In February 2024, Reddit struck a $60 million deal with Google to let the search giant use data on the platform to train its artificial intelligence models. Notably absent from the discussions were Reddit users, whose data were being sold.

The deal reflected the reality of the modern internet: Big tech companies own virtually all our online data and get to decide what to do with that data. Unsurprisingly, many platforms monetize their data, and the fastest-growing way to accomplish that today is to sell it to AI companies, who are themselves massive tech companies using the data to train ever more powerful models.

The decentralized platform Vana, which started as a class project at MIT, is on a mission to give power back to the users. The company has created a fully user-owned network that allows individuals to upload their data and govern how they are used. AI developers can pitch users on ideas for new models, and if the users agree to contribute their data for training, they get proportional ownership in the models.

The idea is to give everyone a stake in the AI systems that will increasingly shape our society while also unlocking new pools of data to advance the technology.

“This data is needed to create better AI systems,” says Vana co-founder Anna Kazlauskas ’19. “We’ve created a decentralized system to get better data — which sits inside big tech companies today — while still letting users retain ultimate ownership.”

From economics to the blockchain

A lot of high school students have pictures of pop stars or athletes on their bedroom walls. Kazlauskas had a picture of former U.S. Treasury Secretary Janet Yellen.

Kazlauskas came to MIT sure she’d become an economist, but she ended up being one of five students to join the MIT Bitcoin club in 2015, and that experience led her into the world of blockchains and cryptocurrency.

From her dorm room in MacGregor House, she began mining the cryptocurrency Ethereum. She even occasionally scoured campus dumpsters in search of discarded computer chips.

“It got me interested in everything around computer science and networking,” Kazlauskas says. “That involved, from a blockchain perspective, distributed systems and how they can shift economic power to individuals, as well as artificial intelligence and econometrics.”

Kazlauskas met Art Abal, who was then attending Harvard University, in the former Media Lab class Emergent Ventures, and the pair decided to work on new ways to obtain data to train AI systems.

“Our question was: How could you have a large number of people contributing to these AI systems using more of a distributed network?” Kazlauskas recalls.

Kazlauskas and Abal were trying to address the status quo, where most models are trained by scraping public data on the internet. Big tech companies often also buy large datasets from other companies.

The founders’ approach evolved over the years and was informed by Kazlauskas’ experience working at the financial blockchain company Celo after graduation. But Kazlauskas credits her time at MIT with helping her think about these problems, and the instructor for Emergent Ventures, Ramesh Raskar, still helps Vana think about AI research questions today.

“It was great to have an open-ended opportunity to just build, hack, and explore,” Kazlauskas says. “I think that ethos at MIT is really important. It’s just about building things, seeing what works, and continuing to iterate.”

Today Vana takes advantage of a little-known law that allows users of most big tech platforms to export their data directly. Users can upload that information into encrypted digital wallets in Vana and disburse it to train models as they see fit.

AI engineers can suggest ideas for new open-source models, and people can pool their data to help train the model. In the blockchain world, the data pools are called data DAOs, which stands for decentralized autonomous organization. Data can also be used to create personalized AI models and agents.

In Vana, data are used in a way that preserves user privacy because the system doesn’t expose identifiable information. Once the model is created, users maintain ownership so that every time it’s used, they’re rewarded proportionally based on how much their data helped trained it.

“From a developer’s perspective, now you can build these hyper-personalized health applications that take into account exactly what you ate, how you slept, how you exercise,” Kazlauskas says. “Those applications aren’t possible today because of those walled gardens of the big tech companies.”

Crowdsourced, user-owned AI

Last year, a machine-learning engineer proposed using Vana user data to train an AI model that could generate Reddit posts. More than 140,000 Vana users contributed their Reddit data, which contained posts, comments, messages, and more. Users decided on the terms in which the model could be used, and they maintained ownership of the model after it was created.

Vana has enabled similar initiatives with user-contributed data from the social media platform X; sleep data from sources like Oura rings; and more. There are also collaborations that combine data pools to create broader AI applications.

“Let’s say users have Spotify data, Reddit data, and fashion data,” Kazlauskas explains. “Usually, Spotify isn’t going to collaborate with those types of companies, and there’s actually regulation against that. But users can do it if they grant access, so these cross-platform datasets can be used to create really powerful models.”

Vana has over 1 million users and over 20 live data DAOs. More than 300 additional data pools have been proposed by users on Vana’s system, and Kazlauskas says many will go into production this year.

“I think there’s a lot of promise in generalized AI models, personalized medicine, and new consumer applications, because it’s tough to combine all that data or get access to it in the first place,” Kazlauskas says.

The data pools are allowing groups of users to accomplish something even the most powerful tech companies struggle with today.

“Today, big tech companies have built these data moats, so the best datasets aren’t available to anyone,” Kazlauskas says. “It’s a collective action problem, where my data on its own isn’t that valuable, but a data pool with tens of thousands or millions of people is really valuable. Vana allows those pools to be built. It’s a win-win: Users get to benefit from the rise of AI because they own the models. Then you don’t end up in scenario where you don’t have a single company controlling an all-powerful AI model. You get better technology, but everyone benefits.”

The MIT School of Science welcomes Jess Speedie, one of eight recipients of the 2025 51 Pegasi b Fellowship. The announcement was made March 27 by the Heising-Simons Foundation.

The 51 Pegasi b Fellowship, named after the first exoplanet discovered orbiting a sun-like star, was established in 2017 to provide postdocs with the opportunity to conduct theoretical, observational, and experimental research in planetary astronomy.

Speedie, who expects to complete her PhD in astronomy at the University of Victoria, Canada, this summer, will be hosted by the Department of Earth, Atmospheric and Planetary Sciences (EAPS). She will be mentored by Kerr-McGee Career Development Professor Richard Teague as she uses a combination of observational data and simulations to study the birth of planets and the processes of planetary formation.

“The planetary environment is where all the good stuff collects … it has the greatest potential for the most interesting things in the universe to happen, such as the origin of life,” she says. “Planets, for me, are where the stories happen.”

Speedie’s work has focused on understanding “cosmic nurseries” and the detection and characterization of the youngest planets in the galaxy. A lot of this work has made use of the Atacama Large Millimeter/submillimeter Array (ALMA), located in northern Chile. Made up of a collection of 66 parabolic dishes, ALMA studies the universe with radio wavelengths, and Speedie has developed a novel approach to find signals in the data of gravitational instability in protoplanetary disks, a method of planetary formation.

“One of the big, big questions right now in the community focused on planet formation is, where are the planets? It is that simple. We think they’re developing in these disks, but we’ve detected so few of them,” she says.

While working as a fellow, Speedie is aiming to develop an algorithm that carefully aligns and stacks a decade of ALMA observational data to correct for a blurring effect that happens when combining images captured at different times. Doing so should produce the sharpest, most sensitive images of early planetary systems to date.

