Mapping the ocean with autonomous sensors

In late October 2025, Tropical Storm Melissa moved through the Caribbean Sea with moderate winds that didn’t get much attention. But on Oct. 25, aided by a patch of warm ocean, the storm rapidly intensified. By the time it made landfall in Jamaica, it was one of the strongest Atlantic hurricanes on record, uprooting trees, tearing the roofs from buildings, and causing catastrophic flooding and power outages.

Ravi Pappu SM ’95, PhD ’01 blames the surprise on our inability to gather high-quality ocean data.

“The storm intensified because of a small pool of hot water in the Caribbean Ocean that fed it energy,” Pappu explains. “These pools are everywhere. They can be hundreds of kilometers wide and are literally invisible to us. If we knew about that pool, we could say very precisely how the hurricane would intensify and better deal with it.”

Pappu thinks he has a way to solve that problem. He is the founder of Apeiron Labs, a company deploying low-cost autonomous ocean sensors to capture more data, in more places, and at a lower cost than is possible today. The company’s devices roam the ocean up to a quarter mile below the surface and continuously gather data on temperature, acoustics, salinity, and more, providing a real-time look at one of the planet’s last known mysteries. He says the sensors can do for the ocean what small, modular CubeSat satellites did for Earth observation from space.

When the devices are ready to be recharged, trackers make it easy to scoop them from the ocean surface. Pappu envisions the recovery process being done by autonomous boats in the future.

“Humanity needs ocean measurements, and we need them at a scale that has never been attempted before,” Pappu says. “It’s a massively hard problem. In the last century, oceanographers resigned themselves to calling it the century of undersampling. If we are successful, we will have a much more fine-grained understanding of our oceans and how they impact humans. That’s what drives us.”

Homework

Pappu came to MIT after completing a 10-year homework assignment. It started when he was a child in India in the 1980s, when he saw a hologram on the cover of National Geographic for the first time.

“I was so taken by it that I decided I needed to learn how to make those three-dimensional images,” Pappu recalls. “I learned what I could by reading books and papers. I didn’t know who invented the hologram until I read a book about MIT’s Media Lab. The book named the person who invented the rainbow hologram, so I wrote him a letter. I didn’t know his address, so I just wrote on the envelope, ‘Steve Benton, holography researcher, MIT, USA.’”

To Pappu’s surprise, the letter reached Benton, and the former Media Lab professor even wrote back with some further topics he needed to learn about.

Pappu never forgot that. He earned a bachelor’s degree in electrical engineering in India, then earned his master’s degree at Villanova University, taking all the optics classes he could.

“Eventually, about 10 years after I saw my first hologram, I wrote to Steve and I said, ‘I did all these things you asked me, now I want to study with you,’” Pappu says. “That’s how I got into MIT.”

Pappu studied under Benton for the next three years. He also studied under Professor Neil Gershenfeld as part of his PhD. Following graduation, Pappu and four classmates started ThingMagic, a consulting company that eventually sold RFID readers. ThingMagic was acquired 2010. Pappu returned to MIT for two years as a visiting scientist around the time of the acquisition.

Following that experience, Pappu worked at In-Q-Tel, an organization that invested in ThingMagic and other companies with potential to advance national security. It was there that Pappu realized how badly the world needed large-scale, inexpensive ocean sensing.

“All of the ocean sensing up to that point, and even today, was about making a really expensive thing that cost $20 million, goes to the bottom of the ocean, and stays there for five years,” Pappu says. “We needed things that are cheap and scalable to deploy wherever you need them for as long as you want.”

Pappu officially founded Apeiron Labs in 2022.

“What we’re focused on is figuring out how the ocean works,” Pappu says. “How warm is it? What is the pH? How salty is it? These things vary from place to place every 10 kilometers or so. It varies over time, and it varies by season. If we knew the details of the ocean with the same fidelity we have for the atmosphere, we would be able to tell exactly when and where hurricanes hit. It would mean less uncertainty.”

Apeiron’s ocean-sensing devices are each 3 feet long and about 20 pounds. They’re designed to be dropped off a boat or plane with biodegradable parachutes and stay in the ocean for six months. Each device continuously sends data to the cloud, is controllable through a cloud-based ocean operating system, and is accessible on a mobile phone.

“We lower the carbon footprint and cost of gathering ocean data because everything else needs a diesel ship — and a fully crewed ship costs $100,000 a day,” Rappu says. “By the time you collect the first data in the old model, you’ve already committed to a lot of money in addition to millions of dollars for the sensors. “

The company’s devices currently have two types of sensors: one for measuring salinity, temperature, and depth, and the other that uses a hydrophone to passively listen for things like submarines and whales.

That could be used to detect the low-frequency calls and clicks of endangered whales and other fish species. Currently, fishermen must look for whales manually with spotters on ships or planes. The data could also be used to improve weather forecasts, monitor noise from offshore energy projects, and track currents.

“Currents are determined by temperature and salinity, so if there’s an oil spill, our data could help determine where that spill is going,” Pappu says. “Or if you’re a fisherman, knowing where the water changes from warm to cold, which is where the fish hang out, is very useful.”

An ocean of possibilities

Apeiron Labs has worked with government defense agencies including the U.S. Navy over the last two years. The company has also tested its devices off the coast of California and in the Boston Harbor.

“The most important thing is, when we show people our approach and what we’ve demonstrated so far, they are no longer asking, ‘Can it be done?’ they’re asking, ‘What can we do with it?’” Pappu says. “Our customers have spent decades working in the ocean and they understand how novel these capabilities are.”

Of all the possibilities, improved storm forecasting could be the one Pappu is most excited about.

“Our mission is to lower the barriers to ocean data,” Pappu says. “The ocean is a huge determinant of weather, climate, and short-term forecasting. Despite our best efforts to predict the intensity of storms, sudden changes are still the norm, and much of that comes down to a lack of understanding of our oceans. If we were monitoring these things over long periods of time and finer spatial scales, we could see these storms coming much earlier with more certainty.”

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Rethinking how our brains use categories to make sense of the world

In the new review article, “Categorization is Baked into the Brain,” cognitive scientists Earl K. Miller, Picower Professor of Neuroscience at MIT, and Lisa Feldman Barrett, university distinguished professor at Northeastern University, contend that categorization is part of a predictive process the brain uses to efficiently meet the body’s needs in a fast-paced, otherwise overwhelming sensory world. In that sense, their paper in Nature Reviews Neuroscience challenges decades of dogma about how and why the brain boils down what it sees, hears, smells, tastes, and feels.

Categories are groups of things that are similar enough to be considered functionally equivalent. When you walk through a neighborhood, you’ll naturally experience the furry, four-legged, barking animal ahead of you as a “dog.” In the classic view of cognition, your brain arrives at that categorization by soaking in lots of basic sensory features of the hound — its shape, its size, the sounds it makes, its behavior — and compares that to some prototype “dog” stored in your memory. Hundreds of milliseconds after the first sensory inputs, you can then decide what you might want to do about the dog.

Barrett and Miller argue that that’s wrong. Instead, they propose that your brain comes prepared for sensory patterns with predictions of the motor action plans that are most likely to achieve the needs and goals you bring to the moment. Those prediction signals can be described as a momentary category that the brain constructs to shape the processing of sensory signals. 

From the very start, incoming sensory signals are compressed and abstracted into that category to efficiently select the best predicted plan. If you are in an unfamiliar neighborhood your brain might construct the category “dog” to avoid being bit, resulting in: “Back away slowly while saying nice doggie.” If you are on your own block and encounter a familiar dog, your brain might construct a category to kneel and open up your arms to summon your neighbor’s adorable pup for a satisfying petting.

In either case, the category “dog” arises in the context of your needs and your prediction from a menu of learned action plans for similar situations, not from an intellectual exercise of neutrally regarding sensory inputs, comparing them to a fixed prototype, and then planning from there. If the brain really worked the classically believed way, you’d be on the back foot when the unfamiliar dog lunged at you.

“One of the main things your brain has to do is predict the world,” says Miller, a faculty member of The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “It takes several hundred milliseconds to process things, and meanwhile the world is moving on. Your brain has to anticipate things.”

The most pragmatic and efficient way to survive and thrive in such a world, Barrett says, is to have your needs and potential plans ready for the sensory situation. If your predictions are right, you’re prepared in time. If they are wrong, you adjust and learn from it.

“The stimulus, cognition, response model of the brain is wrong,” says Barrett, a faculty member in Northeastern’s Department of Psychology and co-director of the Interdisciplinary Affective Science Laboratory. “The brain prepares for a response and then perceives a stimulus. A brain is not reactive. It’s predictive. Action planning comes first. Perception comes second, as a function of the action plan.”

Anatomical and functional evidence

Throughout the review, Barrett and Miller ground the provocative proposal in copious anatomical, electrophysiological, and imaging evidence about the brain. They cite numerous experimental results that show how the brain is structured to broadcast memories to create motor plans that flow back toward signals that arrive from the body’s sensory surfaces, actively whittling them down and shaping them to give them meaning.

“The capacity to create similarities from differences — to abstract — is embedded in the architecture of the nervous system, and you can see that by looking at what is connected to what and by observing signal flow,” Barrett says.

For example, as circuits feed signals “forward” from sensory surfaces (such as the retina) to regions of the cerebral cortex that are focused on sensory processing (such as the visual cortex) toward the areas that are important for executive control (the prefrontal cortex) and control of the body (limbic cortex), information passes from many small, barely connected neurons to fewer, bigger, and more well-connected neurons. Such an architecture compresses sensory details into increasingly abstract representations that group many different features into smaller groups of similar features, and in doing so helps to select a predicted action plan from the broader category that’s already there.

