predicted that this product would be the “new LED bulb. We all swapped out incandescent for LED bulbs because the energy saving was so big and so obvious. This is what's next.”
• Brimstone is aiming to reduce the 8 percent of emissions that come from making cement. And as the emerging economies come along, they do a lot of building. So, finding a way to decarbonize cement is critical. The company is mining a different source of critical raw materials that contains a lower concentration of the conventional cement feedstock used today, but also yields valuable metals, including aluminum.
• Ecocem is investigating a recent discovery as it also seeks to reduce emissions related to making cement. Controlling the particle size of the cement enables much more effective packing, thus reducing the total amount of cement needed.
• CarbonCure Technologies is adding carbon dioxide to cement as it’s being poured to yield cement that’s harder, resulting in a 10 percent reduction in the total amount of cement that’s needed.

Another challenge in the energy transition is how we make and distribute electricity, and how much that costs. Solar and wind are some of the cheapest sources today, but both are intermittent. Breakthrough companies are addressing two ways to solve that problem: First, by moving the electricity from where it’s abundant to places where it's needed; and second, by storing it when it’s abundant so that it's available when it’s needed. Here are some examples:

• TS Conductor is building transmission wires with a solid core that keeps them from sagging, touching vegetation, and potentially starting fires. “That means I can put up to three times as much energy down an existing transmission corridor,” said Toone.
• VEIR, a company founded by a MITEI Energy Scholar, is using superconducting cables that can transmit 10 times as much power down an existing corridor.
• Form Energy, a spinout from MIT, is developing iron-air batteries that can store power for as long as a month or even a couple of months.

The other thing we could do would be to put our solar collectors where there isn’t an intermittency — for example, space. “If I go to space, the sun always shines,” said Toone. The idea of space-based solar has been around for a long time. The challenge is the cost, particularly the cost of “getting stuff into space,” he said. A Breakthrough company called Stoke Space Technologies is building reusable rockets to solve that problem.

Geothermal energy — that is, the energy that's contained as heat in the Earth — is another option for generating electricity. Fervo Energy, another Breakthrough portfolio company, is drawing on advances in drilling technology that came about for fracking. The company is now building the world's first large-scale geothermal power plant in southwest Utah, the Cape Station power plant. The plant is already generating 500 megawatts — continuously, regardless of the weather at the Earth’s surface.

Another source of always-available power is fusion energy, but the main challenge with fusion is containing materials at temperatures equivalent to temperatures in the core of the sun. A physical vessel is not an option. Containing them in a magnetic field is the approach that’s the furthest along. Commonwealth Fusion Systems, an MIT startup and Breakthrough-supported company, is a leader in this approach. There, researchers have created high-temperature superconducting tapes that generate the enormous magnetic fields needed to contain those high-temperature materials.

The potential of all of these technologies, if successful, could reduce the cost to decarbonize the world economy to $3.5 trillion — half as much as the initial estimate, Toone said.

The $5 billion that Breakthrough is investing is only a fraction of the total global investment in clean energy, Toone said. About $220 billion has been invested in clean technology during that same period of time, and venture capitalists have invested about $2.5 trillion, he said. 

The biggest challenge that Toone sees is that the world has an enormous existing infrastructure that has to be turned over. So, it's critical that those new technologies be deployed in the developing and emerging world so there’s not another enormous infrastructure that has to be replaced.

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

Three technologies developed by MIT Lincoln Laboratory are among recipients of 2026 R&D 100 Awards. The awards recognize the 100 most significant innovations that have transitioned to use or been made available for license in the past year.

R&D World, an online publication, manages the awards program, which has run annually since 1963. The worldwide competition is judged by an independent panel of science and technology experts and industry professionals.

The laboratory's winners overcome obstacles to improving the security and capability of systems. One technology is an operating system designed to protect embedded computers from cyberattacks. Another technology is a platform for integrating superconductor and semiconductor chips in a wafer-scale system for quantum or high-performance computing. The final winner is an algorithm that removes interference 5G base stations incur on co-channel radio frequency (RF) devices. 

"These awarded technologies address critical problems in cybersecurity, quantum computing, and communications. We're proud of our dedicated staff who developed and transitioned these technologies for the nation's advantage," says Melissa Choi, Lincoln Laboratory director. 

Since 2010, Lincoln Laboratory has received 111 R&D 100 Awards recognizing the transfer of its unclassified technologies to industry and government. Each year, many technology transitions also occur for classified projects. This transfer of technology is central to the laboratory's role as a Department of War (DoW) federally funded research and development center.