She is also interested in studying infant planets, especially ones that may be forming in disks around protoplanets, rather than stars. Modeling how these ingredient materials in orbit behave could give astronomers a way to measure the mass of young planets.

“What’s exciting is the potential for discovery. I have this sense that the universe as a whole is infinitely more creative than human minds — the kinds of things that happen out there, you can’t make that up. It’s better than science fiction,” she says.

The other 51 Pegasi b Fellows and their host institutions this year are Nick Choksi (Caltech), Yan Liang (Yale University), Sagnick Mukherjee (Arizona State University), Matthew Nixon (Arizona State University), Julia Santos (Harvard University), Nour Skaf (University of Hawaii), and Jerry Xuan (University of California at Los Angeles).

The fellowship provides up to $450,000 of support over three years for independent research, a generous salary and discretionary fund, mentorship at host institutions, an annual summit to develop professional networks and foster collaboration, and an option to apply for another grant to support a future position in the United States.

When major disasters hit and structures collapse, people can become trapped under rubble. Extricating victims from these hazardous environments can be dangerous and physically exhausting. To help rescue teams navigate these structures, MIT Lincoln Laboratory, in collaboration with researchers at the University of Notre Dame, developed the Soft Pathfinding Robotic Observation Unit (SPROUT). SPROUT is a vine robot — a soft robot that can grow and maneuver around obstacles and through small spaces. First responders can deploy SPROUT under collapsed structures to explore, map, and find optimum ingress routes through debris. 

"The urban search-and-rescue environment can be brutal and unforgiving, where even the most hardened technology struggles to operate. The fundamental way a vine robot works mitigates a lot of the challenges that other platforms face," says Chad Council, a member of the SPROUT team, which is led by Nathaniel Hanson. The program is conducted out of the laboratory's Human Resilience Technology Group. 

First responders regularly integrate technology, such as cameras and sensors, into their workflows to understand complex operating environments. However, many of these technologies have limitations. For example, cameras specially built for search-and-rescue operations can only probe on a straight path inside of a collapsed structure. If a team wants to search further into a pile, they need to cut an access hole to get to the next area of the space. Robots are good for exploring on top of rubble piles, but are ill-suited for searching in tight, unstable structures and costly to repair if damaged. The challenge that SPROUT addresses is how to get under collapsed structures using a low-cost, easy-to-operate robot that can carry cameras and sensors and traverse winding paths. 

SPROUT is composed of an inflatable tube made of airtight fabric that unfurls from a fixed base. The tube inflates with air, and a motor controls its deployment. As the tube extends into rubble, it can flex around corners and squeeze through narrow passages. A camera and other sensors mounted to the tip of the tube image and map the environment the robot is navigating. An operator steers SPROUT with joysticks, watching a screen that displays the robot's camera feed. Currently, SPROUT can deploy up to 10 feet, and the team is working on expanding it to 25 feet.

When building SPROUT, the team overcame a number of challenges related to the robot's flexibility. Because the robot is made of a deformable material that bends at many points, determining and controlling the robot's shape as it unfurls through the environment is difficult — think of trying to control an expanding wiggly sprinkler toy. Pinpointing how to apply air pressure within the robot so that steering is as simple as pointing the joystick forward to make the robot move forward was essential for system adoption by emergency responders. In addition, the team had to design the tube to minimize friction while the robot grows and engineer the controls for steering.

While a teleoperated system is a good starting point for assessing the hazards of void spaces, the team is also finding new ways to apply robot technologies to the domain, such as using data captured by the robot to build maps of the subsurface voids. "Collapse events are rare but devastating events. In robotics, we would typically want ground truth measurements to validate our approaches, but those simply don't exist for collapsed structures," Hanson says. To solve this problem, Hanson and his team made a simulator that allows them to create realistic depictions of collapsed structures and develop algorithms that map void spaces.

SPROUT was developed in collaboration with Margaret Coad, a professor at the University of Notre Dame and an MIT graduate. When looking for collaborators, Hanson — a graduate of Notre Dame — was already aware of Coad's work on vine robots for industrial inspection. Coad's expertise, together with the laboratory's experience in engineering, strong partnership with urban search-and-rescue teams, and ability to develop fundamental technologies and prepare them for  transition to industry, "made this a really natural pairing to join forces and work on research for a traditionally underserved community," Hanson says. "As one of the primary inventors of vine robots, Professor Coad brings invaluable expertise on the fabrication and modeling of these robots."

Lincoln Laboratory tested SPROUT with first responders at the  Massachusetts Task Force 1  training site in Beverly, Massachusetts. The tests allowed the researchers to improve the durability and portability of the robot and learn how to grow and steer the robot more efficiently. The team is planning a larger field study this spring.

"Urban search-and-rescue teams and first responders serve critical roles in their communities but typically have little-to-no research and development budgets," Hanson says. "This program has enabled us to push the technology readiness level of vine robots to a point where responders can engage with a hands-on demonstration of the system."

Sensing in constrained spaces is not a problem unique to disaster response communities, Hanson adds. The team envisions the technology being used in the maintenance of military systems or critical infrastructure with difficult-to-access locations.

The initial program focused on mapping void spaces, but future work aims to localize hazards and assess the viability and safety of operations through rubble. "The mechanical performance of the robots has an immediate effect, but the real goal is to rethink the way sensors are used to enhance situational awareness for rescue teams," says Hanson. "Ultimately, we want SPROUT to provide a complete operating picture to teams before anyone enters a rubble pile." 

C. Cem Tasan has been appointed director of MIT’s Materials Research Laboratory (MRL), effective March 15. The POSCO Associate Professor of Metallurgy in the Department of Materials Science and Engineering (DMSE), Tasan succeeds Lionel “Kim” Kimerling, who has held the post of interim director since Carl Thompson stepped down in August 2023.

“MRL is a strategic asset for MIT, and Cem has a clear vision to build upon the lab’s engagement with materials researchers across the breadth of the Institute as well as with external collaborators and sponsors,” wrote Vice President for Research Ian Waitz, in a letter announcing the appointment.

The MRL is a leading interdisciplinary center dedicated to materials science and engineering. As a hub for innovation, the MRL unites researchers across disciplines, fosters industry and government partnerships, and drives advancements that shape the future of technology. Through groundbreaking research, the MRL supports MIT’s mission to advance science and technology for the benefit of society, enabling discoveries that have a lasting impact across industries and everyday life.

“MRL has a position at the core of materials research activities across departments at MIT,” Tasan says. “It can only grow from where it is, right in the heart of the Institute’s innovative hub.”

As director, Tasan will lead MRL’s research mission, with a view to strengthening internal collaboration and building upon the interdisciplinary laboratory’s long history of industry engagement. He will also take on responsibility for the management of Building 13, the Vannevar Bush Building, which houses key research facilities and labs.

“MRL is in very good hands with Cem Tasan’s leadership,” says Kimerling, the outgoing interim director. “His vision for a united MIT materials community whose success is stimulated by the convergence of basic science and engineering solutions provides the nutrition for MIT’s creative relevance to society. His collegial nature, motivating energy, and patient approach will make it happen.”