“Your brain is a big funnel to take the outside world and turn it into an output,” Miller says.

Moreover, anatomical evidence shows that the neurons in the cortex maintain many more connections to provide feedback from memory that control sensory regions than to feed sensory information forward. As much as 90 percent of synapses in the visual cortex are “feedback” instead of “feedforward,” Barrett and Miller wrote. In other words, the brain is built to use memory to filter incoming sensory signals, consistent with imposing needs and goals on what would otherwise be a deluge of sights, sounds, and other sensations.

Yet another line of evidence are numerous studies from Miller’s own lab showing that at the broad network level of information flow in the cortex, the brain uses beta frequency waves that carry information about goals and plans, to constrain the expression of gamma frequency waves that carry information about specific sensory inputs.

Finally, the dominance of “feedback” over “feedforward” signals in the cortical architecture allows for the possibility that sensory signals are made meaningful in terms of predicted plans. When these plans are wrong, the resulting surprise can be integrated for future use.

“In science, there is a special name for that: learning,” Barrett says.

Implications for human thought and disease

In the end, Barrett and Miller’s proposal completely changes the idea of categorization, shifting it from being a particular intellectual skill to being a fundamental function for predictively meeting the body’s needs (or, “allostasis”).

“A category may not be a representation that an animal has, but a signal processing event than an animal does, predictively, to constrain the meaning of a high-dimensional ensemble of signals in a particular situation,” the authors wrote. “Categorization renders these signals meaningful — similar to one another and to past allostatic events — in terms of some goal or function.”

Humans, Barrett says, have a relatively massive amount of the neural network architecture to perform these pragmatic abstractions, and therefore can make categorizations that seem outright metaphorical (e.g., a functional similarity between “climbing the career ladder” and climbing a literal physical ladder).

But these processes can also go awry in disease, Barrett and Miller note. Depression can be seen as a disorder in which the brain imposes overly broad categories, such as “threat” or “criticism” on sensory episodes that don’t have to be perceived that way. By contrast, autism can manifest with features of inadequate compression of incoming sensory signals, not generalizing enough to recognize when a situation is similar enough to a prior one to select the appropriate plan.

Funding to support the paper came from the National Institutes of Health, The U.S. Army Research Institute for the Behavioral and Social Sciences, the Office of Naval Research, the Unlikely Collaborators Foundation, The Freedom Together Foundation, and The Picower Institute for Learning and Memory.

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Photonics advance could enable compact, high-performance lidar sensors

Lidar systems use pulses of infrared light to measure distance and map a 3D scene with high resolution, allowing autonomous vehicles to rapidly react to obstacles that appear in their path. But traditional lidar sensors are expensive, bulky systems with many moving parts that degrade over time, limiting how the sensors can be deployed.

A new study from MIT researchers could help to enable next-generation lidar sensors that are compact, durable, and have no moving parts. The key advance is a novel design for a silicon-photonics chip, which is a semiconductor device that manipulates light rather than electricity. 

Typically, such silicon-photonics chip-based systems have a restricted field of view, so a silicon-photonics-based lidar would not be able to scan angles in the periphery. Existing workarounds to this problem increase noise and hamper precision.

To avoid these drawbacks, the MIT researchers designed and demonstrated an array of integrated antennas that minimizes unwanted crosstalk between the antennas. Their innovation allows a lidar chip to scan a wider field of view while maintaining low-noise operation compared to other silicon-photonics-based approaches.

This novel demonstration could fuel the development of advanced lidar sensors for demanding applications like autonomous vehicle navigation, aerial surveying, and construction site monitoring.

“The functionality we demonstrated in this work solves a fundamental problem for integrated optical-phased-array technology, enabling future lidar sensors that can achieve significantly higher performance than we could demonstrate previously,” says Jelena Notaros, the Robert J. Shillman Career Development Associate Professor of Electrical Engineering and Computer Science (EECS) at MIT, a member of the Research Laboratory of Electronics, and senior author of a paper on this innovation.

She is joined on the paper by lead author and EECS graduate student Henry Crawford-Eng as well as EECS graduate students Andres Garcia Coleto, Benjamin M. Mazur, Daniel M. DeSantis, and Tal Sneh. The research appears today in Nature Communications.

Adjusting an antenna array

Many traditional lidar systems map a scene using a bulky box that spins to send pulses of light in multiple directions. The light bounces off nearby objects and returns to the sensor, providing data that are used to reconstruct the environment. 

Instead, silicon-photonics-based lidar sensors systematically scan an emitted light beam in multiple directions non-mechanically using a system called an integrated optical phased array (OPA).

Key to an OPA is an array of integrated antennas that have tiny perturbations placed periodically along their length. These corrugations allow the antenna to scatter light from an input source up and out of the photonic chip.

By adjusting the phase of light routed to each antenna, the researchers can change the angle at which the light is emitted out of the array. In this way, they can steer the beam with no moving parts.

But if engineers place the antennas too close together, the antennas will couple with each other and the light they emit will get jumbled. To avoid this, scientists typically space the antennas farther apart, but this also has downsides.

If the antennas are spaced too far apart, the array will emit multiple copies of the light beam at different angles. The researchers can only steer the primary beam so far in either direction until it is undiscernible from its neighboring copies.

“This limits our field of view, so the autonomous vehicle now only knows what is in front of it for a certain angular range,” Garcia Coleto explains.

These beam copies, known as grating lobes, can cause false positives by confusing the sensor. They also waste power.

The MIT researchers solved this problem by designing a set of reduced-crosstalk antennas that can be placed close together without causing a significant coupling effect.

In a standard OPA, all the antennas have the same design, meaning the same arrangement of corrugations. These identical antennas couple very strongly when placed close together.

To address this fundamental roadblock, the MIT researchers designed a set of three antennas with different geometries, varying the width of each antenna and the size and arrangement of corrugations. With varied geometries, each antenna has a different propagation coefficient, which determines how light travels down the antenna.

“Because the antennas have very different propagation coefficients, when we put them close together, essentially each antenna doesn’t ‘see’ the antenna next to it. Therefore, it won’t couple with its neighbor,” Garcia Coleto says. 

A photonic balancing act

But even though the antennas have different propagation coefficients, the researchers still need them to emit light in the same way. 

They achieved this by carefully designing the antennas to meet three parameters. 

First, each antenna must emit the same amount of light. Second, each antenna must emit a beam at the same angle for the same wavelength of light. Third, the emission angle must change uniformly across the array as the researchers steer it.

“We have this challenge where we require the antennas to have different geometries to reduce the crosstalk, but we need to simultaneously design the antennas to have the same emission characteristics. While it is possible to engineer this, it is extremely difficult because, typically, when antennas are designed with different geometries, they tend to behave differently,” Crawford-Eng says.

The researchers first developed the fundamental electromagnetic theory behind how radiative modes couple. They used that theory as a guide to design and simulate their antennas.

Building on those analyses, they fabricated the OPA with reduced-crosstalk antennas spaced significantly closer than they would be in a traditional OPA, then experimentally tested the system.

While a typical OPA would have coupling of about 100 percent in this experiment, their OPA reduced coupling to about 1 percent while generating a single, precise beam. Using this design, they demonstrated accurate beam steering across a wide field of view without any grating lobes. 

In the future, the researchers plan to further improve their technique to enable an even wider field of view. In addition, they are exploring a new potential solution to wide field-of-view functionality that they discovered while developing the underlying theory.

“This work addresses a longstanding challenge in integrated optical phased arrays: simultaneously achieving both a wide field of view, which requires dense antenna spacing, and high beam quality, which requires low crosstalk between neighboring antennas. The authors solve this problem with an elegant antenna design. Their innovation is an important step forward for chip-scale, solid-state beam-steering technology,” says Joyce Poon, professor of electrical and computer engineering at the University of Toronto and director of the Max Planck Institute of Microstructure Physics, who was not involved with this work.

This research was supported, in part, by the Semiconductor Research Corporation, the National Science Foundation, an MIT MathWorks Fellowship, the U.S. Department of War, and the MIT Rolf G. Locher Endowed Fellowship.

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Study: Firms often use automation to control certain workers’ wages

When we hear about automation and artificial intelligence replacing jobs, it may seem like a tsunami of technology is going to wipe out workers broadly, in the name of greater efficiency. But a study co-authored by an MIT economist shows markedly different dynamics in the U.S. since 1980. 

Rather than implement automation in pursuit of maximal productivity, firms have often used automation to replace employees who specifically receive a “wage premium,” earning higher salaries than other comparable workers. In practice, that means automation has frequently reduced the earnings of non-college-educated workers who had obtained better salaries than most employees with similar qualifications. 

This finding has at least two big implications. For one thing, automation has affected the growth in U.S. income inequality even more than many observers realize. At the same time, automation has yielded a mediocre productivity boost, plausibly due to the focus of firms on controlling wages rather than finding more tech-driven ways to enhance efficiency and long-term growth.

“There has been an inefficient targeting of automation,” says MIT’s Daron Acemoglu, co-author of a published paper detailing the study’s results. “The higher the wage of the worker in a particular industry or occupation or task, the more attractive automation becomes to firms.” In theory, he notes, firms could automate efficiently. But they have not, by emphasizing it as a tool for shedding salaries, which helps their own internal short-term numbers without building an optimal path for growth.

The study estimates that automation is responsible for 52 percent of the growth in income inequality from 1980 to 2016, and that about 10 percentage points derive specifically from firms replacing workers who had been earning a wage premium. This inefficient targeting of certain employees has offset 60-90 percent of the productivity gains from automation during the time period.