Secure-by-design operating system

Computers embedded in deployed systems — such as satellites, drones, or radars — keep missions intact and people safe. The operating systems (OS) inside these embedded computers, however, are prime targets for cyberattacks. They are large, complex, and built on decades of legacy designs predating modern cyber threats. Increasingly, artificial intelligence is helping adversaries exploit OS vulnerabilities. 

To protect embedded systems, Lincoln Laboratory developed an inherently more secure OS called Magnetite. "If Windows and Linux are the familiar workhorses of general-purpose computing, Magnetite is built for the different realities of embedded systems, for which real-time performance matters, resources are tight, and system failure has physical consequences," says Juliana Furgala, principal investigator in the Secure Resilient Systems and Technology Group. 

A few components assure Magnetite's security. It is built on top of seL4, a formally verified microkernel (i.e., software core). Formal verification means that mathematical proofs have guaranteed the microkernel's code behaves exactly as intended, eliminating a source of security flaws. On top of this foundation, Magnetite’s OS services — such networking, storage, and communication — are written in Rust. This programming language prevents common memory-corruption bugs that hackers often exploit. Finally, Magnetite configures its services as isolated processes under the principle of "least privilege." This configuration prevents different parts of an embedded system from accessing unnecessary data. If a cyberattack compromises one service, that component can be locked down while critical functions of the system continue running.

Free for government use and approved partners, Magnetite is now being integrated into space and flight platforms for the DoW. Looking ahead, the team sees potential to extend the platform to other embedded domains, such as control systems for critical infrastructure. Because Magnetite offers a common, reusable foundation for building specialized mission capabilities, the team is also collaborating with other technical groups across the laboratory to offer Magnetite as an alternative to costly, proprietary operating systems currently in use.

In addition to earning an R&D 100 Award, Magnetite was selected as MIT Lincoln Laboratory's Best Invention of 2025.

Wafer-scale platform for quantum systems

Modern computing systems are increasingly limited not by transistor performance, but by interconnection bottlenecks. Electrical pathways linking densely packed chips are taking up space, slowing data transfer, and generating heat. The problem is especially pronounced in quantum systems, which require bulky coaxial cables to bridge superconducting chips (which must be cryogenically cooled to achieve zero electrical resistance) with room-temperature control electronics.

The Quantum Microsystems platform addresses this interconnection challenge, integrating hundreds of superconductor and semiconductor chips into a single, wafer-scale system. The platform combines several innovations. First, an AI decision-tree approach determines the optimal way to route connections between chips across a wafer. After the wafer is tested for defects, a digital lithography process "draws" custom wiring layers between chip locations and around defective circuit components to enable the highest yield possible from the wafer. Chips are then connected using microbump (tiny "bumps" of conductive material used to connect stacked chips) or bumpless techniques that place them extremely close together; a superconducting fabric extends these connections across the wafer to create a unified computing platform, carrying signals at near light speed. Coaxial cables are replaced with integrated, lithographically defined flexible interconnects, significantly reducing cryogenic wiring overhead and system complexity. 

The Quantum Microsystems platform is designed to be reworkable, so chiplets can be replaced or upgraded without rebuilding the entire system. The platform enables the design and fabrication of systems ranging from individual and stitched‑reticle architectures to mix‑and‑match multireticle configurations and fully integrated wafer‑scale platforms. The platform's AI engine continuously adapts to process variations and design changes to enable efficient, data-driven microsystem manufacturing. 

"By shifting from chip-level scaling to AI-optimized system-level integration, the platform establishes a new paradigm for building ultra-efficient, large-scale computing infrastructure for applications in quantum computing, AI, and high-performance computing," says Rabindra Das, principal investigator in the Quantum-Enabled Computation Group. The technology is designed for transition to government and commercial foundries and may be available for licensing and collaborative development through Lincoln Laboratory’s technology transfer process.

Algorithm to cancel 5G interference 

As 5G cellular networks have expanded, so has an unintended source of interference. In certain environments, powerful signals emitting from 5G base stations can encroach into frequency bands used by nearby non-5G RF antennas, burying signals for such systems as military radars, aircraft altimeters, satellite ground stations, or radio telescopes. 

To cancel this interference, Lincoln Laboratory researchers developed an algorithm that isolates and removes the 5G signal from the affected antenna's signal. Because 5G networks adhere to a published international standard, the canceler can decode the structure of a nearby base station's transmission — and estimate distortion caused by multipath scattering in the environment — well enough to reconstruct a copy of the interfering signal. That reconstructed copy is then subtracted from the incoming data, leaving a clean signal.