Tasan is a metallurgist with expertise in the fracture in metals and the design of damage-resistant alloys. Among other advances, his lab has demonstrated a multiscale means of designing high-strength/high-ductility titanium alloys; and explained the stress intensification mechanism by which human hair damages hard steel razors, pointing the way to stronger and longer-lasting blades.

“We need better materials that operate in more and more extreme conditions, for almost all of our critical industries and applications,” says Tasan. “Materials research in MRL identifies interdisciplinary pathways to address this important challenge.” 

He studied in Turkey and the Netherlands, earning his PhD at Eindhoven University of Technology before spending several years leading a research group at the Max Planck Institute for Sustainable Materials in Germany. He joined the MIT faculty in 2016 and earned tenure in 2022.

“Cem has led one of the major collaborative research teams at MRL, and he expects to continue developing a strong community among the MIT materials research faculty,” wrote Waitz in his letter on March 14.

The MRL was established in 2017 through the merger of the MIT Materials Processing Center (MPC) and the Center for Materials Science and Engineering. This unification aimed to strengthen MIT’s leadership in materials research by fostering interdisciplinary collaboration and advancing breakthroughs in areas such as energy conversion, quantum materials, and materials sustainability.

From 2008 to 2017, Thompson, the Stavros Salapatas Professor of Materials Science and Engineering, served as director of the MPC. During his tenure, he played a crucial role in expanding materials research and building partnerships with industry, government agencies, and academic institutions. With the formation of the MRL in 2017, Thompson was appointed its inaugural director, guiding the new laboratory to prominence as a hub for cutting-edge materials science. He stepped down from this role in August 2023.

At that time, Kimerling stepped in to serve as interim director of MRL. He brought special knowledge of the lab’s history, having served as director of the MPC from 1993 to 2008, transforming it into a key industry-academic interface. Under his leadership, the MPC became a crucial gateway for industry partners to collaborate with MIT faculty across materials-related disciplines, bridging fundamental research with industrial applications. His vision helped drive technological innovation and economic development by aligning academic expertise with industry needs. As interim director of MRL these past 18 months, Kimerling has ensured continuity in leadership.

“I’m delighted that Cem will be the next MRL director,” says Thompson. “He’s a great fit. He has been affiliated with MPC, and then MRL, since the beginning of his faculty career at MIT. He’s also played a key role in leading a renaissance in physical metallurgy at MIT and has many close ties to industry.”

Imagine a coffee company trying to optimize its supply chain. The company sources beans from three suppliers, roasts them at two facilities into either dark or light coffee, and then ships the roasted coffee to three retail locations. The suppliers have different fixed capacity, and roasting costs and shipping costs vary from place to place.

The company seeks to minimize costs while meeting a 23 percent increase in demand.

Wouldn’t it be easier for the company to just ask ChatGPT to come up with an optimal plan? In fact, for all their incredible capabilities, large language models (LLMs) often perform poorly when tasked with directly solving such complicated planning problems on their own.

Rather than trying to change the model to make an LLM a better planner, MIT researchers took a different approach. They introduced a framework that guides an LLM to break down the problem like a human would, and then automatically solve it using a powerful software tool.

A user only needs to describe the problem in natural language — no task-specific examples are needed to train or prompt the LLM. The model encodes a user’s text prompt into a format that can be unraveled by an optimization solver designed to efficiently crack extremely tough planning challenges.

During the formulation process, the LLM checks its work at multiple intermediate steps to make sure the plan is described correctly to the solver. If it spots an error, rather than giving up, the LLM tries to fix the broken part of the formulation.

When the researchers tested their framework on nine complex challenges, such as minimizing the distance warehouse robots must travel to complete tasks, it achieved an 85 percent success rate, whereas the best baseline only achieved a 39 percent success rate.

The versatile framework could be applied to a range of multistep planning tasks, such as scheduling airline crews or managing machine time in a factory.

“Our research introduces a framework that essentially acts as a smart assistant for planning problems. It can figure out the best plan that meets all the needs you have, even if the rules are complicated or unusual,” says Yilun Hao, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS) and lead author of a paper on this research.

She is joined on the paper by Yang Zhang, a research scientist at the MIT-IBM Watson AI Lab; and senior author Chuchu Fan, an associate professor of aeronautics and astronautics and LIDS principal investigator. The research will be presented at the International Conference on Learning Representations.

Optimization 101

The Fan group develops algorithms that automatically solve what are known as combinatorial optimization problems. These vast problems have many interrelated decision variables, each with multiple options that rapidly add up to billions of potential choices.

Humans solve such problems by narrowing them down to a few options and then determining which one leads to the best overall plan. The researchers’ algorithmic solvers apply the same principles to optimization problems that are far too complex for a human to crack.

But the solvers they develop tend to have steep learning curves and are typically only used by experts.

“We thought that LLMs could allow nonexperts to use these solving algorithms. In our lab, we take a domain expert’s problem and formalize it into a problem our solver can solve. Could we teach an LLM to do the same thing?” Fan says.

Using the framework the researchers developed, called LLM-Based Formalized Programming (LLMFP), a person provides a natural language description of the problem, background information on the task, and a query that describes their goal.

Then LLMFP prompts an LLM to reason about the problem and determine the decision variables and key constraints that will shape the optimal solution.

LLMFP asks the LLM to detail the requirements of each variable before encoding the information into a mathematical formulation of an optimization problem. It writes code that encodes the problem and calls the attached optimization solver, which arrives at an ideal solution.

“It is similar to how we teach undergrads about optimization problems at MIT. We don’t teach them just one domain. We teach them the methodology,” Fan adds.

As long as the inputs to the solver are correct, it will give the right answer. Any mistakes in the solution come from errors in the formulation process.

To ensure it has found a working plan, LLMFP analyzes the solution and modifies any incorrect steps in the problem formulation. Once the plan passes this self-assessment, the solution is described to the user in natural language.

Perfecting the plan

This self-assessment module also allows the LLM to add any implicit constraints it missed the first time around, Hao says.

For instance, if the framework is optimizing a supply chain to minimize costs for a coffeeshop, a human knows the coffeeshop can’t ship a negative amount of roasted beans, but an LLM might not realize that.

The self-assessment step would flag that error and prompt the model to fix it.

“Plus, an LLM can adapt to the preferences of the user. If the model realizes a particular user does not like to change the time or budget of their travel plans, it can suggest changing things that fit the user’s needs,” Fan says.

In a series of tests, their framework achieved an average success rate between 83 and 87 percent across nine diverse planning problems using several LLMs. While some baseline models were better at certain problems, LLMFP achieved an overall success rate about twice as high as the baseline techniques.

Unlike these other approaches, LLMFP does not require domain-specific examples for training. It can find the optimal solution to a planning problem right out of the box.