“It’s one of the possible reasons productivity improvements have been relatively muted in the U.S., despite the fact that we’ve had an amazing number of new patents, and an amazing number of new technologies,” Acemoglu says. “Then you look at the productivity statistics, and they are fairly pitiful.”

The paper, “Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity,” appears in the May print issue of the Quarterly Journal of Economics. The authors are Acemoglu, who is an Institute Professor at MIT; and Pascual Restrepo, an associate professor of economics at Yale University.

Inequality implications

Dating back to the 2010s, Acemoglu and Restrepo have combined to conduct many studies about automation and its effects on employment, wages, productivity, and firm growth. In general, their findings have suggested that the effects of automation on the workforce after 1980 are more significant than many other scholars have believed. 

To conduct the current study, the researchers used data from many sources, including U.S. Census Bureau statistics, data from the bureau’s American Community Survey, industry numbers, and more. Acemoglu and Restrepo analyzed 500 detailed demographic groups, sorted by five levels of education, as well as gender, age, and ethnic background. The study links this information to an analysis of changes in 49 U.S. industries, for a granular look at the way automation affected the workforce. 

Ultimately, the analysis allowed the scholars to estimate not just the overall amount of jobs erased due to automation, but how much of that consisted of firms very specifically trying to remove the wage premium accruing to some of their workers. 

Among other findings, the study shows that within groups of workers affected by automation, the biggest effects occur for workers in the 70th-95th percentile of the salary range, indicating that higher-earning employees bear much of the brunt of this process. 

And as the analysis indicates, about one-fifth of the overall growth in income inequality is attributable to this sole factor.

“I think that is a big number,” says Acemoglu, who shared the 2024 Nobel Prize in economic sciences with his longtime collaborators Simon Johnson of MIT and James Robinson of the University of Chicago.

He adds: “Automation, of course, is an engine of economic growth and we’re going to use it, but it does create very large inequalities between capital and labor, and between different labor groups, and hence it may have been a much bigger contributor to the increase in inequality in the United States over the last several decades.” 

The productivity puzzle

The study also illuminates a basic choice for firm managers, but one that gets overlooked. Imagine a type of automation — call-center technology, for instance — that might actually be inefficient for a business. Even so, firm managers have incentive to adopt it, reduce wages, and oversee a less productive business with increased net profits.

Writ large, some version of this seems to have been happening to the U.S. economy since 1980: Greater profitability is not the same as increased productivity.

“Those two things are different,” says Acemoglu. “You can reduce costs while reducing productivity.” 

Indeed, the current study by Acemoglu and Restrepo calls to mind an observation by the late MIT economist Robert M. Solow, who in 1987 wrote, “You can see the computer age everywhere but in the productivity statistics.” 

In that vein, Acemoglu observes, “If managers can reduce productivity by 1 percent but increase profits, many of them might be happy with that. It depends on their priorities and values. So the other important implication of our paper is that good automation at the margins is being bundled with not-so-good automation.” 

To be clear, the study does not necessarily imply that less automation is always better. Certain types of automation can boost productivity and feed a virtuous cycle in which a firm makes more money and hires more workers. 

But currently, Acemoglu believes, the complexities of automation are not yet recognized clearly enough. Perhaps seeing the broad historical pattern of U.S. automation, since 1980, will help people better grasp the tradeoffs involved — and not just economists, but firm managers, workers, and technologists. 

“The important thing is whether it becomes incorporated into people’s thinking and where we land in terms of the overall holistic assessment of automation, in terms of inequality, productivity and labor market effects,” Acemoglu says. “So we hope this study moves the dial there.”

Or, as he concludes, “We could be missing out on potentially even better productivity gains by calibrating the type and extent of automation more carefully, and in a more productivity-enhancing way. It’s all a choice, 100 percent.”

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MIT BrainTrust supports neighbors living with brain injuries

Since 1998, members of MIT’s BrainTrust club have helped Boston-area residents with brain injuries or other neurological disorders through their buddy program. The organization’s members also visit patients in nursing homes suffering from neurological issues.

BrainTrust is one of the founding chapters of Synapse National, an organization created by MIT alumna Alissa Totman ’13. Synapse’s goal is to provide social support for individuals living with brain injuries and to educate and inspire student leaders in the field of brain injury.

“Learning directly from individuals who had experienced brain injury during my time in BrainTrust gave me an appreciation of the gaps in resources and opportunities for improvement in brain injury care, which ultimately motivated me to pursue a career in brain injury medicine. My experience in BrainTrust continues to shape my approach to patient care and my professional goal of improving access to specialized care for individuals with brain injury by serving as a consulting provider in the acute care hospital, as well as by training the next generation of leaders in the field,” says Totman.

The club’s president, junior Karie Shen, who is pursuing a double major in biology (Course 7) and brain and cognitive science (Course 9), says, “BrainTrust is a student-run service organization that provides support for individuals with brain injury and other neurological disorders. I joined BrainTrust because it seemed like the perfect intersection of community service and neuroscience, and I care about these two things deeply.”

BrainTrust volunteers participate in training and then are paired with a local buddy who has experienced a brain injury. Members can also spend time on the weekends with patients in nursing homes who have dementia, Alzheimer’s disease, or who have had a stroke.

Shen, along with Elizabeth Zhang, president of the MIT Pre-Med Society, recently developed a program that allows BrainTrust members to visit patients in hospice. “It’s an experience that is deeply valuable for students. We work through a third-party organization called Compassus. Because the pairing process is HIPAA-protected, our role as BrainTrust executive members is to recruit students and connect them with the hospice volunteer coordinator for training. We also provide funding for transportation, generously supported by the UA Community Service Committee,” says Shen.

Shen, who plans to go to medical school and specialize in neurology, neuro-oncology, or geriatric medicine when she completes her degree, finds the experience rewarding, at times difficult, but also offers a glimpse into the reality of working with people with brain injuries.

“Visiting the people in hospice or a nursing home is hard. I’ve seen residents cry for no apparent reason that the nurses or I can understand. But I have also come to understand that caring for a patient’s quality of life and dignity is equally important. What I came to realize is that my presence itself mattered. That perspective has shaped how I think about the kind of physician I want to become,” says Shen.

First-year student Jordan Lacsamana heard about the club during Campus Preview Weekend and was immediately interested. Lacsamana, who will major in brain and cognitive sciences, is a volunteer in the Buddy Program and meets with her buddy at least once a month.

“I joined the club because it aligned with my interests academically, but I also wanted to support someone in the Boston community. I’m pre-med, and I’m interested in surgery, possibly neurosurgery or cardiovascular surgery. But I also think it’s nice to have someone outside of MIT to talk with. It’s great to learn more about them and have that one-on-one friendship, which really is the goal,” says Lacsamana.

Lacsamana says she enjoys spending time with Amanda, her buddy, and exploring Boston and Harvard Square, meeting for coffee or meals, and getting as much out of the relationship as Amanda does.

“I see her as a mentor because coming to Boston from Dallas was such a big change, so I’ve also been able to look to her for advice. But I think one of the great things about the program is that you get to learn more about them as an individual, instead of seeing them as just a person with an injury,” says Lacsamana.

“Many of our brain injury buddies simply enjoy being around students, staying connected to what we are learning and doing. Some have been with the club for years, even upwards of a decade, and still keep up with former student members long after they graduate. It is really wonderful to see how BrainTrust has created this web of friendships between people who would otherwise never have met,” says Shen.

“Amanda has stayed in touch with her former buddy since she graduated from MIT and is going to her wedding,” says Lacsamana. “I think it’s a testament to how amazing this program is at forming those connections.”

MIT students who seek real-world opportunities in fields such as cognitive science, health care, medicine, and cognitive/neurological prosthetics, or who want to help a local resident, can join BrainTrust. Email braintrust-exec@mit.edu for more information.

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Method for stress-testing cloud computing algorithms helps avoid network failures

Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications. 

The technique uncovers hidden blind spots that might cause a shortcut algorithm to fail unexpectedly when it is deployed. 

This new approach can identify worse-case scenarios that an engineer might miss if they use a traditional method that compares an algorithm against a set of human-designed past test cases. It is also less labor-intensive than other verification tools that require engineers to rewrite an algorithm in a complex mathematical code each time they want to test it.

Instead of needing a mathematical reformulation, the new method reads the algorithm’s source code directly and automatically searches for worse-case scenarios that lead to the highest level of underperformance.

By helping engineers quickly and easily stress-test a networking algorithm before deployment, the method could catch failure modes that might otherwise only appear in a real outage. The technique could also be used to analyze the risks of deploying AI-generated code.

“We need to have good tools to measure the worse-case scenario performance of our algorithms so we know what could happen before we put them into production. This is an easy-to-use tool that can be plugged into current systems so we can find the best algorithm to use and ensure the worse-case scenarios are identified in advance,” says Pantea Karimi, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this new technique. 

She is joined on the paper by senior authors Mohammad Alizadeh, an associate professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Behnaz Arzani, a principal researcher at Microsoft Research; along with Ryan Beckett, Siva Kesava Reddy Karkarla, and Pooria Namyar, researchers at Microsoft Research; and Santiago Segarra, a professor at Rice University. The research will be presented at the USENIX Symposium on Networked Systems Design and Implementation. 

Assessing algorithms

In large systems like cloud servers, the tried-and-true algorithms that route data from one place to another or are often too computationally intensive to run in a feasible amount of time. 

So, engineers and researchers develop suboptimal algorithms called heuristics that can run much faster. However, there could be unexpected but plausible circumstances that will cause a heuristic to underperform or fail when deployed.

A heuristic can route millions of data requests across a cloud network in seconds, but under the wrong conditions — like an unusual traffic pattern or a sudden spike in demand — the shortcut can break down in ways the designer never anticipated.

