In medical school, Matthew Dolan ’81 briefly considered specializing in orthopedic surgery because of the materials science nature of the work — but he soon realized that he didn’t have the innate skills required for that type of work.

“I’ll be honest with you — I can’t parallel park,” he jokes. “You can consider a lot of things, but if you find the things that you’re good at and that excite you, you can hopefully move forward with those.”

Dolan certainly has, tackling problems from bench to bedside and beyond. Both in the United States and abroad through the U.S. Air Force, Dolan has emerged as a leader in immunology and virology, and has served as director of the Defense Institute for Medical Operations. He’s worked on everything from foodborne illnesses and Ebola to biological weapons and Covid-19, and has even been a guest speaker on NPR’s “Science Friday.”

“This is fun and interesting, and I believe that, and I work hard to convey that — and it’s contagious,” he says. “You can affect people with that excitement.”

Pieces of the puzzle

Dolan fondly recalls his years at MIT, and is still in touch with many of the “brilliant” and “interesting” friends he made while in Cambridge.

He notes that the challenges that were the most rewarding in his career were also the ones that MIT had uniquely prepared him for. Dolan, a Course 7 major, naturally took many classes outside of biology as part of his undergraduate studies: organic chemistry was foundational for understanding toxicology while studying chemical weapons, while pathogens like Legionella, which causes pneumonia and can spread through water systems such as ice machines or air conditioners, are solved at the interface between public health and ecology.

“I learned that learning can be a high-intensity experience,” Dolan recalls. “You can be aggressive in your learning; you can learn and excel in a wide variety of things and gather up all the knowledge and knowledgeable people to work together towards solutions.”

Dolan, for example, worked in the Amazon Basin in Peru on a public health crisis of a sharp rise in childhood mortality due to malaria. The cause was a few degrees removed from the immediate problem: human agriculture had affected the Amazon’s tributaries, leading to still and stagnant water where before there had been rushing streams and rivers. This change in the environment allowed a certain mosquito species of “avid human biters” to thrive. 

“It can be helpful and important for some people to have a really comprehensive and contextual view of scientific problems and biological problems,” he says. “It’s very rewarding to put the pieces in a puzzle like that together.”

Choosing To serve

Dolan says a key to finding meaning in his work, especially during difficult times, is a sentiment from Alsatian polymath and Nobel Peace Prize winner Albert Schweitzer: “The only ones among you who will be really happy are those who will have sought and found how to serve.”

One of Dolan’s early formative experiences was working in the heart of the HIV/AIDS epidemic, at a time when there was no effective treatment. No matter how hard he worked, the patients would still die.

“Failure is not an option — unless you have to fail. You can’t let the failures destroy you,” he says. “There are a lot of other battles out there, and it’s self-indulgent to ignore them and focus on your woe.”

Lasting impacts

Dolan couldn’t pick a favorite country, but notes that he’s always impressed seeing how people value the chance to excel with science and medicine when offered resources and respect. Ultimately, everyone he’s worked with, no matter their differences, was committed to solving problems and improving lives.

Dolan worked in Russia after the Berlin Wall fell, on HIV/AIDS in Moscow and tuberculosis in the Russian Far East. Although relations with Russia are currently tense, to say the least, Dolan remains optimistic for a brighter future.

“People that were staunch adversaries can go on to do well together,” he says. “Sometimes, peace leads to partnership. Remembering that it was once possible gives me great hope.”

Dolan understands that the most lasting impact he has had is, likely, teaching: Time marches on, and discoveries can be lost to history, but teaching and training people continues and propagates. In addition to guiding the next generation of health-care specialists, Dolan also developed programs in laboratory biosafety and biosecurity with the U.S. departments of State and Defense, and taught those programs around the world.

“Working in prevention gives you the chance to take care of process problems before they become people problems — patient care problems,” he says. “I have been so impressed with the courageous and giving people that have worked with me.” 

When it comes to water use, most of us think of the water we drink. But industrial uses for things like manufacturing account for billions of gallons of water each day. For instance, making a single iPhone, by one estimate, requires more than 3,000 gallons.

Gradiant is working to reduce the world’s industrial water footprint. Founded by a team from MIT, Gradiant offers water recycling, treatment, and purification solutions to some of the largest companies on Earth, including Coca Cola, Tesla, and the Taiwan Semiconductor Manufacturing Company. By serving as an end-to-end water company, Gradiant says it helps companies reuse 2 billion gallons of water each day and saves another 2 billion gallons of fresh water from being withdrawn.

The company’s mission is to preserve water for generations to come in the face of rising global demand.

“We work on both ends of the water spectrum,” Gradiant co-founder and CEO Anurag Bajpayee SM ’08, PhD ’12 says. “We work with ultracontaminated water, and we can also provide ultrapure water for use in areas like chip fabrication. Our specialty is in the extreme water challenges that can’t be solved with traditional technologies.”

For each customer, Gradiant builds tailored water treatment solutions that combine chemical treatments with membrane filtration and biological process technologies, leveraging a portfolio of patents to drastically cut water usage and waste.

“Before Gradiant, 40 million liters of water would be used in the chip-making process. It would all be contaminated and treated, and maybe 30 percent would be reused,” explains Gradiant co-founder and COO Prakash Govindan PhD ’12. “We have the technology to recycle, in some cases, 99 percent of the water. Now, instead of consuming 40 million liters, chipmakers only need to consume 400,000 liters, which is a huge shift in the water footprint of that industry. And this is not just with semiconductors. We’ve done this in food and beverage, we’ve done this in renewable energy, we’ve done this in pharmaceutical drug production, and several other areas.”

Learning the value of water

Govindan grew up in a part of India that experienced a years-long drought beginning when he was 10. Without tap water, one of Govindan’s chores was to haul water up the stairs of his apartment complex each time a truck delivered it.

“However much water my brother and I could carry was how much we had for the week,” Govindan recalls. “I learned the value of water the hard way.”

Govindan attended the Indian Institute of Technology as an undergraduate, and when he came to MIT for his PhD, he sought out the groups working on water challenges. He began working on a water treatment method called carrier gas extraction for his PhD under Gradiant co-founder and MIT Professor John Lienhard.

Bajpayee also worked on water treatment methods at MIT, and after brief stints as postdocs at MIT, he and Govindan licensed their work and founded Gradiant.

Carrier gas extraction became Gradiant’s first proprietary technology when the company launched in 2013. The founders began by treating wastewater created by oil and gas wells, landing their first partner in a Texas company. But Gradiant gradually expanded to solving water challenges in power generation, mining, textiles, and refineries. Then the founders noticed opportunities in industries like electronics, semiconductors, food and beverage, and pharmaceuticals. Today, oil and gas wastewater treatment makes up a small percentage of Gradiant’s work.

As the company expanded, it added technologies to its portfolio, patenting new water treatment methods around reverse osmosis, selective contaminant extraction, and free radical oxidation. Gradiant has also created a digital system that uses AI to measure, predict, and control water treatment facilities.