Unlike traditional interference cancellation approaches like beamforming, the technique does not depend on spatial separation of interfering and desired signals. Moreover, because it removes interference on each individual antenna channel of the receiver, it preserves the antenna resources that may be needed to handle other types of interference, such as jamming.

The team validated the algorithm using signals captured over the air from a Nokia 5G base station at Hill Air Force Base. The tests recovered a test radar signal buried by the cellular interference. The signal was restored to within two decibels of its original signal-to-noise ratio, translating to 89 percent preservation of the radar's interference-free detection range. 

"While 5G waveforms exhibit complex structure, their generation is governed by global standards that allow our cancellation methodology to generalize across base station radio heads made by a diverse set of vendors," says Binoy Kurien, principal investigator in the Tactical Edge Communications Group. 

The DoW is now evaluating the technology for use on multiple platforms. Because the core structure of the 5G waveform is expected to carry into future cellular generations, this approach may also extend to interference from 6G networks as they emerge.

Penlink’s Webloc is an application that locates people using information from the weather, gaming, email, and religious apps on their phone. It’s sold to the police.

The tool collects vast quantities of location data from smartphones via commonly used weather, gaming, email and religious apps. It can build a detailed picture of where a smartphone has been over the past 12 months, and update the data hourly, showing the movements of people on a near real-time basis. Law enforcement officers can use Webloc to zero in on a map and look at all phones that were present at a specific place and time, analyzing “billions of location data points,” according to company documents reviewed by Bloomberg...

Accurate, up-to-date climate projections are essential for planning decisions that enhance the resilience of power grids, protect public health, maximize agricultural yields, and more.

Now, MIT researchers have developed a technique that streamlines the production of fast, trustworthy, and adaptable artificial intelligence models for climate prediction.

These specialized AI models, known as climate emulators, can rapidly simulate the effects of human activities onto a future climate, predicting impacts on temperatures or sea level, for example. 

Because emulators run in a matter of seconds, scientists use them to efficiently test thousands of varied climate scenarios. This provides more reliable estimates and can identify unexpected outcomes of regional interventions.

The MIT researchers developed a training technique that boosts the performance and adaptability of a climate emulator while using only a fraction of the data typically required. This significantly cuts production time for these practical scientific tools.

They show that an emulator trained with a much smaller amount of carefully selected synthetic data accurately predicts outcomes of scenarios it did not see during training, while traditional emulators failed to adapt to new situations.

“Climate emulators are especially well-suited for modeling policy-relevant scenarios. By using our method to generate optimized training data for an emulator, we can speed up the production of much more reliable emulators, so we can better trust their outputs for making policy decisions,” says Christoher Womack PhD ’26, who is now a postdoc at ETH Zurich and lead author of a paper on this technique.

He is joined on the paper by Shahine Bouabid, a postdoc in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS); Andrei Sokolov, a research scientist in MIT’s Center for Sustainability Science and Strategy (CS3); Popat Salunke, a postdoc in CS3; Glenn Flierl, a professor of oceanography in EAPS; Sebastian D. Eastham, an associate professor at Imperial College London; and senior author Noelle E. Selin, a professor in the MIT Institute for Data, Systems, and Society (IDSS) and EAPS, and director of CS3. The research appears this week in the Proceedings of the National Academy of Sciences. 

Speedier surrogates

Climate scientists typically use earth system models (ESMs) to make long-term projections about how global temperature or precipitation could change in response to varying greenhouse gas emissions.

These enormous, physics-based models capture the major components and interactions of the earth’s climate in extreme detail. While powerful, ESMs can take months to run on a cluster of supercomputers — and that’s only to project one possible future climate scenario. 

Selin and her collaborators design climate emulators, which are fast AI surrogate models that mimic the behavior of full-scale ESMs. Emulators help scientists test many scenarios, make more accurate forecasts, and explore specific pollutants and regional effects.

“Climate emulators can run in just a few seconds, so it really expands what we can do in terms of exploring these future climate scenarios,” Womack says.

When scientists design a climate emulator, they often train the AI model using a set of six to eight future scenarios based on realistic future trajectories of emissions. They must run each scenario through a full-scale ESM, making these datasets costly and time-consuming to generate. 

The MIT researchers recently found that using realistic future scenarios as training data can lead to underperforming emulators that fail to predict unfamiliar or novel scenarios. 