When these problems occur, a company may have no choice but to drop some requests that can’t be processed. 

The firm could also deliberately allocate more resources in advance to head-off a potential disaster, leading to higher overall costs and wasted electricity from underutilization.

“This is really bad for a company because, either way, they are going to lose a lot of money. If this particular scenario hasn’t happened before and was never tested, how would a developer know in advance before it happens?” Karimi says.

Stress-testing heuristics typically involves running a new algorithm in simulation using a set of human-designed test cases and manually comparing the performance with a previous algorithm. But this is time-consuming and can leave blind spots if an engineer doesn’t know to test for certain situations.

Alternatively, engineers could use a verification tool to evaluate the performance of their heuristic more systematically. However, these tools require the engineer to encode the algorithm into a complex, mathematical formula that can take days to flesh out. The process, which doesn’t work for every type of heuristic, must be repeated each time the engineer changes the code.

Instead, the researchers developed a more user-friendly and efficient verification tool, called MetaEase, that analyzes the heuristic’s existing implementation code directly to identify the biggest risks of deploying it.

“This would reduce the friction of using these heuristic analysis tools,” Karimi says.

She began this work during an internship at Microsoft Research, where the team previously developed MetaOpt, a heuristic analyzer that requires engineers to rewrite their algorithms as formal optimization models. MetaEase grew out of the desire to remove that barrier.

Maximizing the gap

MetaEase is driven by two key innovations. First, it uses a technique called symbolic execution to map out the different decision points in the heuristic's code. These are places where the algorithm might behave differently depending on the input.

This technique produces a set of representative starting points, each corresponding to a distinct behavior the heuristic could exhibit.

Second, from these starting points, MetaEase utilizes a guided search to systematically move toward inputs that make the heuristic perform as poorly as possible, compared to the optimal algorithm.

In machine learning, for instance, an input could be a set of user queries to an AI chatbot at a given time.

“In this way, we have exploited every possible heuristic behavior and used special techniques to move in the direction where we think the performance gap is going to increase,” Karimi explains.

In the end, MetaEase identifies the input that maximizes the performance gap between the heuristic and an optimal benchmark.

With this information, a heuristic developer could inspect the input to understand what went wrong and incorporate safeguards that will prevent the problem from happening during deployment.

In simulated experiments, MetaEase often identified inputs with larger performance gaps than traditional methods — pinpointing more catastrophic worse-case scenarios. And it did so much more efficiently. 

It was also able to analyze a recent networking heuristic that no state-of-the-art method could handle.

In the future, the researchers want to enhance MetaEase so it can process additional types of types of data, like categorical inputs. They also want to improve the scalability of their method and adapt MetaEase to evaluate more complex heuristics.

“Reasoning about the worst-case performance of deployed heuristics is a hard and longstanding problem. MetaEase makes tangible progress by analyzing heuristics directly from source code, eliminating the need for formal models that have historically limited who can use such analysis tools. I was pleasantly surprised that it handles non-convex and randomized heuristics by combining symbolic execution with gradient-based search in a practical and effective way,” says Ratul Mahajan of the University of Washington Paul G. Allen School of Computer Science and Engineering, who was not involved with this research.

This research was funded, in part, by a Microsoft Research internship and the U.S. National Science Foundation (NSF).

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MIT marks first Robert R. Taylor Day with Tuskegee University

On April 10, MIT marked its first official Robert R. Taylor Day with a program centered on the life and work of Robert Robinson Taylor (Class of 1892), the Institute’s first Black graduate and the first academically trained Black architect in the United States.

After graduating from MIT, Taylor joined Tuskegee Institute (now Tuskegee University), where he designed campus buildings, developed a curriculum, and helped establish an approach to architectural education grounded in making and community life — an orientation that continues to shape the relationship between MIT and Tuskegee today. 

Taylor returned to MIT on April 10, 1911, to speak at the 50th anniversary of the Institute’s founding — the date now observed as Robert R. Taylor Day. Reflecting on his education, he credited MIT with the “methods and plans” he carried to Tuskegee Institute. “Certainly the spirit,” he said, was found “in the love of doing things correctly, of putting logical ways of thinking into the humblest task … to build up the immediate community in which the persons live.”

One hundred fifteen years later, at the MIT Museum, students and faculty gathered around Taylor’s original thesis, “A Soldiers Home.” The work was presented alongside archival materials from Taylor’s time at MIT by Jonathan Duval, assistant curator of architecture and design. Rather than framing Taylor as a distant historical figure, the encounter with the work itself — its drawings, assumptions, and ambitions — set the terms for the day, bringing forward not only his accomplishments but the ideas and methods that continue to inform teaching and collaboration today. Attendees then gathered for a lunch-and-learn session including a hybrid panel involving MIT and Tuskegee University faculty. 

“It is so important to continue to develop the MIT-Tuskegee relationship begun by Robert R. Taylor,” says Kwesi Daniels, associate professor and head of the architecture department at Tuskegee University. “MIT students are provided an opportunity to experience the campus Taylor designed and his ethos of social architecture. For the Tuskegee students, they are able to appreciate the foundation Taylor received at MIT. The engagement epitomizes the ‘mind and hand’ philosophy of MIT and the head, hand, heart philosophy of Tuskegee.”

An ongoing exchange

Student and faculty exchanges, launched by the architecture departments at both institutions, have extended these connections in recent years. MIT students travel to Tuskegee for work in historic preservation and community engagement, sampling Daniels’ scanning and drone equipment, while Tuskegee students come to MIT to engage with digital fabrication and entrepreneurship.

For Nicholas de Monchaux, professor and head of the Department of Architecture at MIT, the relationship reflects continuity. “We are not uniting. We’re reuniting,” he says. “This year’s celebration should really be seen as the kickoff of a year of reflecting on Robert Taylor’s legacy and imagining what the day, and his legacy, can become over time.”

The day’s program — the vision for which originally emerged from a suggestion made by MIT literature professor Joshua Bennett during a meeting at Tuskegee with de Monchaux, Daniels, and Tuskegee President Mark Brown — moved into a broader effort among faculty and collaborators across architecture, history, and the humanities. As Bennett put it, “The primary aim of Robert R. Taylor Day is to lift up not only Taylor’s accomplishments, but his ideas — and the fact that his ideas live on in those of us who have inherited his legacy.”

That emphasis is also visible in the dedicated coursework and research that has accompanied the exchange since 2022. In class 4.s12 (Brick x Brick: Drawing a Particular Survey), taught by Carrie Norman, assistant professor in architecture at MIT, students document buildings on the Tuskegee campus through measured drawings and archival interpretation. Working from limited historical material, they reconstruct both form and intent.

“My role has been to structure this work pedagogically,” Norman says, “guiding students in methods of close looking, measured drawing, and archival interpretation.” She describes Taylor’s work as “an ongoing research agenda,” adding that “the broader aim is not only to deepen engagement with Taylor’s legacy, but to build on it through new forms of design research.”

Related work has contributed to a recent exhibition on the Tuskegee Chapel at the National Building Museum, curated by Helen Bechtel of the Yale School of Architecture. Building on research conducted in Norman’s course, students developed large-scale models that form part of the exhibition. New 3D fabrications use a limited set of archival materials to reconstruct the chapel originally designed by Taylor as the first electrified building in Alabama’s Macon County, which was destroyed by fire in 1957.

Looking ahead

Timothy Hyde, professor in the MIT Department of Architecture, has also been involved in the ongoing MIT–Tuskegee collaboration and in efforts to situate Taylor’s work within a broader historical context. He notes that Taylor’s training at MIT helped shape the curriculum he later developed at Tuskegee. “The other influence I would like to mention is the city of Boston itself,” Hyde adds. “Boston was a prosperous city with a wealth of civic architecture that Taylor would have seen and studied.” 

A documentary project on Taylor’s life, supported by the MIT Human Insight Collaborative and led by Hyde and historian Christopher Capozzola, senior associate dean for MIT Open Learning, is currently in development.

For some students, these encounters shape longer trajectories. As an undergraduate at Tuskegee, Myles Sampson participated in the MIT Summer Research Program (MSRP), where he began to connect architecture with a growing interest in computation. He later enrolled in MIT’s Master of Science in Architecture Studies (SMArchS) computation program, working with Professor Larry Sass, who introduced him to robotic fabrication.

“I never looked back,” Sampson says. “Without that hands-on research experience, I would never have looked past contemporary architectural practice.” He is now pursuing a doctorate in computational design at Carnegie Mellon University, focused on the role of automation in architecture and construction.

Sampson contributed significant work to the National Building Museum’s exhibition. His installation, Brick Parable, brings together historical reference and robotic construction. As de Monchaux notes, the project reflects the long arc of Taylor’s legacy: “bricks were fired by students as part of Taylor’s training program … Myles [Sampson]’s piece, made with a robotic assembly of bricks, explores the architectural idea of the chapel in contemporary form.”

For Daniels, the continued circulation of students between the two institutions remains central. Viewing Taylor’s thesis in particular offers a shared point of reference. “Whether the student is from Tuskegee or MIT, they are able to appreciate the quality of work Taylor completed as a student,” he says, “and how he built on that work by creating a college campus, beginning at age 25.”

Across these activities, Taylor’s work is approached not as a fixed legacy, but as a set of methods and commitments that continue to be tested. As Catherine Armwood, dean of Tuskegee University Robert R. Taylor School of Architecture and Construction Science, describes it: “While our students leverage [the design and entrepreneurship program] MITdesignX to turn architectural concepts into social enterprises through advanced fabrication and venture mentorship, MIT students come to Tuskegee for an immersion in historic preservation. By surveying buildings handcrafted by our founding students, they learn a legacy of self-reliance and community impact that can’t be found anywhere else,” Armwood says. “Together, we are bridging technical innovation with deep-rooted heritage to train a new generation of visionary leaders.” 