“The advantage Gradiant has over every other water company is that R&D is in our DNA,” Govindan says, noting Gradiant has a world-class research lab at its headquarters in Boston. “At MIT, we learned how to do cutting-edge technology development, and we never let go of that.”

The founders compare their suite of technologies to LEGO bricks they can mix and match depending on a customer’s water needs. Gradiant has built more than 2,500 of these end-to-end systems for customers around the world.

“Our customers aren’t water companies; they are industrial clients like semiconductor manufacturers, drug companies, and food and beverage companies,” Bajpayee says. “They aren’t about to start operating a water treatment plant. They look at us as their water partner who can take care of the whole water problem.”

Continuing innovation

The founders say Gradiant has been roughly doubling its revenue each year over the last five years, and it’s continuing to add technologies to its platform. For instance, Gradiant recently developed a critical minerals recovery solution to extract materials like lithium and nickel from customers’ wastewater, which could expand access to critical materials essential to the production of batteries and other products.

“If we can extract lithium from brine water in an environmentally and economically feasible way, the U.S. can meet all of its lithium needs from within the U.S.,” Bajpayee says. “What’s preventing large-scale extraction of lithium from brine is technology, and we believe what we have now deployed will open the floodgates for direct lithium extraction and completely revolutionized the industry.”

The company has also validated a method for eliminating PFAS — so-called toxic “forever chemicals” — in a pilot project with a leading U.S. semiconductor manufacturer. In the near future, it hopes to bring that solution to municipal water treatment plants to protect cities.

At the heart of Gradiant’s innovation is the founders’ belief that industrial activity doesn’t have to deplete one of the world’s most vital resources.

“Ever since the industrial revolution, we’ve been taking from nature,” Bajpayee says. “By treating and recycling water, by reducing water consumption and making industry highly water efficient, we have this unique opportunity to turn the clock back and give nature water back. If that’s your driver, you can’t choose not to innovate.”

Highly energetic explosions in the sky are commonly attributed to gamma-ray bursts. We now understand that these bursts originate from either the merger of two neutron stars or the collapse of a massive star. In these scenarios, a newborn black hole is formed, emitting a jet that travels at nearly the speed of light. When these jets are directed toward Earth, we can observe them from vast distances — sometimes billions of light-years away — due to a relativistic effect known as Doppler boosting. Over the past decade, thousands of such gamma-ray bursts have been detected.

Since its launch in 2024, the Einstein Probe — an X-ray space telescope developed by the Chinese Academy of Sciences (CAS) in partnership with European Space Agency (ESA) and the Max Planck Institute for Extraterrestrial Physics — has been scanning the skies looking for energetic explosions, and in April the telescope observed an unusual event designated as EP240408A. Now an international team of astronomers, including Dheeraj Pasham from MIT, Igor Andreoni from University of North Carolina at Chapel Hill, and Brendan O’Connor from Carnegie Mellon University, and others have investigated this explosion using a slew of ground-based and space-based telescopes, including NuSTAR, Swift, Gemini, Keck, DECam, VLA, ATCA, and NICER, which was developed in collaboration with MIT. 

An open-access report of their findings, published Jan. 27 in The Astrophysical Journal Letters, indicates that the characteristics of this explosion do not match those of typical gamma-ray bursts. Instead, it may represent a rare new class of powerful cosmic explosion — a jetted tidal disruption event, which occurs when a supermassive black hole tears apart a star. 

“NICER’s ability to steer to pretty much any part of the sky and monitor for weeks has been instrumental in our understanding of these unusual cosmic explosions,” says Pasham, a research scientist at the MIT Kavli Institute for Astrophysics and Space Research.

While a jetted tidal disruption event is plausible, the researchers say the lack of radio emissions from this jet is puzzling. O’Connor surmises, “EP240408a ticks some of the boxes for several different kinds of phenomena, but it doesn’t tick all the boxes for anything. In particular, the short duration and high luminosity are hard to explain in other scenarios. The alternative is that we are seeing something entirely new!”

According to Pasham, the Einstein Probe is just beginning to scratch the surface of what seems possible. “I’m excited to chase the next weird explosion from the Einstein Probe”, he says, echoing astronomers worldwide who look forward to the prospect of discovering more unusual explosions from the farthest reaches of the cosmos.

The National Academy of Sciences (NAS) recently announced that MIT Associate Professor Evelina Fedorenko will receive a 2025 Troland Research Award for her groundbreaking contributions toward understanding the language network in the human brain.

The Troland Research Award is given annually to recognize unusual achievement by early-career researchers within the broad spectrum of experimental psychology.

Fedorenko, an associate professor of brain and cognitive sciences and a McGovern Institute for Brain Research investigator, is interested in how minds and brains create language. Her lab is unpacking the internal architecture of the brain’s language system and exploring the relationship between language and various cognitive, perceptual, and motor systems. Her novel methods combine precise measures of an individual’s brain organization with innovative computational modeling to make fundamental discoveries about the computations that underlie the uniquely human ability for language.

Fedorenko has shown that the language network is selective for language processing over diverse non-linguistic processes that have been argued to share computational demands with language, such as math, music, and social reasoning. Her work has also demonstrated that syntactic processing is not localized to a particular region within the language network, and every brain region that responds to syntactic processing is at least as sensitive to word meanings.

She has also shown that representations from neural network language models, such as ChatGPT, are similar to those in the human language brain areas. Fedorenko also highlighted that although language models can master linguistic rules and patterns, they are less effective at using language in real-world situations. In the human brain, that kind of functional competence is distinct from formal language competence, she says, requiring not just language-processing circuits but also brain areas that store knowledge of the world, reason, and interpret social interactions. Contrary to a prominent view that language is essential for thinking, Fedorenko argues that language is not the medium of thought and is primarily a tool for communication.

Ultimately, Fedorenko’s cutting-edge work is uncovering the computations and representations that fuel language processing in the brain. She will receive the Troland Award this April, during the annual meeting of the NAS in Washington.

If you’ve watched cartoons like Tom and Jerry, you’ll recognize a common theme: An elusive target avoids his formidable adversary. This game of “cat-and-mouse” — whether literal or otherwise — involves pursuing something that ever-so-narrowly escapes you at each try.

In a similar way, evading persistent hackers is a continuous challenge for cybersecurity teams. Keeping them chasing what’s just out of reach, MIT researchers are working on an AI approach called “artificial adversarial intelligence” that mimics attackers of a device or network to test network defenses before real attacks happen. Other AI-based defensive measures help engineers further fortify their systems to avoid ransomware, data theft, or other hacks.

Here, Una-May O'Reilly, an MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) principal investigator who leads the Anyscale Learning For All Group (ALFA), discusses how artificial adversarial intelligence protects us from cyber threats.

Q: In what ways can artificial adversarial intelligence play the role of a cyber attacker, and how does artificial adversarial intelligence portray a cyber defender?