In addition, the user can adapt LLMFP for different optimization solvers by adjusting the prompts fed to the LLM.

“With LLMs, we have an opportunity to create an interface that allows people to use tools from other domains to solve problems in ways they might not have been thinking about before,” Fan says.

In the future, the researchers want to enable LLMFP to take images as input to supplement the descriptions of a planning problem. This would help the framework solve tasks that are particularly hard to fully describe with natural language.

This work was funded, in part, by the Office of Naval Research and the MIT-IBM Watson AI Lab.

As a young girl growing up in the former Soviet Union, Evelina Fedorenko PhD ’07 studied several languages, including English, as her mother hoped that it would give her the chance to eventually move abroad for better opportunities.

Her language studies not only helped her establish a new life in the United States as an adult, but also led to a lifelong interest in linguistics and how the brain processes language. Now an associate professor of brain and cognitive sciences at MIT, Fedorenko studies the brain’s language-processing regions: how they arise, whether they are shared with other mental functions, and how each region contributes to language comprehension and production.

Fedorenko’s early work helped to identify the precise locations of the brain’s language-processing regions, and she has been building on that work to generate insight into how different neuronal populations in those regions implement linguistic computations.

“It took a while to develop the approach and figure out how to quickly and reliably find these regions in individual brains, given this standard problem of the brain being a little different across people,” she says. “Then we just kept going, asking questions like: Does language overlap with other functions that are similar to it? How is the system organized internally? Do different parts of this network do different things? There are dozens and dozens of questions you can ask, and many directions that we have pushed on.”

Among some of the more recent directions, she is exploring how the brain’s language-processing regions develop early in life, through studies of very young children, people with unusual brain architecture, and computational models known as large language models.

From Russia to MIT

Fedorenko grew up in the Russian city of Volgograd, which was then part of the Soviet Union. When the Soviet Union broke up in 1991, her mother, a mechanical engineer, lost her job, and the family struggled to make ends meet.

“It was a really intense and painful time,” Fedorenko recalls. “But one thing that was always very stable for me is that I always had a lot of love, from my parents, my grandparents, and my aunt and uncle. That was really important and gave me the confidence that if I worked hard and had a goal, that I could achieve whatever I dreamed about.”

Fedorenko did work hard in school, studying English, French, German, Polish, and Spanish, and she also participated in math competitions. As a 15-year-old, she spent a year attending high school in Alabama, as part of a program that placed students from the former Soviet Union with American families. She had been thinking about applying to universities in Europe but changed her plans when she realized the American higher education system offered more academic flexibility.

After being admitted to Harvard University with a full scholarship, she returned to the United States in 1998 and earned her bachelor’s degree in psychology and linguistics, while also working multiple jobs to send money home to help her family.

While at Harvard, she also took classes at MIT and ended up deciding to apply to the Institute for graduate school. For her PhD research at MIT, she worked with Ted Gibson, a professor of brain and cognitive sciences, and later, Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience. She began by using functional magnetic resonance imaging (fMRI) to study brain regions that appeared to respond preferentially to music, but she soon switched to studying brain responses to language.

She found that working with Kanwisher, who studies the functional organization of the human brain but hadn’t worked much on language before, helped Fedorenko to build a research program free of potential biases baked into some of the early work on language processing in the brain.

“We really kind of started from scratch,” Fedorenko says, “combining the knowledge of language processing I have gained by working with Gibson and the rigorous neuroscience approaches that Kanwisher had developed when studying the visual system.”

After finishing her PhD in 2007, Fedorenko stayed at MIT for a few years as a postdoc funded by the National Institutes of Health, continuing her research with Kanwisher. During that time, she and Kanwisher developed techniques to identify language-processing regions in different people, and discovered new evidence that certain parts of the brain respond selectively to language. Fedorenko then spent five years as a research faculty member at Massachusetts General Hospital, before receiving an offer to join the faculty at MIT in 2019.

How the brain processes language

Since starting her lab at MIT’s McGovern Institute for Brain Research, Fedorenko and her trainees have made several discoveries that have helped to refine neuroscientists’ understanding of the brain’s language-processing regions, which are spread across the left frontal and temporal lobes of the brain.

In a series of studies, her lab showed that these regions are highly selective for language and are not engaged by activities such as listening to music, reading computer code, or interpreting facial expressions, all of which have been argued to be share similarities with language processing.

“We’ve separated the language-processing machinery from various other systems, including the system for general fluid thinking, and the systems for social perception and reasoning, which support the processing of communicative signals, like facial expressions and gestures, and reasoning about others’ beliefs and desires,” Fedorenko says. “So that was a significant finding, that this system really is its own thing.”

More recently, Fedorenko has turned her attention to figuring out, in more detail, the functions of different parts of the language processing network. In one recent study, she identified distinct neuronal populations within these regions that appear to have different temporal windows for processing linguistic content, ranging from just one word up to six words.

She is also studying how language-processing circuits arise in the brain, with ongoing studies in which she and a postdoc in her lab are using fMRI to scan the brains of young children, observing how their language regions behave even before the children have fully learned to speak and understand language.

Large language models (similar to ChatGPT) can help with these types of developmental questions, as the researchers can better control the language inputs to the model and have continuous access to its abilities and representations at different stages of learning.

“You can train models in different ways, on different kinds of language, in different kind of regimens. For example, training on simpler language first and then more complex language, or on language combined with some visual inputs. Then you can look at the performance of these language models on different tasks, and also examine changes in their internal representations across the training trajectory, to test which model best captures the trajectory of human language learning,” Fedorenko says.

To gain another window into how the brain develops language ability, Fedorenko launched the Interesting Brains Project several years ago. Through this project, she is studying people who experienced some type of brain damage early in life, such as a prenatal stroke, or brain deformation as a result of a congenital cyst. In some of these individuals, their conditions destroyed or significantly deformed the brain’s typical language-processing areas, but all of these individuals are cognitively indistinguishable from individuals with typical brains: They still learned to speak and understand language normally, and in some cases, they didn’t even realize that their brains were in some way atypical until they were adults.

“That study is all about plasticity and redundancy in the brain, trying to figure out what brains can cope with, and how” Fedorenko says. “Are there many solutions to build a human mind, even when the neural infrastructure is so different-looking?”

How far would you go for a good meal? For some of the ocean’s top predators, maintaining a decent diet requires some surprisingly long-distance dives.

MIT oceanographers have found that big fish like tuna and swordfish get a large fraction of their food from the ocean’s twilight zone — a cold and dark layer of the ocean about half a mile below the surface, where sunlight rarely penetrates. Tuna and swordfish have been known to take extreme plunges, but it was unclear whether these deep dives were for food, and to what extent the fishes’ diet depends on prey in the twilight zone.

In a study published recently in the ICES Journal of Marine Science, the MIT student-led team reports that the twilight zone is a major food destination for three predatory fish — bigeye tuna, yellowfin tuna, and swordfish. While the three species swim primarily in the shallow open ocean, the scientists found these fish are sourcing between 50 and 60 percent of their diet from the twilight zone.