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Biologist Joey Davis explores how cells build complex structures

Ribosomes, the cellular machines that assemble proteins, are made from dozens of proteins and RNA molecules. Putting all of those pieces together is a complex puzzle — one that MIT Associate Professor Joey Davis PhD ’10 revels in trying to solve.

Understanding how these structures form and later break down could help researchers learn more about how disruptions of these fundamental processes can lead to disease. But, as Davis points out, it’s also an interesting biological question.

“Our long-term goal is to really understand how the natural world assembles these huge complexes rapidly and efficiently. It’s a fundamentally interesting question to think about how these things get put together,” he says.

His work has helped reveal that unlike building a house, which happens in a prescribed sequence of steps — pouring the foundation, building the frame, putting on the roof, then doing electrical and plumbing work — ribosomes can be assembled in a more flexible way. Cells can even skip an assembly step and then come back to it later.

“In these natural systems, it seems like the assembly pathways are much more dynamic and flexible,” he says. “It appears that evolution has selected pathways that aren’t strictly ordered in the way we would think about an assembly line, where you always put in one component, then the next, and then the next. We’re excited to understand the selective advantages of such approaches.”

A love of discovery

Davis’ interest in how things are put together developed early in life, inspired by his father, a carpenter who framed houses. During the mid-1980s, the family moved from Colorado to Southern California, where his father worked in construction during a housing boom there.

“I was always interested in building things, which I think probably came from being around my dad and other builders,” Davis says.

As an undergraduate at the University of California at Berkeley, where he majored in computer science and biological engineering, Davis’ interests turned toward smaller scales, in the realm of cells and molecules. During his junior year, he started working in the lab of chemistry professor Michael Marletta, who studies molecular-level biological interactions.

In the lab, Davis investigated how enzymes that contain heme are able to preferentially bind to either oxygen or nitric oxide, two gases that are very similar in structure. That work kindled a love of studying the natural world and pursuing discoveries in fundamental science.

“Being in the Marletta lab and seeing students and postdocs that were really passionate about these problems had a big impact on me,” Davis says. “The goal was to understand the fundamentals of how molecular discrimination works, and the idea of discovery for the sake of discovery was thrilling.”

After graduating from Berkeley, Davis spent another year working in Marletta’s lab, and then a year working odd jobs, before heading to MIT to pursue a PhD in biology. There, he worked with Professor Bob Sauer, now emeritus, who studied the relationship between protein structure and function, with a particular focus on the molecular machines that degrade or remodel proteins.

Davis’ thesis research centered on enzymes called AAA proteases, which remove damaged proteins from cellular membranes and send them to cell organelles that break them down. In addition to studying the structure and function of the proteases, Davis worked on ways to engineer them to tag specific proteins for destruction.

That work led him into synthetic biology, which he used to develop genetic parts that drive production of proteins of interest. Some of those parts ended up being used by the biotech startup Ginkgo Bioworks, where Davis took a job as a senior scientist after graduating.

Working at Ginkgo Bioworks allowed Davis to stay in Boston while his partner finished her PhD. The couple then moved back to California, where Davis worked as a postdoc at Scripps Research, which was home to one of the first direct electron detection cameras for cryo-electron microscopy (cryo-EM). These detectors allow researchers to generate structures with near atomic resolution. At Scripps, Davis began using them to study ribosomes as they were being assembled.

Peering into the ribosome

After joining the MIT faculty in 2017, Davis continued his work on ribosomes and assembled a lab group that includes students from a variety of backgrounds who work together to develop new ways to explore biological phenomena.

“I have a mix of method developers and biologists in the group, and the work from each of them informs each other,” Davis says. “My lab goes back and forth between building sets of tools to answer biological questions, and then as we’re answering those questions, it motivates the next generation of tool development.”

During ribosome assembly, RNA molecules fold themselves into the correct shapes, creating docking sites for proteins to attach. Then, more RNA molecules come in and fold themselves into the structure.

“It’s a beautifully coupled process by which the cell folds hundreds of RNA helices and binds on the order of 50 proteins, and it does it in two minutes from start to finish. E. coli does this 100,000 times per hour, and it’s amazing how rapid and efficient the process is,” Davis says.

Cryo-EM allows scientists to capture this process in minute detail. It can be used to take hundreds of thousands of two-dimensional images of ribosome samples frozen in a thin layer of ice, from different angles. Computer algorithms then piece together these images into a three-dimensional representation of the ribosome.

To gain insight into how ribosomes are assembled, researchers can stall the process at different points and then analyze the resulting structures. In 2021, Davis’s lab developed a new method called CryoDRGN, which uses neural networks to analyze cryo-EM data and generate the full ensemble of structures that were present in the sample.

This work has shown that when certain steps of ribosome assembly are blocked, many different structures result, suggesting that the assembly can occur in a variety of ways.

In future work, Davis aims to dramatically increase the throughput of cryo-EM to generate datasets of protein structures that could help improve the AI-based models that are now used to predict protein structures.

“There are still huge swaths of sequence space that these models are very poor at predicting, but if we could collect data on those sequences en masse, that could potentially serve as key training data for a next-generation protein structure prediction method that could fill out that space,” he says.

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Rett syndrome study highlights potential for personalized treatments

Although many studies approach the developmental disorder Rett syndrome as a single condition arising from general loss of function in the gene MECP2, a new study by neuroscientists in The Picower Institute for Learning and Memory at MIT shows that two different mutations of the gene caused many distinct abnormalities in lab cultures. Moreover, correcting key differences made by each mutation required different treatments.

“Individual mutations matter,” says Mriganka Sur, senior author of the new open-accdess study in Nature Communications and the Newton Professor in the Picower Institute and the Department of Brain and Cognitive Sciences. “This is an approach to personalizing treatment, even for a single-gene disorder.”

The study employed advanced 3D human brain tissue cultures called “organoids” or “minibrains” derived from skin cells or blood cells donated by Rett syndrome patients with each mutation. Lead author Tatsuya Osaki, a Picower Institute research scientist, says that the organoids’ ability to model the specific consequences of each mutation enabled him to gain mutation-specific insights that haven’t emerged in prior studies, where scientists just knocked out MECP2 overall. The organoids also provided a novel opportunity to understand how each mutation affected different cell types and their interactions.

Distinct effects

More than 800 mutations in MECP2 can cause Rett syndrome, but just eight account for more than 60 percent of cases. Sur and Osaki chose one of these, R306C, which involves a difference of just one DNA base pair (916C>T), because it represents 7-8 percent of Rett syndrome cases. The other mutation they chose, V247X, is much more rare and severe because it cuts off production of the gene’s protein product by a single DNA base deletion (705Gdel), leaving the protein not just errant, but incomplete.

In organoids cultured for three months, each mutation produced some common but also sometimes distinct consequences compared to control organoids with non-mutated MECP2. For many of their experiments, the team used “three-photon” microscopes capable of cellular-level resolution all the way through the organoids’ approximate 1 millimeter thickness, resolving both their structure (via “third-harmonic generation” imaging), and the live activity patterns of their neurons (via calcium fluorescence).

For instance, the scientists observed that the V247X organoids exhibited several structural differences from their controls — they were larger and had different thicknesses of various layers — but the R306C ones were much more like their controls. Organoids harboring either mutation exhibited less-developed axon projections from their neurons, compared to their control comparators.

Looking at properties of neural activity and connectivity in the organoids, the scientists found some similar deficits across both mutations. Both showed reduced spiking activity and synchronicity between neurons compared to in their controls.

But when the scientists looked at other properties, the organoids started to diverge from each other. In particular, an indication of the efficiency of their network structure called “small-world propensity” (SWP) was decreased in R306C organoids, and increased in V247X ones, compared to controls. This means that both mutations altered the development of typical network structures for information processing, but in different directions.

To ensure that their results were meaningful for Rett syndrome patients, the team collaborated with Charles Nelson at Boston Children’s Hospital, whose team measured EEG in several children with different Rett mutations. Although the sample was small, the researchers measured indications that the SWP property in the EEG readings was altered in the volunteers, much like in the organoids.

Finally, by labeling excitatory neurons to flash in one color and inhibitory neurons to flash in a different color, the scientists were able to see that connectivity between the different neural types differed significantly from controls in the V247X organoids.

Treatment tests

All the testing showed that each mutation caused several changes in organoid structure, activity, and connectivity, and that the deviations were often particular to the specific mutation.

To understand how these differences emerged, and how they might be corrected, Sur and Osaki’s team turned to examining how the cells in each kind of organoid might be expressing their genes differently than controls. Differences in gene expression often lead to alterations of key molecular pathways in cells that can disrupt their activity and function. Analysis with a technique called single cell RNA sequencing indeed yielded hundreds of differences in each organoid type, where some genes were expressed more than in controls while others were underexpressed.

For instance, the analyses revealed that in R306C organoids a gene called HDAC2 was overexpressed. That protein is known for repressing expression of other genes. Meanwhile, in the V247X organoids, the scientists found reduced expression of genes for some receptors of the inhibitory neurotransmitter GABA. These organoids also showed defects in the function of astrocyte cells, which support many aspects of neural function.

Organoids with either mutation also exhibited aberrations in molecular pathways that enable the development of circuit connections between neurons, called synapses.

Given the specific defects they observed, the scientists decided to treat the organoids with a drug that can inhibit HDAC2 activity and another that increases GABA’s efficacy. The HDAC2 inhibitor restored neuronal activity and SWP to normal levels in the R306C organoids, and the GABA “agonist” baclofen restored SWP to control levels in the V247X organoids.