A: Cyber attackers exist along a competence spectrum. At the lowest end, there are so-called script-kiddies, or threat actors who spray well-known exploits and malware in the hopes of finding some network or device that hasn't practiced good cyber hygiene. In the middle are cyber mercenaries who are better-resourced and organized to prey upon enterprises with ransomware or extortion. And, at the high end, there are groups that are sometimes state-supported, which can launch the most difficult-to-detect "advanced persistent threats" (or APTs).

Think of the specialized, nefarious intelligence that these attackers marshal — that's adversarial intelligence. The attackers make very technical tools that let them hack into code, they choose the right tool for their target, and their attacks have multiple steps. At each step, they learn something, integrate it into their situational awareness, and then make a decision on what to do next. For the sophisticated APTs, they may strategically pick their target, and devise a slow and low-visibility plan that is so subtle that its implementation escapes our defensive shields. They can even plan deceptive evidence pointing to another hacker! 

My research goal is to replicate this specific kind of offensive or attacking intelligence, intelligence that is adversarially-oriented (intelligence that human threat actors rely upon). I use AI and machine learning to design cyber agents and model the adversarial behavior of human attackers. I also model the learning and adaptation that characterizes cyber arms races.

I should also note that cyber defenses are pretty complicated. They've evolved their complexity in response to escalating attack capabilities. These defense systems involve designing detectors, processing system logs, triggering appropriate alerts, and then triaging them into incident response systems. They have to be constantly alert to defend a very big attack surface that is hard to track and very dynamic. On this other side of attacker-versus-defender competition, my team and I also invent AI in the service of these different defensive fronts. 

Another thing stands out about adversarial intelligence: Both Tom and Jerry are able to learn from competing with one another! Their skills sharpen and they lock into an arms race. One gets better, then the other, to save his skin, gets better too. This tit-for-tat improvement goes onwards and upwards! We work to replicate cyber versions of these arms races.

Q: What are some examples in our everyday lives where artificial adversarial intelligence has kept us safe? How can we use adversarial intelligence agents to stay ahead of threat actors?

A: Machine learning has been used in many ways to ensure cybersecurity. There are all kinds of detectors that filter out threats. They are tuned to anomalous behavior and to recognizable kinds of malware, for example. There are AI-enabled triage systems. Some of the spam protection tools right there on your cell phone are AI-enabled!

With my team, I design AI-enabled cyber attackers that can do what threat actors do. We invent AI to give our cyber agents expert computer skills and programming knowledge, to make them capable of processing all sorts of cyber knowledge, plan attack steps, and to make informed decisions within a campaign.

Adversarially intelligent agents (like our AI cyber attackers) can be used as practice when testing network defenses. A lot of effort goes into checking a network's robustness to attack, and AI is able to help with that. Additionally, when we add machine learning to our agents, and to our defenses, they play out an arms race we can inspect, analyze, and use to anticipate what countermeasures may be used when we take measures to defend ourselves.

Q: What new risks are they adapting to, and how do they do so?

A: There never seems to be an end to new software being released and new configurations of systems being engineered. With every release, there are vulnerabilities an attacker can target. These may be examples of weaknesses in code that are already documented, or they may be novel. 

New configurations pose the risk of errors or new ways to be attacked. We didn't imagine ransomware when we were dealing with denial-of-service attacks. Now we're juggling cyber espionage and ransomware with IP [intellectual property] theft. All our critical infrastructure, including telecom networks and financial, health care, municipal, energy, and water systems, are targets. 

Fortunately, a lot of effort is being devoted to defending critical infrastructure. We will need to translate that to AI-based products and services that automate some of those efforts. And, of course, to keep designing smarter and smarter adversarial agents to keep us on our toes, or help us practice defending our cyber assets.

Imagine a boombox that tracks your every move and suggests music to match your personal dance style. That’s the idea behind “Be the Beat,” one of several projects from MIT course 4.043/4.044 (Interaction Intelligence), taught by Marcelo Coelho in the Department of Architecture, that were presented at the 38th annual NeurIPS (Neural Information Processing Systems) conference in December 2024. With over 16,000 attendees converging in Vancouver, NeurIPS is a competitive and prestigious conference dedicated to research and science in the field of artificial intelligence and machine learning, and a premier venue for showcasing cutting-edge developments.

The course investigates the emerging field of large language objects, and how artificial intelligence can be extended into the physical world. While “Be the Beat” transforms the creative possibilities of dance, other student submissions span disciplines such as music, storytelling, critical thinking, and memory, creating generative experiences and new forms of human-computer interaction. Taken together, these projects illustrate a broader vision for artificial intelligence: one that goes beyond automation to catalyze creativity, reshape education, and reimagine social interactions.

Be the Beat 

“Be the Beat,” by Ethan Chang, an MIT mechanical engineering and design student, and Zhixing Chen, an MIT mechanical engineering and music student, is an AI-powered boombox that suggests music from a dancer's movement. Dance has traditionally been guided by music throughout history and across cultures, yet the concept of dancing to create music is rarely explored.

“Be the Beat” creates a space for human-AI collaboration on freestyle dance, empowering dancers to rethink the traditional dynamic between dance and music. It uses PoseNet to describe movements for a large language model, enabling it to analyze dance style and query APIs to find music with similar style, energy, and tempo. Dancers interacting with the boombox reported having more control over artistic expression and described the boombox as a novel approach to discovering dance genres and choreographing creatively.

A Mystery for You

“A Mystery for You,” by Mrinalini Singha SM ’24, a recent graduate in the Art, Culture, and Technology program, and Haoheng Tang, a recent graduate of the Harvard University Graduate School of Design, is an educational game designed to cultivate critical thinking and fact-checking skills in young learners. The game leverages a large language model (LLM) and a tangible interface to create an immersive investigative experience. Players act as citizen fact-checkers, responding to AI-generated “news alerts” printed by the game interface. By inserting cartridge combinations to prompt follow-up “news updates,” they navigate ambiguous scenarios, analyze evidence, and weigh conflicting information to make informed decisions.

This human-computer interaction experience challenges our news-consumption habits by eliminating touchscreen interfaces, replacing perpetual scrolling and skim-reading with a haptically rich analog device. By combining the affordances of slow media with new generative media, the game promotes thoughtful, embodied interactions while equipping players to better understand and challenge today’s polarized media landscape, where misinformation and manipulative narratives thrive.

Memorscope

“Memorscope,” by MIT Media Lab research collaborator Keunwook Kim, is a device that creates collective memories by merging the deeply human experience of face-to-face interaction with advanced AI technologies. Inspired by how we use microscopes and telescopes to examine and uncover hidden and invisible details, Memorscope allows two users to “look into” each other’s faces, using this intimate interaction as a gateway to the creation and exploration of their shared memories.

The device leverages AI models such as OpenAI and Midjourney, introducing different aesthetic and emotional interpretations, which results in a dynamic and collective memory space. This space transcends the limitations of traditional shared albums, offering a fluid, interactive environment where memories are not just static snapshots but living, evolving narratives, shaped by the ongoing relationship between users.