The findings suggest that tuna and swordfish rely more heavily on the twilight zone than scientists had assumed. This implies that any change to the twilight zone’s food web, such as through increased fishing, could negatively impact fisheries of more shallow tuna and swordfish.

“There is increasing interest in commercial fishing in the ocean’s twilight zone,” says Ciara Willis, the study’s lead author, who was a PhD student in the MIT-Woods Hole Oceanographic Institution (WHOI) Joint Program when conducting the research and is now a postdoc at WHOI. “If we start heavily fishing that layer of the ocean, our study suggests that could have profound implications for tuna and swordfish, which are very reliant on the twilight zone and are highly valuable existing fisheries.”

The study’s co-authors include Kayla Gardener of MIT-WHOI, and WHOI researchers Martin Arostegui, Camrin Braun, Leah Hougton, Joel Llopiz, Annette Govindarajan, and Simon Thorrold, along with Walt Golet at the University of Maine.

Deep-ocean buffet

The ocean’s twilight zone is a vast and dim layer that lies between the sunlit surface waters and the ocean’s permanently dark, midnight zone. Also known as the midwater, or mesopelagic layer, the twilight zone stretches between 200 and 1,000 meters below the ocean’s surface and is home to a huge variety of organisms that have adapted to live in the darkness.

“This is a really understudied region of the ocean, and it’s filled with all these fantastic, weird animals,” Willis says.

In fact, it’s estimated that the biomass of fish in the twilight zone is somewhere close to 10 billion tons, much of which is concentrated in layers at certain depths. By comparison, the marine life that lives closer to the surface, Willis says, is “a thin soup,” which is slim pickings for large predators.

“It’s important for predators in the open ocean to find concentrated layers of food. And I think that’s what drives them to be interested in the ocean’s twilight zone,” Willis says. “We call it the ‘deep ocean buffet.’”

And much of this buffet is on the move. Many kinds of fish, squid, and other deep-sea organisms in the twilight zone will swim up to the surface each night to find food. This twilight community will descend back into darkness at dawn to avoid detection.

Scientists have observed that many large predatory fish will make regular dives into the twilight zone, presumably to feast on the deep-sea bounty. For instance, bigeye tuna spend much of their day making multiple short, quick plunges into the twilight zone, while yellowfin tuna dive down every few days to weeks. Swordfish, in contrast, appear to follow the daily twilight migration, feeding on the community as it rises and falls each day.

“We’ve known for a long time that these fish and many other predators feed on twilight zone prey,” Willis says. “But the extent to which they rely on this deep-sea food web for their forage has been unclear.”

Twilight signal

For years, scientists and fishers have found remnants of fish from the twilight zone in the stomach contents of larger, surface-based predators. This suggests that predator fish do indeed feed on twilight food, such as lanternfish, certain types of squid, and long, snake-like fish called barracudina. But, as Willis notes, stomach contents give just a “snapshot” of what a fish ate that day.

She and her colleagues wanted to know how big a role twilight food plays in the general diet of predator fish. For their new study, the team collaborated with fishermen in New Jersey and Florida, who fish for a living in the open ocean. They supplied the team with small tissue samples of their commercial catch, including samples of bigeye tuna, yellowfin tuna, and swordfish.

Willis and her advisor, Senior Scientist Simon Thorrold, brought the samples back to Thorrold’s lab at WHOI and analyzed the fish bits for essential amino acids — the key building blocks of proteins. Essential amino acids are only made by primary producers, or members of the base of the food web, such as phytoplankton, microbes, and fungi. Each of these producers makes essential amino acids with a slightly different carbon isotope configuration that then is conserved as the producers are consumed on up their respective food chains.

“One of the hypotheses we had was that we’d be able to distinguish the carbon isotopic signature of the shallow ocean, which would logically be more phytoplankton-based, versus the deep ocean, which is more microbially based,” Willis says.

The researchers figured that if a fish sample had one carbon isotopic make-up over another, it would be a sign that that fish feeds more on food from the deep, rather than shallow waters.

“We can use this [carbon isotope signature] to infer a lot about what food webs they’ve been feeding in, over the last five to eight months,” Willis says.

The team looked at carbon isotopes in tissue samples from over 120 samples including bigeye tuna, yellowfin tuna, and swordfish. They found that individuals from all three species contained a substantial amount of carbon derived from sources in the twilight zone. The researchers estimate that, on average, food from the twilight zone makes up 50 to 60 percent of the diet of the three predator species, with some slight variations among species.

“We saw the bigeye tuna were far and away the most consistent in where they got their food from. They didn’t vary much from individual to individual,” Willis says. “Whereas the swordfish and yellowfin tuna were more variable. That means if you start having big-scale fishing in the twilight zone, the bigeye tuna might be the ones who are most at risk from food web effects.”

The researchers note there has been increased interest in commercially fishing the twilight zone. While many fish in that region are not edible for humans, they are starting to be harvested as fishmeal and fish oil products. In ongoing work, Willis and her colleagues are evaluating the potential impacts to tuna fisheries if the twilight zone becomes a target for large-scale fishing.

“If predatory fish like tunas have 50 percent reliance on twilight zone food webs, and we start heavily fishing that region, that could lead to uncertainty around the profitability of tuna fisheries,” Willis says. “So we need to be very cautious about impacts on the twilight zone and the larger ocean ecosystem.”

This work was part of the Woods Hole Oceanographic Institution’s Ocean Twilight Zone Project, funded as part of the Audacious Project housed at TED. Willis was additionally supported by the Natural Sciences and Engineering Research Council of Canada and the MIT Martin Family Society of Fellows for Sustainability.

Next time you’re watching football you might be looking at an important feat of engineering from an MIT alumnus.

For the last year, former MIT middle linebacker and mechanical engineer Kodiak Brush ’17 has been leading the development of football helmets for the California-based sports equipment manufacturer LIGHT Helmets. In December, Brush notched a major achievement in that work: LIGHT Helmets’ new Apache helmet line was ranked the highest-performing helmet ever in safety tests by Virginia Tech’s renowned helmet-testing lab.

The ranking bolsters LIGHT Helmets’ innovative effort to make football helmets lighter and safer.

“We’re trying to lower the overall amount of energy going into each impact by lowering the weight of the helmet,” Brush says. “It’s a balancing act trying to have a complete, polished product with all the bells and whistles while at the same time keeping the mass of the helmet as low as possible.”

No helmet ensures total safety, and the NFL carries out helmet tests of its own, but for Brush, who played football for most of his life, the latest results were a rewarding milestone.

“It’s really cool to work in the football helmet space after playing the sport for so long,” Brush says. “We did this with a fraction of the research and development budget of our competitors. It’s a great feeling to have worked on something that could help so many people.”