Osaki notes each of the treatment drugs has already been studied in other disease contexts, meaning they are well-understood drugs that could be repurposed.

Now that the researchers have developed an organoid platform for dissecting individual mutations’ consequences, identifying both their roots and testing treatments, they plan to apply it to studying four more mutations, Sur says, comparing all of them against a standardized control organoid.

In addition to Sur, Osaki, and Nelson, the paper’s other authors are Chloe Delepine, Yuma Osako, Devorah Kranz, April Levin, and Michela Fagiolini.

The National Institutes of Health, a MURI grant, The Freedom Together Foundation, and the Simons Foundation provided support for the research.

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It took 40 years for technology to catch up to this zipper design

In 1985, the Innovative Design Fund placed an ad in Scientific American offering up to $10,000 to support clever prototypes for clothing, home decor, and textiles. William Freeman PhD ’92, then an electrical engineer at Polaroid and now an MIT professor, saw it and submitted a novel idea: a three-sided zipper. Instead of fastening pants, it’d be like a switch that seamlessly flips chairs, tents, and purses between soft and rigid states, making them easier to pack and put together.

Freeman’s blueprint was much like a regular zipper, except triangular. On each side, he nailed a belt to connect narrow wooden “teeth” together. A slider wrapping around the device could be moved up to fasten the three strips into place, straightening them into a triangular tube. His proposal was rejected, but Freeman patented his prototype and stored it in his garage in the hopes it might come in handy one day.

Nearly 40 years later, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers wanted to revive the project to create items with “tunable stiffness.” Prior attempts to adjust that weren’t easily reversible or required manual assembly, so CSAIL built an automated design tool and adaptable fastener called the “Y-zipper.” The scientists’ software program helps users customize three-sided zippers, which it then builds on its own in a 3D printer using plastics. These devices can be attached or embedded into camping equipment, medical gear, robots, and art installations for more convenient assembly.

“A regular zipper is great for closing up flat objects, like a jacket, but Freeman ideated something more dynamic. Using current fabrication technology, his mechanism can transform more complex items,” says MIT postdoc and CSAIL researcher Jiaji Li, who is a lead author on an open-access paper presenting the project. “We’ve developed a process that builds objects you can rapidly shift from flexible to rigid, and you can be confident they’ll work in the real world.”

Why zippers?

Users can customize how the fasteners look when they’re zipped up in CSAIL’s software program; they can select the length of each strip, as well as the direction and angle at which they’ll bend. They can also choose from one of four motion “primitives” to select how the zipper will appear when it’s zipped up: straight, bent (similar to an arch), coiled (resembling a spring), or twisted (looks like screws).

The Y-zipper that results will appear to “shape-shift” in the real world. When unzipped, it can look like a squid with three sprawling tentacles, and when you close it up, it becomes a more compact structure (like a rod, for instance). This flexibility could be useful when you’re traveling — take pitching a tent, for example. The process can take up to six minutes to do alone, but with the Y-zipper’s help, it can be done in one minute and 20 seconds. You simply attach each arm to a side of the tent, supporting the structure from the top so that the zipper seemingly pops the canopy into place. 

This seamless transition could also unlock more flexible wearables, often useful in medical scenarios. The team wrapped the Y-zipper around a wrist cast, so that a user could loosen it during the day, and zip it up at night to prevent further injuries. In turn, a seemingly stiff device can be made more comfortable, adjusting to a patient’s needs.

The system can also aid users in crafting technology that moves at the push of a button. One can attach a motor to the Y-zipper after fabrication to automate the zipping process, which helps build things like an adaptive robotic quadruped. The robot could potentially change the size of its legs, tightening up into taller limbs and unzipping when it needs to be lower to the ground. Eventually, such rapid adjustments could help the robot explore the uneven terrain of places like canyons or forests. Actuated Y-zippers can also build dynamic art installations — for example, the team created a long, winding flower that “bloomed” thanks to a static motor zipping up the device.

Mastering the material

While Li and his colleagues saw the creative potential of the Y-zipper, it wasn’t yet clear how durable it would be. Could they sustain daily use?

The team ran a series of stress tests to find out. First, they evaluated the strength and flexibility of polylactic acid (PLA) and thermoplastic polyurethane (TPU), two plastics commonly used in 3D printing. Using a machine that bent the Y-zippers down, they found that PLA could handle heavier loads, while TPU was more pliable.

In another experiment, CSAIL researchers used an actuator to continuously open and close the Y-zipper to see how long it’d take to snap. Some 18,000 cycles of zipping and unzipping later, they finally broke. Y-zipper’s secret to durability, according to 3D simulations: its elastic structure, which helps distribute the stress of heavy loads.

Despite these findings, Li envisions an even more durable three-sided zipper using stronger materials, like metal. They may also make the zippers bigger for larger-scale projects, but that’s not yet possible with their current 3D printing platform.

Jiaji also notes that some applications remain unexplored, like space exploration, wherein Y-zipper’s tentacles could be built into a spacecraft to grab nearby rock samples. Likewise, the zippers could be embedded into structures that can be assembled rapidly, helping relief workers quickly set up shelters or medical tents during natural disasters and rescues.

“Reimagining an everyday zipper to tackle 3D morphological transitions is a brilliant approach to dynamic assembly,” says Zhejiang University assistant professor Guanyun Wang, who wasn’t involved in the paper. “More importantly, it effectively bridges the gap between soft and rigid states, offering a highly scalable and innovative fabrication approach that will greatly benefit the future design of embodied intelligence.”

Li and Freeman wrote the paper with Tianjin University PhD student Xiang Chang and MIT CSAIL colleagues: PhD student Maxine Perroni-Scharf; undergraduate Dingning Cao; recent visiting researchers Mingming Li (Zhejiang University), Jeremy Mrzyglocki (Technical University of Munich), and Takumi Yamamoto (Keio University); and MIT Associate Professor Stefanie Mueller, who is a CSAIL principal investigator and senior author on the work. Their research was supported, in part, by a postdoctoral research fellowship from Zhejiang University and the MIT-GIST Program.

The researchers’ work was presented at the ACM’s ​​Computer-Human Interaction (CHI) conference on Human Factors in Computing Systems in April.

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How chromatin movement helps control gene expression

Gene expression is controlled, in part, by the interactions between genes and regulatory elements located along the genome. Those interactions depend on the ability of chromatin — a mix of DNA and proteins — to move around within a crowded space.

In a new study, MIT researchers have measured chromatin movement at timescales ranging from hundreds of microseconds to hours, allowing them to rigorously quantify those dynamics for the first time.

Their analysis revealed that chromatin can exist in two different categories: In one, chromatin moves in a constrained way that allows it to primarily contact only neighboring regions of the genome; in the other, chromatin moves more freely and contacts regions that are farther away, but only over longer timescales.

The findings offer insight into how gene expression is regulated, as well as how chromatin segments come together for other processes such as DNA repair, the researchers say.

“Because we were able to look at chromatin dynamics for the first time at these very fast timescales, and also for the first time across the full dynamic range, we were able to observe chromatin motion over a range that just wasn’t possible before,” says Anders Sejr Hansen, an associate professor of biological engineering at MIT and the senior author of the new study, which appears today in Nature Structural and Molecular Biology.

The paper’s lead authors are MIT postdoc Matteo Mazzocca, Domenic Narducci PhD ’25, and Simon Grosse-Holz PhD ’23. Jessica Matthias, chief commercial officer of Abberior Instruments, and Tatiana Karpova, manager of the National Cancer Institute Optical Microscopy Core, are also authors of the paper.

Constrained movement

In textbooks, chromatin is often depicted as a static structure within the cell nucleus, but in reality, it is constantly moving. Those movements are necessary for genes to interact with DNA regulatory sequences such as enhancers, which can be as far as 1 million base pairs away. They also ensure that when DNA breaks occur, the two ends of DNA can encounter each other to be repaired.

“Chromatin dynamics are foundational to all processes in the nucleus, and especially processes that involve two things finding each other. That’s important in DNA repair, gene regulation, recombination, or moving a particular gene to the right compartment of the nucleus,” Hansen says.

The movement of any particular location on the genome, or locus, is constrained by the fact that DNA is a polymer. After moving in any direction, a locus will be pulled back by the DNA on either side of it.

“Chromosomes are polymers. They’re held together by many nucleotides of DNA. Being part of DNA is a little bit like running while holding hands with other people. If a hundred people are holding hands and you, in the middle of the chain, try to run in one direction, you’ll get pulled back,” Hansen says.

This type of behavior is known as subdiffusive movement. Previous studies have yielded conflicting reports on how subdiffusive chromatin is, mainly because the studies were not able to track the movement over a long enough period of time to obtain statistically robust measurements. Because the movements are so small, on the order of nanometers, data needs to be obtained over long dynamic ranges — from milliseconds to hours.

In those earlier studies, researchers used imaging techniques that can track the position of a single molecule over time by comparing images frame by frame. These are useful but can only be used over a small dynamic range because of the limitations of conventional microscopy.

To generate more statistically robust data, the MIT team used MINFLUX — a super-resolution light microscopy technique that can track the movement of tiny objects such as proteins over longer periods of time. This technique was recently developed by Stefan Hell of the Max Planck Institute, a Nobel laureate for his work in super resolution microscopy. In this study, the MIT team became the first to apply this technique to chromatin in living cells.

“MINFLUX allowed us to get around the limitations of conventional microscopy, letting us measure chromatin movement faster and for a longer period of time than ever before,” Narducci says. “To our knowledge, it’s the first time this technique has been used this way.”