Narratron

“Narratron,” by Harvard Graduate School of Design students Xiying (Aria) Bao and Yubo Zhao, is an interactive projector that co-creates and co-performs children's stories through shadow puppetry using large language models. Users can press the shutter to “capture” protagonists they want to be in the story, and it takes hand shadows (such as animal shapes) as input for the main characters. The system then develops the story plot as new shadow characters are introduced. The story appears through a projector as a backdrop for shadow puppetry while being narrated through a speaker as users turn a crank to “play” in real time. By combining visual, auditory, and bodily interactions in one system, the project aims to spark creativity in shadow play storytelling and enable multi-modal human-AI collaboration.

Perfect Syntax

“Perfect Syntax,” by Karyn Nakamura ’24, is a video art piece examining the syntactic logic behind motion and video. Using AI to manipulate video fragments, the project explores how the fluidity of motion and time can be simulated and reconstructed by machines. Drawing inspiration from both philosophical inquiry and artistic practice, Nakamura's work interrogates the relationship between perception, technology, and the movement that shapes our experience of the world. By reimagining video through computational processes, Nakamura investigates the complexities of how machines understand and represent the passage of time and motion.

Last year the Earth exceeded 1.5 degrees Celsius of warming above preindustrial times, a threshold beyond which wildfires, droughts, floods, and other climate impacts are expected to escalate in frequency, intensity, and lethality. To cap global warming at 1.5 C and avert that scenario, the nearly 200 signatory nations of the Paris Agreement on climate change will need to not only dramatically lower their greenhouse gas emissions, but also take measures to remove carbon dioxide (CO2) from the atmosphere and durably store it at or below the Earth’s surface.

Past analyses of the climate mitigation potential, costs, benefits, and drawbacks of different carbon dioxide removal (CDR) options have focused primarily on three strategies: bioenergy with carbon capture and storage (BECCS), in which CO2-absorbing plant matter is converted into fuels or directly burned to generate energy, with some of the plant’s carbon content captured and then stored safely and permanently; afforestation/reforestation, in which CO2-absorbing trees are planted in large numbers; and direct air carbon capture and storage (DACCS), a technology that captures and separates CO2 directly from ambient air, and injects it into geological reservoirs or incorporates it into durable products. 

To provide a more comprehensive and actionable analysis of CDR, a new study by researchers at the MIT Center for Sustainability Science and Strategy (CS3) first expands the option set to include biochar (charcoal produced from plant matter and stored in soil) and enhanced weathering (EW) (spreading finely ground rock particles on land to accelerate storage of CO2 in soil and water). The study then evaluates portfolios of all five options — in isolation and in combination — to assess their capability to meet the 1.5 C goal, and their potential impacts on land, energy, and policy costs.

The study appears in the journal Environmental Research Letters. Aided by their global multi-region, multi-sector Economic Projection and Policy Analysis (EPPA) model, the MIT CS3 researchers produce three key findings.

First, the most cost-effective, low-impact strategy that policymakers can take to achieve global net-zero emissions — an essential step in meeting the 1.5 C goal — is to diversify their CDR portfolio, rather than rely on any single option. This approach minimizes overall cropland and energy consumption, and negative impacts such as increased food insecurity and decreased energy supplies.

By diversifying across multiple CDR options, the highest CDR deployment of around 31.5 gigatons of CO2 per year is achieved in 2100, while also proving the most cost-effective net-zero strategy. The study identifies BECCS and biochar as most cost-competitive in removing CO2 from the atmosphere, followed by EW, with DACCS as uncompetitive due to high capital and energy requirements. While posing logistical and other challenges, biochar and EW have the potential to improve soil quality and productivity across 45 percent of all croplands by 2100.

“Diversifying CDR portfolios is the most cost-effective net-zero strategy because it avoids relying on a single CDR option, thereby reducing and redistributing negative impacts on agriculture, forestry, and other land uses, as well as on the energy sector,” says Solene Chiquier, lead author of the study who was a CS3 postdoc during its preparation.

The second finding: There is no optimal CDR portfolio that will work well at global and national levels. The ideal CDR portfolio for a particular region will depend on local technological, economic, and geophysical conditions. For example, afforestation and reforestation would be of great benefit in places like Brazil, Latin America, and Africa, by not only sequestering carbon in more acreage of protected forest but also helping to preserve planetary well-being and human health.

“In designing a sustainable, cost-effective CDR portfolio, it is important to account for regional availability of agricultural, energy, and carbon-storage resources,” says Sergey Paltsev, CS3 deputy director, MIT Energy Initiative senior research scientist, and supervising co-author of the study. “Our study highlights the need for enhancing knowledge about local conditions that favor some CDR options over others.”

Finally, the MIT CS3 researchers show that delaying large-scale deployment of CDR portfolios could be very costly, leading to considerably higher carbon prices across the globe — a development sure to deter the climate mitigation efforts needed to achieve the 1.5 C goal. They recommend near-term implementation of policy and financial incentives to help fast-track those efforts.

A home robot trained to perform household tasks in a factory may fail to effectively scrub the sink or take out the trash when deployed in a user’s kitchen, since this new environment differs from its training space.

To avoid this, engineers often try to match the simulated training environment as closely as possible with the real world where the agent will be deployed.

However, researchers from MIT and elsewhere have now found that, despite this conventional wisdom, sometimes training in a completely different environment yields a better-performing artificial intelligence agent.

Their results indicate that, in some situations, training a simulated AI agent in a world with less uncertainty, or “noise,” enabled it to perform better than a competing AI agent trained in the same, noisy world they used to test both agents.

The researchers call this unexpected phenomenon the indoor training effect.

“If we learn to play tennis in an indoor environment where there is no noise, we might be able to more easily master different shots. Then, if we move to a noisier environment, like a windy tennis court, we could have a higher probability of playing tennis well than if we started learning in the windy environment,” explains Serena Bono, a research assistant in the MIT Media Lab and lead author of a paper on the indoor training effect.

The researchers studied this phenomenon by training AI agents to play Atari games, which they modified by adding some unpredictability. They were surprised to find that the indoor training effect consistently occurred across Atari games and game variations.

They hope these results fuel additional research toward developing better training methods for AI agents.

“This is an entirely new axis to think about. Rather than trying to match the training and testing environments, we may be able to construct simulated environments where an AI agent learns even better,” adds co-author Spandan Madan, a graduate student at Harvard University.

Bono and Madan are joined on the paper by Ishaan Grover, an MIT graduate student; Mao Yasueda, a graduate student at Yale University; Cynthia Breazeal, professor of media arts and sciences and leader of the Personal Robotics Group in the MIT Media Lab; Hanspeter Pfister, the An Wang Professor of Computer Science at Harvard; and Gabriel Kreiman, a professor at Harvard Medical School. The research will be presented at the Association for the Advancement of Artificial Intelligence Conference.