In this work, they developed an algorithm that generates a synthetic dataset of optimized climate scenarios tailored to boost one emulator’s predictive performance.

“Our algorithm might generate some weird scenarios which look nothing like a realistic future, but we don’t care so much if the training scenarios are realistic. We are just trying to make the best possible dataset to train an emulator,” Womack says.

Importantly, rather than needing to run a full ESM six or eight times to generate training data, a user would only need to run that model once.

Tuning the training data 

The researchers begin by generating an arbitrary climate scenario using a fast and simple climate model. They test how well their emulator can reproduce the results of that ESM, which gives them a measure of error.

Because of the programming technique they used to build the training pipeline, they can iteratively update the training data to increase the emulator’s predictive skill. For instance, the algorithm could increase CO2 emissions dramatically in the year 2050 or rapidly decrease methane emissions in 2037.

Then the optimization updates the training data until the emulator’s error level reaches a certain threshold.

In the end, rather than resembling realistic scenarios in which emissions follow smooth trajectories, the synthetic data contain rapid variations.

“By introducing these high-frequency wiggles and oscillations, our data enable the emulator to extract a lot more information about the overall system,” Womack explains.

Their training algorithm outputs these emissions scenarios, which they can use to train an emulator that is more accurate and adaptable.

“In the future, we are definitely going to see climates and scenarios we haven’t seen before. If we can simulate these different scenarios more robustly, we can provide more reliable information to the stakeholders who need it,” Selin says.

For instance, an emulator trained with their technique can capture the effects of local pollutant control policies that reduce one pollutant while other emissions continue rising. The traditional emulators they evaluated failed this test.  

In the long run, Womack hopes this work improves the usefulness of fast and adaptable climate emulators for policy formulation. 

“This is such an important piece of the pipeline when it comes to creating useful tools that speak to stakeholder needs. Because the process of training an emulator can takes months or even years, the emulators we have are usually out-of-date. By cutting down that production time, I think this technique will be a game-changer in terms of getting updated tools into the hands of researchers and practitioners,” Selin adds.

In the future, the researchers want to test their technique using an emissions scenario generated by a full ESM. They also want to apply it to other types of climate models and explore its effectiveness in broader machine learning and computer vision applications.

This research was part of the Bringing Computation to the Climate Challenge (BC3) project and supported by Schmidt Sciences through the MIT Grand Challenges.

When we need medical care, we expect decisions about our treatment to be made by our doctors, not a tech company's algorithm. But in January, the federal government launched a program that uses AI to to evaluate Medicare beneficiaries' requests for certain procedures.

EFF sued for records on WISeR, seeking badly needed transparency about Medicare's AI experiment. In our latest EFFector newsletter, we're talking about what happens when the government outsources treatment approval to private AI companies—without sufficient transparency, testing, or legal limits.

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For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This issue covers unconstitutional digital dragnets, a post office surveillance scheme, and what EFF uncovered about Medicare's AI experiment.

Prefer to listen in? EFFector is now available on all major podcast platforms. This time we're talking to EFF's Lena Cohen about WISeR records documenting improper denials, operational chaos, and lengthy delays that have left patients waiting in pain. You can find the episode and subscribe on your podcast platform of choice:

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Order has long been the rule in the design of synthetic “metamaterials,” which have internal structures that give the whole material exceptional properties not found in nature.

When scientists engineer a metamaterial, they design from a single building block, or “unit cell,” that is then copied and pasted many times over. The result is a precise, periodic microstructure that results in improved material performance. 

Now, a team from MIT and Northeastern University has found that engineering a little disorder into a material can further enhance its “meta” features. 

In an open-access study appearing today in the journal Nature, the researchers introduce a new way to design metamaterials, inspired by the types of networks that appear in a leaf’s vasculature, the veins in a dragonfly’s wings, and the virtual connections in a social network. These new metamaterials — termed “scale-rich metamaterials” — exhibit structures of varying sizes, which sprawl and connect to form a physical network.

For decades, networks have been used as theoretical constructs to describe everything from cities to the internet to connections between neurons. The team has now applied such network designs to physical materials. They designed metamaterials made not from repeating unit cells, but from tiny support beams of varying sizes, similar to a network’s dense, web-like patterns. 

When subjected to physical tests, the new materials attained a widely controllable range of stiffness and held up against sudden impacts better than conventional, ordered metamaterials. They also were able to cushion and protect fragile objects embedded within the materials.