Using MINFLUX, the researchers were able to study cells over timescales that covered four orders of magnitude — from 200 microseconds to 10 seconds. And by combining MINFLUX with two traditional imaging techniques, they could track chromatin movement over seven orders of magnitude across time, from hundreds of microseconds to several hours.

“Region of influence”

These studies, performed across several different mouse and human cell types, allowed the researchers to identify two distinct classes of chromatin dynamics. In both classes, over short and intermediate timescales (up to 200 seconds), any given locus tends to move only within about 200 nanometers. This suggests that the subdiffusive pull is stronger than had been previously thought.

“One of the main takeaways is that you have this region of influence where a genomic locus has access to other genomic loci, and this is roughly a couple hundred nanometers large,” Grosse-Holz says. “If loci are much closer together than a couple hundred nanometers, they’re effectively in contact all the time. You get a cutoff at a couple hundred nanometers where everything within that region around a given locus can see that locus, and everything outside cannot.”

This constant contact is likely beneficial for DNA repair, as the broken strands remain in close proximity to each other. The findings also suggest that for genes and regulatory elements that are within about 100,000 base pairs, they don’t need any extra help to find each other — they will do so routinely through their normal movement.

“If they are closer than 100,000 bases, and most regulatory elements are, then those elements are going to find their target gene within a few milliseconds or a few minutes,” Mazzocca says. “These are timescales that are completely consistent with transcription.”

In the other class of chromatin dynamics that the researchers identified, chromatin is able to move over a wider range, but only at longer timescales (a few minutes to hours). This class of chromatin appeared in some types of cells but not others, for reasons that are not yet understood.

“It would be reasonable to assume that the behavior would be more or less the same in all cell types, but that’s not at all what we found,” Hansen says. “It’s very different in different cell types, with no obvious way of categorizing things.”

He adds that the strength of the subdiffusive pull that the researchers found in this study can’t be explained with existing models that have been developed to study chromatin dynamics — the Rouse model and the fractal globule model. This suggests that the models may need to incorporate factors that were previously left out, such as the interactions between chromatin and the crowded nucleoplasm it sits within.

“These findings are significant for two key reasons,” says Luca Giorgetti, a group leader at the Friedrich Miescher Institute for Biomedical Research in Switzerland, who was not involved in the study. “First, they rigorously confirm longstanding but anecdotal observations that chromatin motion is strongly subdiffusive. Second, they demonstrate that this behavior is consistent across multiple cell types and persists across all measured timescales.”

The research was funded, in part, by the National Institutes of Health, a National Science Foundation CAREER Award, a Pew-Stewart Scholar for Cancer Research Award, and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center.

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Found Industries aims to strengthen America’s industrial supply chains

Found Industries has gone through several distinct phases in the four years since it was originally formed as Found Energy. There was the scrappy startup stage, in which the company was primarily housed in the basement of founder Peter Godart ’15, SM ’19, PhD ’21. Then there was the demonstration phase, in which the company worked to productize its technology for transforming aluminum into high-density fuel for industrial operations.

Now, after confronting supply chain vulnerabilities related to critical metals in its aluminum fuel business, the company is launching a new division, Found Metals, to extract the critical metal gallium from mineral refineries — a move that builds on its original technology while addressing a major national security need.

Gallium is a critical material in the defense, semiconductor, and energy sectors. In 2024, China produced 99 percent of the world’s primary supply — market dominance the country takes advantage of through export controls.

Godart’s company developed an electrochemical gallium extraction technology for internal use after realizing how dependent it would be on China for the catalyst material at the center of its aluminum fuel reactors. Now, with support from the U.S. Department of Energy, Found is hoping to use that technology to create a new domestic supply chain for gallium and a host of other important metals.

Found Industries is still committed to its aluminum fuel operations, now under its Found Energy division. It is already running a 100-kilowatt-class demonstration plant and is preparing for industrial pilot deployments next year. But with its expansion, which was announced April 21, the company is also working to meet the moment for critical metals production.

“Gallium is the world’s most critical metal, as it’s 99 percent controlled by China,” Godart says. “When you produce 99 percent of something, you also produce 99 percent of the tools required to extract it. We couldn’t get our hands on some of those tools, so we were forced to come up with a new technology. Now we believe we can deploy this at scale to become one the first major Western suppliers of these metals.”

From fuel to metals

Godart focused on robotics as an undergraduate in MIT’s Department of Mechanical Engineering and Department of Electrical Engineering and Computer Science. Following graduation, he worked at NASA’s Jet Propulsion Laboratory, where he explored systems for tapping into high-density fuels like aluminum on other planets.

“I had this crazy idea that you could use aluminum, which is already a common construction material for aerospace, as a fuel on other planets,” Godart says. “You don’t need most of the aluminum on a spacecraft once you land on another planet. Aluminum is around 40 times more energy-dense than lithium-ion batteries, and if you have an oxidizer, like water on an icy moon for example, then you can react that aluminum with water and extract energy as heat and hydrogen.”

Luckily for people who might spill water on aluminum while cooking, the metal is normally very stable when exposed to air. In order to tap into aluminum’s stored energy, it needs to undergo a chemical reaction. Godart began exploring catalyst materials to create that reaction at NASA. He continued that work with professor of mechanical engineering Douglas Hart when he returned to MIT in 2017, this time for applications a little closer to home.

“If we want to think about moving humanity to other planets, we have some problems to solve here first,” Godart says. “That was the impetus for me to go back to MIT to study using aluminum as a fuel for energy distribution on Earth.”

Around 70 million tons of aluminum are already transported around the globe every year. Godart says that gives aluminum an easier path to scale. During his PhD, he created a process for coating aluminum with a gallium-containing alloy to help tap into aluminum’s embodied energy.

“We found a catalyst that, when mixed with aluminum scraps, enabled aluminum to react with water very rapidly and at orders of magnitude higher power density than what had been possible before,” Godart says. “That meant you could use aluminum as a fuel and get megawatt-scale power from compact reactor systems.”

By the time he finished his PhD in 2021, Godart and his collaborators had developed a system that mixes aluminum fuel with those catalysts to continuously produce electricity at the kilowatt scale through a hydrogen fuel cell.

Godart launched Found Energy in 2022, licensing part of his research from MIT’s Technology License Office and receiving support from MIT’s Venture Mentoring Service. The company received an Activate fellowship, and after quickly outgrowing Godart’s basement, moved into its current 20,000 square foot facility in Charlestown, Massachusetts.

Today, Found Energy is working with industrial companies that have abundant aluminum scrap.

“When you invent a fuel, you then have to invent the engine,” Godart says. “Our engine is called a catalyzed aluminum water reactor. You feed in aluminum that’s been treated with the catalyst and water, and you get a steam-hydrogen gas mixture. We call that our power stream. We use it to cogenerate industrial heat and electricity. The reaction byproduct is a hydrated aluminum oxide that can be sold into various industries or recycled back into aluminum, which is the long-term vision.”

As Godart worked to build more of the systems, he became concerned about Found’s reliance on Chinese supply chains for its catalyst material. So, in 2024, he developed a new way to extract gallium from Bayer liquor, an industrial process stream used to produce aluminum. Traditional methods for extracting gallium rely on foreign-controlled organic chemicals or resins to bind and concentrate the gallium.

Found uses a continuous electrochemical process to recover the gallium directly from Bayer liquor and other industrial feedstocks, even at low concentrations.

“We thought of it as a way to future-proof what we were doing,” Godart says. “Necessity was the mother of invention.”

Then, toward the end of 2024, China began restricting the export of critical metals including gallium.

“We realized we had already developed a technique for producing these restricted metals that could be very quickly adapted,” Godart recalls.

Scaling for national security

On April 14, the Department of Energy’s Office of Critical Minerals and Energy Innovation selected Found as part of its $5.4 million program to recover gallium from domestic feedstocks. The company plans to start extracting gallium, along with other critical metals like indium and germanium, by the end of 2027.

Meanwhile, Found is already running a 100-kilowatt-class aluminum fuel demonstration system in Charlestown and is working through a orders of several megawatts from large public companies.

“For our fuel technology, the vision is to go as big as possible,” Godart says. “We envision major power plants. Aluminum refineries today, for example, consume hundreds of megawatts of continuous thermal power. That’s what we aim to deliver.”

Godart says he spends most of his time now on gallium extraction, but both branches of the business could make supply chains more secure in the future.

“The big focus now is critical metals, because the government needs this,” Godart says. “We’re also making these metals for ourselves, so we’re vertically integrating our own supply chain, which is table stakes now for companies that deal in physical goods. You need to be able to control your inputs. By focusing on metals, it improves the likelihood of success for our aluminum fuel business.”

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MIT affiliates awarded 2026 Guggenheim Fellowships

MIT Research Scientist Afreen Siddiqi ’99, SM ’01, PhD ’06; MIT professors Kathleen Thelen and Vinod Vaikuntanathan SM ’05, PhD ’09; as well as Kate Manne PhD ’11 are among 223 scientists, artists, and scholars awarded 2026 fellowships from the John Simon Guggenheim Memorial Foundation. Working across 55 disciplines, the fellows were selected from almost 5,000 applicants for “prior career achievement and exceptional promise.”

Each fellow receives a monetary stipend to pursue independent work at the highest level under “the freest possible conditions.” Since its founding in 1925, the Guggenheim Foundation has awarded nearly $450 million in fellowships to more than 19,000 fellows. This year, MIT faculty and staff were recognized in the categories of geography and environmental studies, political science, and computer science.