Training troubles

The researchers set out to explore why reinforcement learning agents tend to have such dismal performance when tested on environments that differ from their training space.

Reinforcement learning is a trial-and-error method in which the agent explores a training space and learns to take actions that maximize its reward.

The team developed a technique to explicitly add a certain amount of noise to one element of the reinforcement learning problem called the transition function. The transition function defines the probability an agent will move from one state to another, based on the action it chooses.

If the agent is playing Pac-Man, a transition function might define the probability that ghosts on the game board will move up, down, left, or right. In standard reinforcement learning, the AI would be trained and tested using the same transition function.

The researchers added noise to the transition function with this conventional approach and, as expected, it hurt the agent’s Pac-Man performance.

But when the researchers trained the agent with a noise-free Pac-Man game, then tested it in an environment where they injected noise into the transition function, it performed better than an agent trained on the noisy game.

“The rule of thumb is that you should try to capture the deployment condition’s transition function as well as you can during training to get the most bang for your buck. We really tested this insight to death because we couldn’t believe it ourselves,” Madan says.

Injecting varying amounts of noise into the transition function let the researchers test many environments, but it didn’t create realistic games. The more noise they injected into Pac-Man, the more likely ghosts would randomly teleport to different squares.

To see if the indoor training effect occurred in normal Pac-Man games, they adjusted underlying probabilities so ghosts moved normally but were more likely to move up and down, rather than left and right. AI agents trained in noise-free environments still performed better in these realistic games.

“It was not only due to the way we added noise to create ad hoc environments. This seems to be a property of the reinforcement learning problem. And that was even more surprising to see,” Bono says.

Exploration explanations

When the researchers dug deeper in search of an explanation, they saw some correlations in how the AI agents explore the training space.

When both AI agents explore mostly the same areas, the agent trained in the non-noisy environment performs better, perhaps because it is easier for the agent to learn the rules of the game without the interference of noise.

If their exploration patterns are different, then the agent trained in the noisy environment tends to perform better. This might occur because the agent needs to understand patterns it can’t learn in the noise-free environment.

“If I only learn to play tennis with my forehand in the non-noisy environment, but then in the noisy one I have to also play with my backhand, I won’t play as well in the non-noisy environment,” Bono explains.

In the future, the researchers hope to explore how the indoor training effect might occur in more complex reinforcement learning environments, or with other techniques like computer vision and natural language processing. They also want to build training environments designed to leverage the indoor training effect, which could help AI agents perform better in uncertain environments.

In 2014, a team of MIT students in course 15.366 (Climate and Energy Ventures) developed a plan to commercialize MIT research on how to move information between chips with light instead of electricity, reducing energy usage.

After completing the class, which challenges students to identify early customers and pitch their business plan to investors, the team went on to win both grand prizes at the MIT Clean Energy Prize. Today the company, Ayar Labs, has raised a total of $370 million from a group including chip leaders AMD, Intel, and NVIDIA, to scale the manufacturing of its optical chip interconnects.

Ayar Labs is one of many companies whose roots can be traced back to 15.366. In fact, more than 150 companies have been founded by alumni of the class since its founding in 2007.

In the class, student teams select a technology or idea and determine the best path for its commercialization. The semester-long project, which is accompanied by lectures and mentoring, equips students with real-world experience in launching a business.

“The goal is to educate entrepreneurs on how to start companies in the climate and energy space,” says Senior Lecturer Tod Hynes, who co-founded the course and has been teaching since 2008. “We do that through hands-on experience. We require students to engage with customers, talk to potential suppliers, partners, investors, and to practice their pitches to learn from that feedback.”

The class attracts hundreds of student applications each year. As one of the catalysts for MIT spinoffs, it is also one reason a 2015 report found that MIT alumni-founded companies had generated roughly $1.9 trillion in annual revenues. If MIT were a country, that figure that would make it the 10th largest economy in the world, according to the report.

“’Mens et manus’ (‘mind and hand’) is MIT's motto, and the hands-on experience we try to provide in this class is hard to beat,” Hynes says. “When you actually go through the process of commercialization in the real world, you learn more and you’re in a better spot. That experiential learning approach really aligns with MIT’s approach.”

Simulating a startup

The course was started by Bill Aulet, a professor of the practice at the MIT Sloan School of Management and the managing director of the Martin Trust Center for MIT Entrepreneurship. After serving as an advisor the first year and helping Aulet launch the class, Hynes began teaching the class with Aulet in the fall of 2008. The pair also launched the Climate and Energy Prize around the same time, which continues today and recently received over 150 applications from teams from around the world.

A core feature of the class is connecting students in different academic fields. Each year, organizers aim to enroll students with backgrounds in science, engineering, business, and policy.

“The class is meant to be accessible to anybody at MIT,” Hynes says, noting the course has also since opened to students from Harvard University. “We’re trying to pull across disciplines.”

The class quickly grew in popularity around campus. Over the last few years, the course has had about 150 students apply for 50 spots.

“I mentioned Climate and Energy Ventures in my application to MIT,” says Chris Johnson, a second-year graduate student in the Leaders for Global Operations (LGO) Program. “Coming into MIT, I was very interested in sustainability, and energy in particular, and also in startups. I had heard great things about the class, and I waited until my last semester to apply.”

The course’s organizers select mostly graduate students, whom they prefer to be in the final year of their program so they can more easily continue working on the venture after the class is finished.

“Whether or not students stick with the project from the class, it’s a great experience that will serve them in their careers,” says Jennifer Turliuk, the practice leader for climate and energy artificial intelligence at the Martin Trust Center for Entrepreneurship, who helped teach the class this fall.

Hynes describes the course as a venture-building simulation. Before it begins, organizers select up to 30 technologies and ideas that are in the right stage for commercialization. Students can also come into the class with ideas or technologies they want to work on.

After a few weeks of introductions and lectures, students form into multidisciplinary teams of about five and begin going through each of the 24 steps of building a startup described in Aulet’s book “Disciplined Entrepreneurship,” which includes things like engaging with potential early customers, quantifying a value proposition, and establishing a business model. Everything builds toward a one-hour final presentation that’s designed to simulate a pitch to investors or government officials.

“It’s a lot of work, and because it’s a team-based project, your grade is highly dependent on your team,” Hynes says. “You also get graded by your team; that’s about 10 percent of your grade. We try to encourage people to be proactive and supportive teammates.”

Students say the process is fast-paced but rewarding.

“It’s definitely demanding,” says Sofie Netteberg, a graduate student who is also in the LGO program at MIT. “Depending on where you’re at with your technology, you can be moving very quickly. That’s the stage that I was in, which I found really engaging. We basically just had a lab technology, and it was like, ‘What do we do next?’ You also get a ton of support from the professors.”

From the classroom to the world

This fall’s final presentations took place at the headquarters of the MIT-affiliated venture firm The Engine in front of an audience of professors, investors, members of foundations supporting entrepreneurship, and more.