The team designed their materials using an algorithm which generates an optimal network of microstructures for a desired material property. The procedure can be used to design resilient, impact-absorbing materials such as helmets and armor, as well as acoustic lenses — materials whose microstructure efficiently focuses sound or vibrations.

“This is a completely new way of thinking for metamaterials,” says study author Carlos Portela, associate professor of mechanical engineering at MIT. “Unlocking this network design for materials really frees us to think of new microstructures and mechanical benefits that haven’t been possible before.” 

The study’s MIT co-authors include Andrew Yen-Jong Chen, Mohammad Charara, and Jayna Wadhwa, along with Csaba Both, Ting-Ting Gao, and Albert-László Barabási of Northeastern University, and Niek Mooij of Utrecht University in the Netherlands.

Looking beyond order

Network science is a field of research that studies complex networks, the nodes and connections within a network, and how these elements relate to each other and to the network as a whole. Network science has been used to map and characterize a wide range of systems, from networks in nature, such as food webs, evolutionary trees, and neural connections, to financial markets, cellular and social networks, and the internet. 

Albert-László Barabási, a co-author of the new study, has been at the forefront of network science. A professor of physics at Northeastern University, Barabási explores the structure of complex networks across many disciplines, studying where a network may exhibit vulnerabilities and how it could be made more robust.

Most of Barabási’s work has involved conceptual networks, or networks that are mapped onto existing systems. Recently, he wondered: Could network science be applied to build a physical material with certain desired properties that are not possible in nature? 

For this, Barabási joined efforts with Portela, whose group at MIT specializes in metamaterials. The researchers engineer materials from the ground up, with precisely patterned microstructures that enhance the material’s performance overall. Like others in the field, Portela has built metamaterials from a single unit cell, repeating this building block in patterns such as square lattice structures and honeycomb architectures.

In many cases, Portela has found that such ordered patterns do improve a material’s strength and resilience. But if these materials are punctured or cracked, the rest quickly buckles and fails. He says that’s because the internal structure is all the same; if one region of the material is deformed, the surrounding, similar unit cells will respond and deform in kind.

“We’ve spent years looking at periodic and ordered microstructures, and we’ve learned there are a lot of limitations and non-ideal responses, such as this localization effect,” Portela says. “We wanted to break free of that notion and use network science to design these materials.”

Line by line

The team developed an algorithm to virtually generate a network of beams with the constraints of a physical material. When applying network science to something like the internet, researchers haven’t had to consider physical constraints, such as limiting the number, density, or intersection of connections. In a physical material, the number and thickness of beams, and the space between them, start to matter. 

To design a physical network, the team’s algorithm starts by drawing a point at a random coordinate in a domain representing the size of the final material. The program draws a line at a random angle, which stretches through the point and crosses the entire space. The program then repeats the process with a second point, at a second random coordinate, and a line drawn through the point at another random angle, until that second line reaches a boundary, of either the whole domain, or the first drawn line. The process then repeats, again and again, drawing progressively shorter, thinner lines. The resulting material represents a dense network of intersecting beams of many varying sizes. 

Scientists can vary the type of network that the program generates, by varying the thickness of the starting line and how fast that thickness decreases with each successive line drawn. By simply tuning these two parameters, the team found they could generate a huge range of networks. They then carried out simulations to predict how tuning one or the other, or both, by varying degrees, would change a material’s stiffness and other physical properties. 

“We see we can really tune the stiffness of a material just by changing these two parameters, which dictate how these network patterns get generated,” Portela says. 

The team then fabricated physical materials with microstructures that were patterned after the networks they virtually generated. They used 3D-printing methods to deposit polymer into patterns of tiny, interconnecting beams. Each resulting sample was a large square, about the size of a credit card, with internal beams that ranged from microns to centimeters in scale. 

They put the samples through various physical tests, such as dropping a large weight onto them and squeezing them with increasing force. They recorded the materials’ response, along with samples of conventional lattice and honeycomb metamaterials. The network-inspired samples, they found, were more resilient to compression and impacts. 

“Because there is such a richness of length scales in these networked materials, if one part fails, there is a strong web of structures around it that keeps the entire material from failing catastrophically,” Portela says. “It’s more efficient at distributing damage and absorbing energy.”

As another demonstration, the researchers designed a material inspired by the density-graded microstructure of a pomelo’s thick rind. They designed the material with edges made from a denser network of beams, and showed it could cushion and protect a fragile object that was embedded at the center of the network.