Afreen Siddiqi is a research scientist in the Engineering Systems Laboratory in the Department of Aeronautics and Astronautics. Her expertise is in the development of systems-theoretic analytical methods and quantitative modeling for technical systems in space and on Earth that need to operate and adapt in changing environments. Her work has focused on space exploration, satellite Earth observation for informing decisions, and critical infrastructure planning. She has served as a contributing author to the sixth assessment report of 2022 of the Intergovernmental Panel on Climate Change (IPCC) on implications of water, energy, and food interconnections for climate change adaptation. Her work has received teaching awards and fellowships including the Amelia Earhart Fellowship, Richard D. DuPont Fellowship, and the Rene H. Miller Prize in Systems Engineering.

Kathleen Thelen is the Ford International Professor of Political Science. Her work focuses on the political economy of the rich democracies, with a current emphasis on the study of American capitalism in comparative perspective. Her most recent book, “Attention Shoppers! American Retail Capitalism and the Origins of the Amazon Economy,” was published by Princeton University Press in 2025. Her awards include the Friedrich Schiedel-Award for Politics and Technology, the Aaron Wildavsky Enduring Contribution Prize, and the Michael Endres Research Prize (2019). She was elected to the American Academy of Arts and Sciences in 2015.

Vinod Vaikuntanathan is the Ford Foundation Professor of Engineering in the Department of Electrical Engineering and Computer Science. A principal investigator at the Computer Science and Artificial Intelligence Laboratory, his research focuses upon the foundations of cryptography and its applications to theoretical computer science at large. He is known for his work on fully homomorphic encryption (a powerful cryptographic primitive that enables complex computations on encrypted data), as well as lattice-based cryptography (which lays down a new mathematical foundation for cryptography in the post-quantum world). His awards include the Harold E. Edgerton Faculty Award, the Godel Prize, the Simons Investigator Award, the Distinguished Alumnus Award from Indian Institute of Technology Madras, a Best Paper Award from CRYPTO 2024, test of time awards from IEEE Symposium on Foundations of Computer Science and CRYPTO conferences, and he was named a MacVicar Faculty Fellow in 2024 and an International Association for Cryptologic Research Fellow in 2026.

Kate Manne, who earned her PhD in philosophy at MIT in 2011, is now a professor at Cornell University.

“Our new class of Guggenheim Fellows is representative of the world’s best thinkers, innovators, and creators in art, science, and scholarship,” says Edward Hirsch, award-winning poet and president of the Guggenheim Foundation. “As the foundation enters its second century and looks to the future, I feel confident that this new class of 223 individuals will do bold and inspiring work, undaunted by the challenges ahead. We are honored to support their visionary contributions.”

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Testing sustainable agriculture in Barcelona

A dozen MIT students recently set out for Barcelona — not just to study climate resilience, but to experience it firsthand. As part of STS.S22 (How to Grow Resilient Futures: Regenerative Agriculture and Economies in Catalunya, Spain), an Independent Activities Period course taught by Kate Brown, the Thomas M. Siebel Distinguished Professor in the History of Science, they stepped beyond the classroom and into living systems of sustainability.

Offered as a Global Classroom through MIT International Science and Technology (MISTI), the course reimagined what learning could look like. Instead of working their way through a syllabus containing texts about sustainable farming and the power of cooperatives, Brown’s students got their hands dirty. 

In fact, quite literally: They visited local farms and slaughterhouses; prepped, cooked, and served a cooperative dinner to migrants; and constructed a working greenhouse. In the process, they built a lasting community and forged their own visions about sustainability and how they are compelled to confront climate change — as MIT students now, and eventually as alumni. 

“I wanted the students to think about alternatives to the notion of capitalist development, where the latest technology is seen as the solution to every social problem that emerges. I wanted them to see ways people are solving problems in a place like Barcelona, where communities and ecologies are centered as part of the solution,” Brown says.

Through Brown’s partnerships at the Barcelona Urban Research Institute and Research and Degrowth (R&D)  — and MISTI Spain’s infrastructure — the group of eight undergraduates and four graduate students had the opportunity to examine the historical roots of cooperative movements in the region while simultaneously experiencing today’s iteration of co-op work. 

Brown intentionally designed the three-week syllabus to push students beyond the classroom walls and get them face-to-face with local MISTI Spain collaborators from across the farming and ecology sectors. For example, the class met with Pino Delàs, a pig farmer who left the industrial system to start his own localized, cyclical operation, called Llavora, which supported community farming and generated significantly less waste. 

Rooted in community 

With more than a century of creating cooperatives — both workers and farms — Barcelona and its Catalan roots provided an ideal environment for the students to consider Brown’s questions through fieldwork rooted in community. 

Within their first week on the ground, they collaborated with volunteers at the Agora Squat. The small “pocket park” was initially slated to be developed into a luxury hotel, but a local group of 200 neighborhood residents came together to protest the plan, instead exercising their legal right to use the land, a caveat in Spanish law that allows neighbors to make a case for possessing land that isn’t being used productively. Now, the lush green square boasts a community kitchen and gardens. One night a week, volunteers provide dinner for upward of 60 recent North African migrants, using ingredients sourced from local fruiterias and shops that would have otherwise gone to waste at the close of business. 

On this particular Thursday, Brown’s students became nonprofit managers and chefs, but they also became community members themselves. In just a few hours from start to finish, the students had to source donated produce from the local vendors, come up with a recipe using what they’d gathered, and then prepare a meal in the rudimentary kitchen. “They received a lot of turnips and had to create a recipe to use them,” Brown says. In the end, a flavorful stew simmered in a massive metal pan over propane burners, brought alive with fresh chilies picked from the garden. 

“This was way outside some students’ comfort zones,” Brown says. Yet, that was exactly the point of the activity. By the end of the evening, the students discovered that sometimes the most profound educational moments take shape only after challenging the limits of learning. 

“Many of us do not consider ourselves chefs, so it was empowering to discover that, together, we had the capacity to create a nourishing meal for 70 people, with produce that would have otherwise gone to waste. This meal that we created on the spot, in combination with many of the other workshops during the program, was a strong reminder of how much agency each of us has to effect change within isolating and constraining systems, especially in community with like-minded individuals,” says Sonia Torres Rodriguez, a first-year PhD student in urban studies and planning.

Torres Rodriguez focuses her doctoral research on affordable and climate-resilient housing. She was drawn to the IAP program's exploration of innovative approaches to more equitably distributing the means of producing housing and food, and was excited to be learning in person in Spain. “Cooking together, admiring healthy regenerative soil, foraging, learning traditional methods to braid grass, installing mini solar panels, and hosting poetry circles, would have been impossible to replicate on Zoom,” she says. 

Calvin Macatantan, a senior in computer science and urban studies and planning, was initially drawn to the program because of his interest in resilient economies and how they support the communities they serve. Other than visiting family in the Philippines, he’d never left the United States before. He was especially moved by the group’s stay at La Bruguera, an eco-resort partnered with R&D that serves as a “living laboratory.” The cohort heard from local experts in regenerative agriculture, soil health, and low-tech agroforestry, alongside hands-on activities such as eco-art sessions, weaving lessons, and the rebuilding of a greenhouse. 

As part of a final project for the course, Macatantan and another student wrote and illustrated a children’s book that explains La Bruguera’s work by making the soil come to life as the main protagonist for young readers. 

Brown’s course pushed Sofia Espindola de La Mora to think more critically about everyday systems and their environmental impact. Originally from Puerto Rico, the first-year student has watched helplessly in recent years as climate change has increased the frequency and magnitude of natural disasters at home.

She came to MIT looking for answers and wanting to make a difference, and signed up for Brown’s course as part of that quest. “It was fascinating to see firsthand that the degrowth movement doesn’t mean slowing down is a bad thing, but instead that the constant striving for more is what has led us to many of the predicaments we now face as a society. It forced me to think about whether it would even be possible for me to sustain the life I have now using renewable energy,” Espindola de La Mora says. The course convinced her to focus her studies on climate system science and engineering. 

A climate context

Broadening students’ perspectives was a priority for Brown, whose research lies at the intersection of history, science, technology, and bio-politics. She’s known on campus for courses like STS.038 (Risky Business: Food Production, Environment, and Health). Her 2026 book, “Tiny Gardens Everywhere: The Past, Present and Future of the Self-Provisioning City,” examines urban systems, including gardens. 

When Brown was designing the Global Classroom — made possible through MISTI, with additional support from the MIT Energy Initiative — she centered a value she considers imperative in any course today: addressing climate and other human-driven environmental challenges.

“I’m focused on training students to approach these problems at the local level, so they see what happens when they’re working through communities, rather than prescribing to them something to scale all over the world,” Brown says.

That localized, individualized approach helped expand on what the students initially believed was possible, and compelled them to become part of the solution through their studies and in their professional lives. 

Since their return to campus, Brown’s students have continued to lean on one another and build community, one meal at a time. Many Tuesday nights, they come together to cook dinner, Barcelona squat style. Each individual brings their ingredients, and together they create a recipe that nourishes and sustains.  

“I was losing a lot of faith in the world before this trip,” Macatantan admits. “We’re constantly surrounded by consumption and the drive to do more. This experience helped me realize that I want to do something that impacts people. For me, that will look like research. I want to become an expert in a subject and become someone who can help communicate that knowledge to people who need it.” 

“MISTI Global Classrooms like this show what happens when learning extends beyond the MIT campus,” says Alicia Goldstein Raun, associate director of MISTI and managing director of the MIT-Spain Program at the Center for International Studies. “I was excited when Professor Brown approached me to help shape this new class, knowing it would resonate with students,” says Raun. “The students tackled global challenges like climate change and explored the degrowth movement while immersing themselves in Spanish communities and culture.”

For faculty interested in designing a MISTI Global Classroom, more information can be found here.

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