“We got to hear feedback from people who would be the real next step for the technology if the startup gets up and running,” said Johnson, whose team was commercializing a method for storing energy in concrete. “That was really valuable. We know that these are not only people we might see in the next month or the next funding rounds, but they’re also exactly the type of people that are going to give us the questions we should be thinking about. It was clarifying.”

Throughout the semester, students treated the project like a real venture they’d be working on well beyond the length of the class.

“No one’s really thinking about this class for the grade; it’s about the learning,” says Netteberg, whose team was encouraged to keep working on their electrolyzer technology designed to more efficiently produce green hydrogen. “We’re not stressed about getting an A. If we want to keep working on this, we want real feedback: What do you think we did well? What do we need to keep working on?”

Hynes says several investors expressed interest in supporting the businesses coming out of the class. Moving forward, he hopes students embrace the test-bed environment his team has created for them and try bold new things.

“People have been very pragmatic over the years, which is good, but also potentially limiting,” Hynes says. “This is also an opportunity to do something that’s a little further out there — something that has really big potential impact if it comes together. This is the time where students get to experiment, so why not try something big?”

Robots have come a long way since the Roomba. Today, drones are starting to deliver door to door, self-driving cars are navigating some roads, robo-dogs are aiding first responders, and still more bots are doing backflips and helping out on the factory floor. Still, Luca Carlone thinks the best is yet to come.

Carlone, who recently received tenure as an associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), directs the SPARK Lab, where he and his students are bridging a key gap between humans and robots: perception. The group does theoretical and experimental research, all toward expanding a robot’s awareness of its environment in ways that approach human perception. And perception, as Carlone often says, is more than detection.

While robots have grown by leaps and bounds in terms of their ability to detect and identify objects in their surroundings, they still have a lot to learn when it comes to making higher-level sense of their environment. As humans, we perceive objects with an intuitive sense of not just of their shapes and labels but also their physics — how they might be manipulated and moved — and how they relate to each other, their larger environment, and ourselves.

That kind of human-level perception is what Carlone and his group are hoping to impart to robots, in ways that enable them to safely and seamlessly interact with people in their homes, workplaces, and other unstructured environments.

Since joining the MIT faculty in 2017, Carlone has led his team in developing and applying perception and scene-understanding algorithms for various applications, including autonomous underground search-and-rescue vehicles, drones that can pick up and manipulate objects on the fly, and self-driving cars. They might also be useful for domestic robots that follow natural language commands and potentially even anticipate human’s needs based on higher-level contextual clues.

“Perception is a big bottleneck toward getting robots to help us in the real world,” Carlone says. “If we can add elements of cognition and reasoning to robot perception, I believe they can do a lot of good.”

Expanding horizons

Carlone was born and raised near Salerno, Italy, close to the scenic Amalfi coast, where he was the youngest of three boys. His mother is a retired elementary school teacher who taught math, and his father is a retired history professor and publisher, who has always taken an analytical approach to his historical research. The brothers may have unconsciously adopted their parents’ mindsets, as all three went on to be engineers — the older two pursued electronics and mechanical engineering, while Carlone landed on robotics, or mechatronics, as it was known at the time.

He didn’t come around to the field, however, until late in his undergraduate studies. Carlone attended the Polytechnic University of Turin, where he focused initially on theoretical work, specifically on control theory — a field that applies mathematics to develop algorithms that automatically control the behavior of physical systems, such as power grids, planes, cars, and robots. Then, in his senior year, Carlone signed up for a course on robotics that explored advances in manipulation and how robots can be programmed to move and function.

“It was love at first sight. Using algorithms and math to develop the brain of a robot and make it move and interact with the environment is one of the most fulfilling experiences,” Carlone says. “I immediately decided this is what I want to do in life.”

He went on to a dual-degree program at the Polytechnic University of Turin and the Polytechnic University of Milan, where he received master’s degrees in mechatronics and automation engineering, respectively. As part of this program, called the Alta Scuola Politecnica, Carlone also took courses in management, in which he and students from various academic backgrounds had to team up to conceptualize, build, and draw up a marketing pitch for a new product design. Carlone’s team developed a touch-free table lamp designed to follow a user’s hand-driven commands. The project pushed him to think about engineering from different perspectives.

“It was like having to speak different languages,” he says. “It was an early exposure to the need to look beyond the engineering bubble and think about how to create technical work that can impact the real world.”

The next generation

Carlone stayed in Turin to complete his PhD in mechatronics. During that time, he was given freedom to choose a thesis topic, which he went about, as he recalls, “a bit naively.”

“I was exploring a topic that the community considered to be well-understood, and for which many researchers believed there was nothing more to say.” Carlone says. “I underestimated how established the topic was, and thought I could still contribute something new to it, and I was lucky enough to just do that.”

The topic in question was “simultaneous localization and mapping,” or SLAM — the problem of generating and updating a map of a robot’s environment while simultaneously keeping track of where the robot is within that environment. Carlone came up with a way to reframe the problem, such that algorithms could generate more precise maps without having to start with an initial guess, as most SLAM methods did at the time. His work helped to crack open a field where most roboticists thought one could not do better than the existing algorithms.

“SLAM is about figuring out the geometry of things and how a robot moves among those things,” Carlone says. “Now I’m part of a community asking, what is the next generation of SLAM?”

In search of an answer, he accepted a postdoc position at Georgia Tech, where he dove into coding and computer vision — a field that, in retrospect, may have been inspired by a brush with blindness: As he was finishing up his PhD in Italy, he suffered a medical complication that severely affected his vision.

“For one year, I could have easily lost an eye,” Carlone says. “That was something that got me thinking about the importance of vision, and artificial vision.”

He was able to receive good medical care, and the condition resolved entirely, such that he could continue his work. At Georgia Tech, his advisor, Frank Dellaert, showed him ways to code in computer vision and formulate elegant mathematical representations of complex, three-dimensional problems. His advisor was also one of the first to develop an open-source SLAM library, called GTSAM, which Carlone quickly recognized to be an invaluable resource. More broadly, he saw that making software available to all unlocked a huge potential for progress in robotics as a whole.

“Historically, progress in SLAM has been very slow, because people kept their codes proprietary, and each group had to essentially start from scratch,” Carlone says. “Then open-source pipelines started popping up, and that was a game changer, which has largely driven the progress we have seen over the last 10 years.”

Spatial AI

Following Georgia Tech, Carlone came to MIT in 2015 as a postdoc in the Laboratory for Information and Decision Systems (LIDS). During that time, he collaborated with Sertac Karaman, professor of aeronautics and astronautics, in developing software to help palm-sized drones navigate their surroundings using very little on-board power. A year later, he was promoted to research scientist, and then in 2017, Carlone accepted a faculty position in AeroAstro.