“This work provides a design framework for materials whose features vary over a very wide range,” says materials expert Tian “Tim” Chen, associate professor in the Department of Aeronautics at Imperial College London, who was not involved with the study. “That may lead to a next generation of materials with unprecedented physical behavior, for any structure that must carry load, absorb energy or control vibration.”

This work was supported, in part, by the U.S. National Science Foundation’s Designing Materials to Revolutionize and Engineer our Future (DMREF) program.

It’s hard to believe how lucrative this crime can be:

According to a press release from the United States attorney’s office for the southern district of New York, three men ­ Jacob Soall, Sharon Hazan, and Guy Sheetrit ­ were arrested for using AI to alter and impersonate the voices and images of executives and attorneys at various companies in order to convince employees to send over large payments. Soall and Hazan were arrested in Spain over the summer, while Sheetrit was arrested in the United Kingdom in September. The alleged fraud took place between 2024 and 2026...

The MIT Schwarzman College of Computing welcomes seven new faculty members in core computing and shared positions to the MIT community in 2026. They’re inventing and using artificial intelligence and computing technologies in new ways to transform areas such as environmental science, quantum computing, human-AI interaction, and macroeconomics and finance.

“It’s a pleasure to welcome these outstanding faculty members to MIT, whose innovative research is leading advances in AI and computing and its impact on society,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Panasonic Professor of Electrical Engineering and Computer Science.

These hires include core faculty with appointments in the Department of Electrical Engineering and Computer Science (EECS), which reports jointly to the MIT Schwarzman College of Computing and School of Engineering, as well as shared faculty positions between the college and other departments, including Urban Studies and Planning, Mathematics, Economics; and, at MIT Sloan School of Management, the Finance Group and the Work and Organization Studies Group.

The new shared faculty include:

David Bruns-Smith, an assistant professor at the MIT Sloan School of Management, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in September. He is also a member of the Laboratory for Information and Decision Systems. His recent research has focused on debiased machine learning with applications in macroeconomics and household finance. Previously, he was a postdoc at Stanford Data Science, and he completed his PhD in computer science at the University of California at Berkeley.

Zana Buçinca, an assistant professor at the MIT Sloan School of Management, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in September. She is also a member of the Laboratory for Information and Decision Systems. Her research interests lie at the intersection of human-AI interaction and the future of work, and involve designing human-AI interaction techniques that complement people and amplify their values in AI-assisted work. She was a postdoc at Microsoft in the Office of Applied Research, and received her PhD in computer science from Harvard University.

Alexandra Cabral, an assistant professor in the Department of Urban Studies and Planning, who holds an MIT Schwarzman College of Computing shared position with the Institute for Data, Systems, and Society (IDSS), joined MIT in September. Her research focus includes low-cost sensor networks, smart cities, environmental informatics, and low-resource computing. Previously, she was a postdoc in the Ka Moamoa Lab at Georgia Tech and served as a 2025-26 NSF ICICLE Educational Fellow. She earned her PhD in computer science from Harvard University.

Alexandru Damian, an assistant professor in the Department of Mathematics, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in September. He is also a member of the Laboratory for Information and Decision Systems. His work is focused on developing a predictive, and ultimately prescriptive, theory of deep learning. He holds a BS in mathematics from Duke University and a PhD in applied and computational mathematics from Princeton University.

Lindsey Raymond PhD ’24, an assistant professor in the Department of Economics, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in July. She is also a member of LIDS. Her research involves the effects of digital technologies and artificial intelligence on labor markets, firms, and market competition. She was a postdoc in economics and computation at Microsoft, and earned her PhD from MIT Sloan.

The new core faculty are:

Jas Brooks, the KDD Career Development Professor in Communications and Technology, joined EECS as an assistant professor in September. They are also a member of the Computer Science and Artificial Intelligence Laboratory. Their work focuses on perceptual engineering: the design of computational interfaces that sense and modulate human sensory perception. Previously, they were a Rising Star in EECS, a Siebel Scholar, and a National Science Foundation Graduate Research Fellow. They hold a BS and PhD in computer science from the University of Chicago. 

Hengyun (Harry) Zhou ’16 joined EECS as an assistant professor in March. He is also a member of the Research Laboratory of Electronics. His research focuses on accelerating the development of large-scale, fault-tolerant quantum computers through full-stack architectural innovation. Before coming to MIT, he previously served as the Quantum Error Correction Architecture Lead at QuEra Computing. He earned his PhD in physics from Harvard University.