From the field to the lab

Brush spent his playing career at middle linebacker, a position often considered the quarterback of the defense. In that role, he got accustomed to helping teammates understand their assignments on the field and making sure everyone was in the right position. At MIT, he quickly realized his role would be different.

“In high school, I was constantly reminding teammates what their job was and helping linemen when they lined up in the wrong spot,” Brush says. “At MIT, I didn’t need to do that at all. Everyone knew exactly what their job was. It was really cool playing football with such an intelligent group.”

Throughout his football career, Brush says concussions hung over the sport. He was only formally diagnosed with one concussion, but he notes how difficult it can be to accurately diagnose concussions during games.

“We did baseline tests before the season so we could take tests after a suspected concussion to see if our cognitively ability was degraded,” Brush explains. “But as a player, you want to get back out there and keep helping your team, so players often try to downplay injuries. The doctors do their best.”

Brush worked as an accident reconstruction expert immediately after graduation before joining a product design firm. It was through that position that he first began working with LIGHT Helmets through a consulting project. He started full time with LIGHT last year.

Since then, Brush has managed research and development along with the production of new helmet lines, working closely with LIGHT’s technology partner, KOLLIDE.

“I’m currently the only engineer at LIGHT, so I wear a lot of different hats,” Brush says.

A safer helmet

Brush led the development of LIGHT’s Apache helmet. His approach harkened back to his favorite class at MIT, 2.009 (Product Engineering Process). In the process of building prototypes, students in that class are often tasked with taking apart other products to study how they’re made. For Apache, Brush started by disassembling competing helmets to try to understand how they work, where they’re limited, and where each ounce of weight comes from.

“That helped us make decisions around what we wanted to incorporate into our helmets and what we thought was unnecessary,” Brush says.

LIGHT’s Apache helmets use an impact-modified nylon shell and a 3D-printed thermoplastic polyurethane liner. The liner can compress up to 80 percent of its thickness under full compression compared to traditional foam, which Brush says may compress 20 to 30 percent at most. The liner is made up of 20 different cylindrical pods, each of which has variable stiffness depending on the location in the helmet.

Brush says the shell is more flexible than traditional helmets, which is part of a broader trend among companies focusing on concussion avoidance.

“The idea with the flexible shell is we’re now able to squish both the inside and outside of the helmet, which lets you extend the length of the impact and lower the severity of the hit,” Brush says.

A winning formula

Brush says the company’s performance in Virginia Tech’s tests has garnered a lot of excitement in the industry. The Apache helmet is available for use across high school, college, and professional levels, and the company is currently developing a youth version.

“Last year, we sold about 5,000 helmets, but we’re anticipating tenfold growth this year,” Brush says. “Dealers see the opportunity to sell the number-one-rated helmet at the price of a lot of much lower-rated helmets.”

Other helmets from LIGHT are already being used at the highest levels, with players from 30 of the 32 NFL teams choosing a LIGHT Helmet when they suit up, the company says. That traction has changed Brush’s relationship with football.

For instance, he only used to watch NFL games on Sundays occasionally. But now that his helmets are on TV, he finds himself rooting for the players and teams wearing them.

Regardless of who he roots for, when football becomes safer, everyone wins.

Frederick “Fred” Davis Greene II, professor emeritus in the MIT Department of Chemistry who was accomplished in the field of physical organic chemistry and free radicals, passed away peacefully after a brief illness, surrounded by his family, on Saturday, March 22. He had been a member of the MIT community for over 70 years.

“Greene’s dedication to teaching, mentorship, and the field of physical organic chemistry is notable,” said Professor Troy Van Voorhis, head of the Department of Chemistry, upon learning of Greene’s passing. “He was also a constant source of joy to those who interacted with him, and his commitment to students and education was legendary. He will be sorely missed.”

Greene, a native of Glen Ridge, New Jersey, was born on July 7, 1927 to parents Phillips Foster Greene and Ruth Altman Greene. He spent his early years in China, where his father was a medical missionary with Yale-In-China. Greene and his family moved to the Philippines just ahead of the Japanese invasion prior to World War Il, and then back to the French Concession of Shanghai, and to the United States in 1940. He joined the U.S. Navy in December 1944, and afterwards earned his bachelor’s degree from Amherst College in 1949 and a PhD from Harvard University in 1952. Following a year at the University of California at Los Angeles as a research associate, he was appointed a professor of chemistry at MIT by then-Department Head Arthur C. Cope in 1953. Greene retired in 1995.

Greene’s research focused on peroxide decompositions and free radical chemistry, and he reported the remarkable bimolecular reaction between certain diacyl peroxides and electron-rich olefins and aromatics. He was also interested in small-ring heterocycles, e.g., the three-membered ring 2,3-diaziridinones. His research also covered strained olefins, the Greene-Viavattene diene, and 9, 9', 10, 10'-tetradehydrodianthracene.

Greene was elected to the American Academy of Arts and Sciences in 1965 and received an honorary doctorate from Amherst College for his research in free radicals. He served as editor-in-chief of the Journal of Organic Chemistry of the American Chemical Society from 1962 to 1988. He was awarded a special fellowship form the National Science Foundation and spent a year at Cambridge University, Cambridge, England, and was a member of the Chemical Society of London.

Greene and Professor James Moore of the University of Philadelphia worked closely with Greene’s wife, Theodora “Theo” W. Greene, in the conversion of her PhD thesis, which was overseen by Professor Elias J. Corey of Harvard University, into her book “Greene’s Protective Groups in Organic Synthesis.” The book became an indispensable reference for any practicing synthetic organic or medicinal chemist and is now in its fifth edition. Theo, who predeceased Fred in July 2005, was a tremendous partner to Greene, both personally and professionally. A careful researcher in her own right, she served as associate editor of the Journal of Organic Chemistry for many years.

Fred Greene was recently featured in a series of videos featuring Professor Emeritus Dietmar Seyferth (who passed away in 2020) that was spearheaded by Professor Rick Danheiser. The videos cover a range of topics, including Seyferth and Greene’s memories during the 1950s to mid-1970s of their fellow faculty members, how they came to be hired, the construction of various lab spaces, developments in teaching and research, the evolution of the department’s graduate program, and much more. 

Danheiser notes that it was a privilege to share responsibility for the undergraduate class 5.43 (Advanced Organic Chemistry) with Greene. “Fred Greene was a fantastic teacher and inspired several generations of MIT undergraduate and graduate students with his superb lectures,” Danheiser recalls. The course they shared was Danheiser’s first teaching assignment at MIT, and he states that Greene’s “counsel and mentoring was invaluable to me.”

The Department of Chemistry recognized Greene’s contributions to its academic program by naming the annual student teaching award the “Frederick D. Greene Teaching Award.” This award recognizes outstanding contributions in teaching in chemistry by undergraduates. Since 1993 the award has been given to 46 students.