“One thing I fell in love with at MIT was that all decisions are driven by questions like: What are our values? What is our mission? It’s never about low-level gains. The motivation is really about how to improve society,” Carlone says. “As a mindset, that has been very refreshing.”

Today, Carlone’s group is developing ways to represent a robot’s surroundings, beyond characterizing their geometric shape and semantics. He is utilizing deep learning and large language models to develop algorithms that enable robots to perceive their environment through a higher-level lens, so to speak. Over the last six years, his lab has released more than 60 open-source repositories, which are used by thousands of researchers and practitioners worldwide. The bulk of his work fits into a larger, emerging field known as “spatial AI.”

“Spatial AI is like SLAM on steroids,” Carlone says. “In a nutshell, it has to do with enabling robots to think and understand the world as humans do, in ways that can be useful.”

It’s a huge undertaking that could have wide-ranging impacts, in terms of enabling more intuitive, interactive robots to help out at home, in the workplace, on the roads, and in remote and potentially dangerous areas. Carlone says there will be plenty of work ahead, in order to come close to how humans perceive the world.

“I have 2-year-old twin daughters, and I see them manipulating objects, carrying 10 different toys at a time, navigating across cluttered rooms with ease, and quickly adapting to new environments. Robot perception cannot yet match what a toddler can do,” Carlone says. “But we have new tools in the arsenal. And the future is bright.”

The MIT Press has announced that Direct to Open (D2O) will open access to over 80 new monographs and edited book collections in the spring and fall publishing seasons, after reaching its full funding goal for 2025.

“It has been one of the greatest privileges of my career to contribute to this program and demonstrate that our academic community can unite to publish high-quality open-access monographs at scale,” says Amy Harris, senior manager of library relations and sales at the MIT Press. “We are deeply grateful to all of the consortia that have partnered with us and to the hundreds of libraries that have invested in this program. Together, we are expanding the public knowledge commons in ways that benefit scholars, the academy, and readers around the world.”

Among the highlights from the MIT Press’s fourth D2O funding cycle is a new three-year, consortium-wide commitment from the Florida Virtual Campus (FLVC) and a renewed three-year commitment from the Big Ten Academic Alliance (BTAA). These long-term collaborations will play a pivotal role in supporting the press’s open-access efforts for years to come.

“The Florida Virtual Campus is honored to participate in D2O in order to provide this collection of high-quality scholarship to more than 1.2 million students and faculty at the 28 state colleges and 12 state universities of Florida,” says Elijah Scott, executive director of library services for the Florida Virtual Campus. “The D2O program allows FLVC to make this research collection available to our member libraries while concurrently fostering the larger global aspiration of sustainable and equitable access to information.”

“The libraries of the Big Ten Academic Alliance are committed to supporting the creation of open-access content,” adds Kate McCready, program director for open publishing at the Big Ten Academic Alliance Library. “We're thrilled that our participation in D2O contributes to the opening of this collection, as well as championing the exploration of new models for opening scholarly monographs.”

In 2025, hundreds of libraries renewed their support thanks to the teams at consortia around the world, including the Council of Australasian University Librarians, the CBB Library Consortium, the California Digital Library, the Canadian Research Knowledge Network, CRL/NERL, the Greater Western Library Alliance, Jisc, Lyrasis, MOBIUS, PALCI, SCELC, and the Tri-College Library Consortium.

Launched in 2021, D2O is an innovative sustainable framework for open-access monographs that shifts publishing from a solely market-based, purchase model where individuals and libraries buy single e-books, to a collaborative, library-supported open-access model. 

Many other models offer open-access opportunities on a title-by-title basis or within specific disciplines. D2O’s particular advantage is that it enables a press to provide open access to its entire list of scholarly books at scale, embargo-free, during each funding cycle. Thanks to D2O, all MIT Press monograph authors have the opportunity for their work to be published open access, with equal support to traditionally underserved and underfunded disciplines in the social sciences and humanities.  

The MIT Press will now turn its attention to its fifth funding cycle and invites libraries and library consortia to participate. For details, please visit the MIT Press website or contact the Library Relations team.

Melissa Smith PhD ’12 is an associate leader in the Advanced Materials and Microsystems Group at MIT Lincoln Laboratory. Her team, which is embedded within the laboratory’s Advanced Technology Division, drives innovation in fields including computation, aerospace, optical systems, and bioengineering by applying micro- and nanofabrication techniques. Smith, an inventor of 11 patents, strongly believes in the power of collaboration when it comes to her own work, the work of her Lincoln Laboratory colleagues, and the innovative research done by MIT professors and students. 

Lincoln Laboratory researches and develops advanced technologies in support of national security. Research done at the laboratory is applied, meaning staff members are given a specific problem to solve by a deadline. Divisions within the laboratory are made up of technical experts, ranging from biologists to cybersecurity researchers, working on different projects simultaneously. Smith appreciates the broad application space of her group’s work, which feeds into programs across the laboratory. “We are like a kitchen drawer full of indispensable gadgets,” she says, some of which are used to develop picosatellites, smart textiles, or microrobots. Their position as a catch-all team makes their work fun, somewhat open-ended, and always interesting.

In 2012, Smith received her PhD from the MIT Department of Materials Science & Engineering (DMSE). After graduation, she remained at the Institute for nine months as a postdoc before beginning her career as an engineer at IBM. While at IBM, Smith maintained a research affiliation with MIT to continue to work on patents and write papers. In 2015, she formally returned to MIT as a technical staff member at Lincoln Laboratory. In 2020, she was promoted to the position of assistant group leader and was awarded the laboratory’s Best Invention Award for “Electrospray devices and methods for fabricating electrospray devices” (U.S. Patent 11,708,182 B2). In 2024, she was promoted to associate group leader. 

Management is an important aspect of Smith’s role, and she credits the laboratory for cultivating people with both academic and technical backgrounds to learn how to effectively run programs and teams. Her demonstrated efficacy in the academic and corporate spaces — both of which contain deadlines and collaborative work — allows her to inspire her team to be innovative and efficient. She keeps her group running smoothly by removing potential roadblocks so they can adequately attend to their projects. Smith focuses on specific tasks that aid in her group’s success, including writing grant proposals, a skill she learned while working at the laboratory, which allows her staff to prioritize their technical work. That, she says, is the value of working as a team.

A true champion of teamwork, Smith advises new staff members to maintain an open mind because they can learn something from everyone they encounter, especially when first starting at the Institute. She notes that every colleague has something unique to offer, and taking time to understand the wealth of experience and knowledge around you will only help you succeed as a staff member at MIT. “Be who you are, do what you do, and run with it,” she says. 

Soundbytes 

Q: What project at MIT are you the proudest of?

Smith: We are building a wafer-scale satellite, which is a little bit out-there as an idea. It was thought up in the 1960s, but the technology wasn't to the point where it could be realized. Technology today is more than capable of making this small space microsystem. I was tasked with taking the idea further. Some people say that it is impossible, and for a lot of good reasons! Slowly addressing the technical issues to the point where people now say, “Oh, you could probably do this,” is exciting.