The MIT School of Engineering welcomes new faculty members across six of its academic units in 2026. This new cohort of faculty members, who have recently started their roles at MIT, conduct research across a diverse range of disciplines.

“I’m pleased to welcome these talented colleagues to our engineering faculty,” says Paula T. Hammond, dean of engineering and an Institute Professor. “They bring fresh perspectives that will open new directions for research and create opportunities for our students to learn through discovery. I look forward to supporting them as they pursue ambitious ideas, build collaborations across MIT, and help our students grow, both in the classroom and in the lab.”

Faculty with appointments in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS) report into both the School of Engineering and the MIT Stephen A. Schwarzman College of Computing.

The new engineering faculty include:

Andrew Akerson joined the Department of Mechanical Engineering as an assistant professor last January. His research interests include optimal design, computational solid mechanics, and material characterization. He previously worked as a research scientist at Meta's Reality Labs Research. Akerson received his BS and MS in aerospace engineering and mechanics from the University of Minnesota and his PhD in mechanical engineering from Caltech.

Jake Hecla ’17, the Charles Stark Draper Career Development Professor, joined the departments of Aeronautics and Astronautics and Nuclear Science and Engineering as an assistant professor in July 2026. His current research interests focus on aerospace nuclear power and propulsion, heat rejection in space, and long-standoff characterization techniques for space nuclear systems. Previously, Hecla was a Stanton Postdoctoral Fellow in the MIT Laboratory for Nuclear Security and Policy. He holds a BS in nuclear science and engineering from MIT and an MS and PhD in nuclear engineering from the University of California at Berkeley.

Zachary Manchester, the Boeing Career Development Professor, joined the Department of Aeronautics and Astronautics as an associate professor in January. He is interested in building autonomous systems that match, and eventually surpass, the levels of performance, agility, and reliability of humans. He previously taught in the Robotics Institute at Carnegie Mellon University. Manchester received his BS in applied physics, his MEng in mechanical engineering, and his PhD in aerospace engineering from Cornell University.

Eric Sun joined the Department of Biological Engineering as an assistant professor in January. He develops new computational and machine learning models to study complex biology across multiple scales with applications in aging, rejuvenation, and immune interactions. Sun is also a member of the Ragon Institute of Mass General Brigham, MIT, and Harvard. He obtained his BS in chemistry and physics and his MS in applied mathematics from Harvard University and his PhD in biomedical informatics from Stanford University.

Haoning Tang joined the Department of Mechanical Engineering as an assistant professor in January. She conducts interdisciplinary research at the intersection of nanophotonics, optical instrumentation, and device engineering, with a focus on metamaterials, quantum materials, and MEMS-enabled platforms. She was previously an adjunct professor in the electrical engineering and computer science department at the University of California at Berkeley. Tang received her BS in electrical and electronics engineering from Hong Kong University of Science and Technology and her PhD in applied physics from Harvard University.

Stylianos Varchanis, the Raymond A. (1921) and Helen E. St. Laurent Career Development Professor, joined the Department of Chemical Engineering as an assistant professor in July. He develops theoretical and computational models to understand fundamental transport processes in complex fluids, soft matter, and living systems, with relevance to materials engineering, energy, and biotechnology. Before joining MIT, Varchanis was a Flatiron Research Fellow at the Flatiron Institute of the Simons Foundation in New York. He received his BS, MS, and PhD in chemical engineering from the University of Patras in Greece.

Hengyun Zhou ’17, the Thomas D. and Virginia W. Cabot Professor, joined the Department of Electrical Engineering and Computer Science as an assistant professor in March. He is developing full-stack architectures for resource-efficient, fault-tolerant quantum computing. His work spans quantum error correction, algorithms and compilation, and hardware architecture. Zhou previously worked as a research scientist for QuEra Computing Inc. He holds a BS in physics and mathematics from MIT and a PhD in physics from Harvard.

The School of Engineering is also welcoming Edward “Jas” Bordeau Brooks, the KDD Career Development Professor in Communications and Technology, who joined the Department of EECS as an assistant professor in September 2026. For more information about Brooks, read the Schwarzman College of Computing’s recent article. 

The School of Engineering also remembers Thomas Rose, MIT’s POSCO Professor of Materials Science and a member of MIT’s Center for Materials Research in Archaeology, who joined the Department of Materials Science and Engineering’s faculty in January. Rose died on Sept. 2 following a bicycle accident. His research focused on ancient metallurgy and pyrotechnology that shaped early human societies, including how copper and its alloys were produced, transformed, and circulated. The work integrated geochemistry, mineralogy, experimental archaeology, and materials science.