Dabney White Dixon PhD ’76 was one of many students with whom Greene formed a lifelong friendship and mentorship. Dixon shares, “Fred Greene was an outstanding scientist — intelligent, ethical, and compassionate in every aspect of his life. He possessed an exceptional breadth of knowledge in organic chemistry, particularly in mechanistic organic chemistry, as evidenced by his long tenure as editor of the Journal of Organic Chemistry (1962 to 1988). Weekly, large numbers of manuscripts flowed through his office. He had an acute sense of fairness in evaluating submissions and was helpful to those submitting manuscripts. His ability to navigate conflicting scientific viewpoints was especially evident during the heated debates over non-classical carbonium ions in the 1970s.

“Perhaps Fred’s greatest contribution to science was his mentorship. At a time when women were rare in chemistry PhD programs, Fred’s mentorship was particularly meaningful. I was the first woman in my scientific genealogical lineage since the 1500s, and his guidance gave me the confidence to overcome challenges. He and Theo provided a supportive and joyful environment, helping me forge a career in academia where I have since mentored 13 PhD students — an even mix of men and women — a testament to the social progress in science that Fred helped foster.

“Fred’s meticulous attention to detail was legendary. He insisted that every new molecule be fully characterized spectroscopically before he would examine the data. Through this, his students learned the importance of thoroughness, accuracy, and organization. He was also an exceptional judge of character, entrusting students with as much responsibility as they could handle. His honesty was unwavering — he openly acknowledged mistakes, setting a powerful example for his students.

“Shortly before the pandemic, I had the privilege of meeting Fred with two of his scientific ‘granddaughters’ — Elizabeth Draganova, then a postdoc at Tufts (now an assistant professor at Emory), and Cyrianne Keutcha, then a graduate student at Harvard (now a postdoc at Yale). As we discussed our work, it was striking how much science had evolved — from IR and NMR of small-ring heterocycles to surface plasmon resonance and cryo-electron microscopy of large biochemical systems. Yet, Fred’s intellectual curiosity remained as sharp as ever. His commitment to excellence, attention to detail, and passion for uncovering chemical mechanisms lived on in his scientific descendants.

“He leaves a scientific legacy of chemists who internalized his lessons on integrity, kindness, and rigorous analysis, carrying them forward to their own students and research. His impact on the field of chemistry — and on the lives of those fortunate enough to have known him — will endure.”

Carl Renner PhD ’74 felt fortunate and privileged to be a doctoral student in the Greene group from 1969 to 1973, and also his teaching assistant for his 5.43 course. Renner recalls, “He possessed a curious mind of remarkable clarity and discipline. He prepared his lectures meticulously and loved his students. He was extremely generous with his time and knowledge. I never heard him complain or say anything unkind. Everyone he encountered came away better for it.”

Gary Breton PhD ’91 credits the development of his interest in physical organic chemistry to his time spent in Greene’s class. Breton says, “During my time in the graduate chemistry program at MIT (1987-91) I had the privilege of learning from some of the world’s greatest minds in chemistry, including Dr. Fred Greene. At that time, all incoming graduate students in organic chemistry were assigned in small groups to a seminar-type course that met each week to work on the elucidation of reaction mechanisms, and I was assigned to Dr. Greene’s class. It was here that not only did Dr. Greene afford me a confidence in how to approach reaction mechanisms, but he also ignited my fascination with physical organic chemistry. I was only too happy to join his research group, and begin a love/hate relationship with reactive nitrogen-containing heterocycles that continues to this day in my own research lab as a chemistry professor. 

“Anyone that knew Dr. Greene quickly recognized that he was highly intelligent and exceptionally knowledgeable about all things organic, but under his mentorship I also saw his creativity and cleverness. Beyond that, and even more importantly, I witnessed his kindness and generosity, and his subtle sense of humor. Dr. Greene’s enduring legacy is the large number of undergraduate students, graduate students, and postdocs whose lives he touched over his many years. He will be greatly missed.”

John Dolhun PhD ’73 recalls Greene’s love for learning, and that he “was one of the kindest persons that I have known.” Dolhun shares, “I met Fred Greene when I was a graduate student. His organic chemistry course was one of the most popular, and he was a top choice for many students’ thesis committees. When I returned to MIT in 2008 and reconnected with him, he was still endlessly curious — always learning, asking questions. A few years ago, he visited me and we had lunch. Back at the chemistry building, I reached for the elevator button and he said, ‘I always walk up the five flights of stairs.’ So, I walked up with him. Fred knew how to keep both mind and body in shape. He was truly a beacon of light in the department.”

Liz McGrath, retired chemistry staff member, warmly recalls the regular coffees and conversations she shared with Fred over two decades at the Institute. She shares, “Fred, who was already emeritus by the time of my arrival, imparted to me a deep interest in the history of MIT Chemistry’s events and colorful faculty. He had a phenomenal memory, which made his telling of the history so rich in its content. He was a true gentleman and sweet and kind to boot. ... I will remember him with much fondness.”

Greene is survived by his children, Alan, Carol, Elizabeth, and Phillips; nine grandchildren; and six great grandchildren. A memorial service will be held on April 5 at 11 a.m. at the First Congregational Church in Winchester, Massachusetts.

Pattie Maes, the Germeshausen Professor of Media Arts and Sciences at MIT and head of the Fluid Interfaces research group within the MIT Media Lab, has been awarded the 2025 ACM SIGCHI Lifetime Research Award. She will accept the award at CHI 2025 in Yokohama, Japan this April.

The Lifetime Research Award is given to individuals whose research in human-computer interaction (HCI) is considered both fundamental and influential to the field. Recipients are selected based on their cumulative contributions, influence on the work of others, new research developments, and being an active participant in the Association for Computing Machinery’s Special Interest Group on Computer-Human Interaction (ACM SIGCHI) community.

Her nomination recognizes her advocacy to place human agency at the center of HCI and artificial intelligence research. Rather than AI replacing human capabilities, Maes has advocated for ways in which human capabilities can be supported or enhanced by the integration of AI.

Pioneering the concept of software agents in the 1990s, Maes’ work has always been situated at the intersection of human-computer interaction and artificial intelligence and has helped lay the foundations for today’s online experience. Her article “Social information filtering: algorithms for automating 'word of mouth'” from CHI 95, co-authored with graduate student Upendra Shardanand, is the second-most-cited paper from ACM SIGCHI.  

Beyond her contributions in desktop-based interaction, she has an extensive body of work in the area of  novel wearable devices that enhance the human experience, for example by supporting memory, learning, decision-making, or health. Through an interdisciplinary approach, Maes has explored accessible and ethical designs while stressing the need for a human-centered approach.

“As a senior faculty member, Pattie is an integral member of the Media Lab, MIT, and larger HCI communities,” says Media Lab Director Dava Newman. “Her contributions to several different fields, alongside her unwavering commitment to enhancing the human experience in her work, is exemplary of not only the Media Lab’s interdisciplinary spirit, but also our core mission: to create transformative technologies and systems that enable people to reimagine and redesign their lives. We all celebrate this well-deserved recognition for Pattie!”