I never want to be someone who thinks something is impossible. I'll say, “I can't do it, but maybe somebody else can,” and I will also add, “Here is what I tried, here is all the data, and here is how I came to the point where I got stuck.” I like taking something that was initially met with disbelief and rendering it. Lincoln Laboratory is active with professors and students. I am collaborating with students from the Department of Aeronautics and Astronautics on the project, and we now have a patent on the technology that came from it. I am happy to have students assist, write papers, and occasionally get their names on patents. It is seeding additional innovation. We don't have the system quite yet, but I've converted a few skeptics!

Q: What are your favorite campus memories from when you were a student?

Smith: When I was a graduate student, I would go with friends to the Muddy Charles Pub in Walker Memorial. One of the things I really enjoy about Walker Memorial is the prime view over the Charles River, and I remember staring out of the windows at the top of Walker Memorial after exams. Also, during Independent Activities Period I learned how to snowboard. I'm from Illinois where there are no mountains. When I came to the East Coast and saw that there were a lot of mountains with people strapping metal to their feet in the snow, I thought, “OK, let's try it.” I love snowboarding to this day. MIT has this kind of unfettered freedom in a way that, even beyond the technical stuff, people can try things from a personal standpoint they maybe wouldn’t have tried somewhere else. 

Q: What do you like the most about the culture at MIT?

Smith: We help people grow professionally. The staff here are above average in terms of capability in what they do. When I interviewed for my job, I asked where people work when they leave MIT. People move on to other labs like the Jet Propulsion Laboratory or companies like Raytheon, they become professors, or they start their own companies. I make sure that people are learning what they want to do with their careers while they work at the laboratory. That is the cultural overlay that exists on campus. When I was a student, I interned at John Deere, 3M, Xerox, and IBM and saw how they are innovative in their own ways that define their corporate cultures. At MIT, you are supported to explore and play. At Lincoln Laboratory people are not pigeonholed into a particular role. If you have an idea, you are encouraged to explore it, as long as it aligns with the mission. There is a specific freedom you can experience at MIT that is above and beyond a typical academic environment.

Gerald E. Schneider, a professor emeritus of psychology and member of the MIT community for over 60 years, passed away on Dec. 11, 2024. He was 84.

Schneider was an authority on the relationships between brain structure and behavior, concentrating on neuronal development, regeneration or altered growth after brain injury, and the behavioral consequences of altered connections in the brain.

Using the Syrian golden hamster as his test subject of choice, Schneider made numerous contributions to the advancement of neuroscience. He laid out the concept of two visual systems — one for locating objects and one for the identification of objects — in a 1969 issue of Science, a milestone in the study of brain-behavior relationships. In 1973, he described a “pruning effect” in the optic tract axons of adult hamsters who had brain lesions early in life. In 2006, his lab reported a previously undiscovered nanobiomedical technology for tissue repair and restoration in Biological Sciences. The paper showed how a designed self-assembling peptide nanofiber scaffold could create a permissive environment for axons, not only to regenerate through the site of an acute injury in the optic tract of hamsters, but also to knit the brain tissue together.

His work shaped the research and thinking of numerous colleagues and trainees. Mriganka Sur, the Newton Professor of Neuroscience and former Department of Brain and Cognitive Sciences (BCS) department head, recalls how Schneider’s paper, “Is it really better to have your brain lesion early? A revision of the ‘Kennard Principle,’” published in 1979 in the journal Neuropsychologia, influenced his work on rewiring retinal projections to the auditory thalamus, which was used to derive principles of functional plasticity in the cortex.

“Jerry was an extremely innovative thinker. His hypothesis of two visual systems — for detailed spatial processing and for movement processing — based on his analysis of visual pathways in hamsters presaged and inspired later work on form and motion pathways in the primate brain,” Sur says. “His description of conservation of axonal arbor during development laid the foundation for later ideas about homeostatic mechanisms that co-regulate neuronal plasticity.”

Institute Professor Ann Graybiel was a colleague of Schneider’s for over five decades. She recalls early in her career being asked by Schneider to help make a map of the superior colliculus.

“I took it as an honor to be asked, and I worked very hard on this, with great excitement. It was my first such mapping, to be followed by much more in the future,” Graybiel recalls. “Jerry was fascinated by animal behavior, and from early on he made many discoveries using hamsters as his main animals of choice. He found that they could play. He found that they could operate in ways that seemed very sophisticated. And, yes, he mapped out pathways in their brains.”

Schneider was raised in Wheaton, Illinois, and graduated from Wheaton College in 1962 with a degree in physics. He was recruited to MIT by Hans-Lukas Teuber, one of the founders of the Department of Psychology, which eventually became the Department of Brain and Cognitive Sciences. Walle Nauta, another founder of the department, taught Schneider neuroanatomy. The pair were deeply influential in shaping his interests in neuroscience and his research.

“He admired them both very much and was very attached to them,” his daughter, Nimisha Schneider, says. “He was an interdisciplinary scholar and he liked that aspect of neuroscience, and he was fascinated by the mysteries of the human brain.”

Shortly after completing his PhD in psychology in 1966, he was hired as an assistant professor in 1967. He was named an associate professor in 1970, received tenure in 1975, and was appointed a full professor in 1977.

After his retirement in 2017, Schneider remained involved with the Department of BCS. Professor Pawan Sinha brought Schneider to campus for what would be his last on-campus engagement, as part of the “SilverMinds Series,” an initiative in the Sinha Lab to engage with scientists now in their “silver years.”

Schneider’s research made an indelible impact on Sinha, beginning as a graduate student when he was inspired by Schneider’s work linking brain structure and function. His work on nerve regeneration, which merged fundamental science and real-world impact, served as a “North Star” that guided Sinha’s own work as he established his lab as a junior faculty member.

“Even through the sadness of his loss, I am grateful for the inspiring example he has left for us of a life that so seamlessly combined brilliance, kindness, modesty, and tenacity,” Sinha says. “He will be missed.”

Schneider’s life centered around his research and teaching, but he also had many other skills and hobbies. Early in his life, he enjoyed painting, and as he grew older he was drawn to poetry. He was also skilled in carpentry and making furniture. He built the original hamster cages for his lab himself, along with numerous pieces of home furniture and shelving. He enjoyed nature anywhere it could be found, from the bees in his backyard to hiking and visiting state and national parks.

He was a Type 1 diabetic, and at the time of his death, he was nearing the completion of a book on the effects of hypoglycemia on the brain, which his family hopes to have published in the future. He was also the author of “Brain Structure and Its Origins,” published in 2014 by MIT Press.

He is survived by his wife, Aiping; his children, Cybele, Aniket, and Nimisha; and step-daughter Anna. He was predeceased by a daughter, Brenna. He is also survived by eight grandchildren and 10 great-grandchildren. A memorial in his honor was held on Jan. 11 at Saint James Episcopal Church in Cambridge.