A senior studying mechanical engineering and finance, Dominic Hess is an Academic All-American with a packed study schedule. But he and his teammates still make room for about 20 hours a week of football during the regular season. (“It’s a little less in the spring,” he notes.) All this time practicing, working out, and playing in games, he says, doesn’t take away from his academic experience at MIT — it amplifies and improves it. In this interview, Hess discusses his experiences on and off the field at the Institute, as well as plans for the future.

Q: How would you compare your high school football experience to your MIT one? 

A: I'm originally from a small town, Damascus, in Maryland. On my way to high school, I would drive past seven farms and a lot of cornfields. 

Football was big there, and the town was historically really good; I think we’ve won 12 state championships. I grew up playing several sports and decided to stick with football going into high school. Being in a super-competitive environment was a great experience. In my freshman year, the No. 1 player in the nation was on my team. And as a 5'10", 135-pound kid playing with a guy who was 6'6" and 280 pounds, it was crazy — a very high level of football. 

I took my academics very seriously and decided I wanted to keep playing in college, but I knew I wasn’t good enough to play at a Division I school. Luckily, I ended up here — and it has been the most impactful four years of my life.

I’ll say, coming to MIT, it was different. I tell people that I play football here, and they're like — “MIT has a football team?” 

Q: How do you strike a balance between academics and football? 

A: It definitely took some getting used to, especially during freshman year. 

Football actually helped a lot. We come to campus in August, a few weeks before classes start. As a freshman, I had no idea about what classes to take, what the numbers all mean, what the names of things are — all that stuff. But I had all this time where I could talk to my teammates about careers, different majors, and what classes to take. I learned a lot. 

Once school starts, you're taking classes that are harder than anything you've ever taken in high school, and the workload is way more than you’ve had before. It's a huge adjustment, and the balance is not easy — but you get to do it with other people. The community in an athletics team, having built-in friends who can support you, really benefits you on the academic side. 

Also, to me, it’s a grounding thing. I have all this other stuff going on — academics, clubs, my fraternity, whatever. When I walk onto the practice field, I say: “I'm just going to focus on football right now.” Those two hours are the one time of my day when I don't have to worry about anything else. It's a blessing to be there.

Q: What was it like when you became a captain of the team? 

A: I was captain of my high school team, but coming into MIT was a reset. You're back to being a freshman, back to the bottom of the lineup, just trying to earn your stripes. You need to do the work to become a good player and a good teammate. Over my first three years, I was able to improve a lot. I guess my teammates and my coaches recognized that, and I ended up getting selected. 

I think a key thing about being a leader, especially on a sports team, is asking your teammates, What are your goals? And then, just as important, What steps are you going to take to get there? I really want to win a NEWMAC [New England Women's and Men's Athletic Conference] championship, and, obviously, the guys on the team want the same thing. Taking the steps to get there is what's important. You're not going to win a championship in a random way in December, but you can do something today and keep stacking it and getting better and better. 

Being a captain on the field is a little different. It's really about keeping players motivated. I've been watching this documentary about Nick Saban, the Alabama football coach. He talks about each play being six seconds — and all you have to do is focus on this play and go all-out for six seconds. 

Q: What motivates you, on and off the field?

A: I'd say, especially at MIT, the community really motivates me. I grew up with three brothers and was very close to my parents. My dad’s in tech consulting and worked at home, so I got to spend a lot of time with him growing up. My mom quit her job to raise us. Four boys is a lot of work. (They've made it to every game this season; my mom always jokes how this season will be the end of her “sports-mom” era.)

And so, coming here, I guess I was looking for a new family — and I was fortunate to find it in football. You hear a lot of things about MIT, like how hard it is and how students can feel alone. It can obviously be hard here, so I wanted to find that community. 

I've been with the same roommate all four years; we’ve gotten very close. I’m also in a fraternity, DKE [Delta Kappa Epsilon]. Also, being a part of the Catholic community has been really important. It turns out a lot of the guys on the team are also Catholic. I didn't know I'd find that here.

Q: What’s after MIT for you?

A: I interned in New York City the past three summers and am fortunate to have a job lined up for next year. My two older brothers already live and work there, so I'm planning to move in with them. Both my parents are actually from New York, so unfortunately that means I'm also a Jets fan, and unfortunately, now, I can say it means being a Yankees fan. But the Knicks….