Maes is the second MIT professor to receive this honor, joining her Media Lab colleague Hiroshi Ishii, the Jerome B. Wiesner Professor of Media Arts and Sciences at MIT and head of the Tangible Media research group.

“I am honored to be recognized by the ACM community, especially given that it can be difficult sometimes for researchers doing highly interdisciplinary research to be appreciated, even though some of the most impactful innovations often emerge from that style of research,” Maes comments.

On March 25, the Abdul Latif Jameel Poverty Action Lab (J-PAL) at MIT launched the global Alliance for Data, Evaluation, and Policy Training (ADEPT) with Community Jameel at an event in São Paulo, Brazil. 

ADEPT is a network of universities, governments, and other members united by a shared vision: To empower the next generation of policymakers, decision-makers, and researchers with the tools to innovate, test, and scale the most effective social policies and programs. These programs have the potential to improve the lives of millions of people around the world.

Too often, policy decisions in governments and other organizations are driven by ideology or guesswork. This can result in ineffective and inefficient policies and programs that don’t always serve their intended populations. ADEPT will bring a scientific perspective to policymaking, focusing on topics like statistical analysis, data science, and rigorous impact evaluation. 

Together with J-PAL, members will create innovative pathways for learners that include virtual and in-person courses, develop new academic programs on policy evaluation and data analysis, and cultivate a network of evidence-informed policy professionals to drive change globally. 

At the launch event at Insper, a Brazilian higher education institution, MIT economists Esther Duflo, co-founder of J-PAL, and Sara Fisher Ellison, faculty director of ADEPT, spoke about the importance of building a community aligned in support of evidence-informed policymaking. 

“Our aim is to create a vision-driven network of institutions around the world able to equip far more people in far more places with the skills and ambition for evidence-informed policymaking,” said Duflo. “We are excited to welcome Insper to the movement and create new opportunities for learners in Brazil.”

Members of the alliance will also have access to the MITx MicroMasters program in Data, Economics, and Design of Policy (DEDP), which offers online courses taught by MIT Department of Economics faculty through MIT’s Office of Open Learning. The program offers graduate-level courses that combine the tools of economics and policy design with a strong foundation in economic and mathematical principles.

Early members of the alliance include Insper, a leading research and training institution in Brazil; the National School of Statistics and Applied Economics of Abidjan in collaboration with the Cote d’Ivorian government; the Paris School of Economics; and Princeton University. 

“This unprecedented initiative in Latin America reinforces Insper’s commitment to academic excellence and the internationalization of teaching, providing Brazilian students with access to a globally renowned program,” says Cristine Pinto, Insper’s director of research. “Promoting large-scale impact through research and data analysis is a core objective of Insper, and shared by J-PAL and the expansion of ADEPT.”

Learners who obtain the DEDP MicroMasters credential through ADEPT can accelerate their pursuit of a master’s degree by applying to participating universities, including Insper and MIT, opening doors for learners who may not otherwise have access to leading economics programs.

By empowering learners with the tools and ambition to create meaningful change, ADEPT seeks to accelerate data-driven decision-making at every step of the policymaking process. Ultimately, the hope is that ADEPT’s impact will be felt not only by alliance members and their individual learners, but by millions of people reached by better policies and programs worldwide.

MIT and GE Vernova today announced the creation of the MIT-GE Vernova Energy and Climate Alliance to help develop and scale sustainable energy systems across the globe.

The alliance launches a five-year collaboration between MIT and GE Vernova, a global energy company that spun off from General Electric’s energy business in 2024. The endeavor will encompass research, education, and career opportunities for students, faculty, and staff across MIT’s five schools and the MIT Schwarzman College of Computing. It will focus on three main themes: decarbonization, electrification, and renewables acceleration.

“This alliance will provide MIT students and researchers with a tremendous opportunity to work on energy solutions that could have real-world impact,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer and dean of the School of Engineering. “GE Vernova brings domain knowledge and expertise deploying these at scale. When our researchers develop new innovative technologies, GE Vernova is strongly positioned to bring them to global markets.”

Through the alliance, GE Vernova is sponsoring research projects at MIT and providing philanthropic support for MIT research fellowships. The company will also engage with MIT’s community through participation in corporate membership programs and professional education.

“It’s a privilege to combine forces with MIT’s world-class faculty and students as we work together to realize an optimistic, innovation-driven approach to solving the world’s most pressing challenges,” says Scott Strazik, GE Vernova CEO. “Through this alliance, we are proud to be able to help drive new technologies while at the same time inspire future leaders to play a meaningful role in deploying technology to improve the planet at companies like GE Vernova.”

“This alliance embodies the spirit of the MIT Climate Project — combining cutting-edge research, a shared drive to tackle today’s toughest energy challenges, and a deep sense of optimism about what we can achieve together,” says Sally Kornbluth, president of MIT. “With the combined strengths of MIT and GE Vernova, we have a unique opportunity to make transformative progress in the flagship areas of electrification, decarbonization, and renewables acceleration.”

The alliance, comprising a $50 million commitment, will operate within MIT’s Office of Innovation and Strategy. It will fund approximately 12 annual research projects relating to the three themes, as well as three master’s student projects in MIT’s Technology and Policy Program. The research projects will address challenges like developing and storing clean energy, as well as the creation of robust system architectures that help sustainable energy sources like solar, wind, advanced nuclear reactors, green hydrogen, and more compete with carbon-emitting sources.

The projects will be selected by a joint steering committee composed of representatives from MIT and GE Vernova, following an annual Institute-wide call for proposals.

The collaboration will also create approximately eight endowed GE Vernova research fellowships for MIT students, to be selected by faculty and beginning in the fall. There will also be 10 student internships that will span GE Vernova’s global operations, and GE Vernova will also sponsor programming through MIT’s New Engineering Education Transformation (NEET), which equips students with career-oriented experiential opportunities. Additionally, the alliance will create professional education programming for GE Vernova employees.

“The internships and fellowships will be designed to bring students into our ecosystem,” says GE Vernova Chief Corporate Affairs Officer Roger Martella. “Students will walk our factory floor, come to our labs, be a part of our management teams, and see how we operate as business leaders. They’ll get a sense for how what they’re learning in the classroom is being applied in the real world.”

Philanthropic support from GE Vernova will also support projects in MIT’s Human Insight Collaborative (MITHIC), which launched last fall to elevate human-centered research and teaching. The projects will allow faculty to explore how areas like energy and cybersecurity influence human behavior and experiences.

In connection with the alliance, GE Vernova is expected to join several MIT consortia and membership programs, helping foster collaborations and dialogue between industry experts and researchers and educators across campus.

With operations across more than 100 countries, GE Vernova designs, manufactures, and services technologies to generate, transfer, and store electricity with a mission to decarbonize the world. The company is headquartered in Kendall Square, right down the road from MIT, which its leaders say is not a coincidence.

“We’re really good at taking proven technologies and commercializing them and scaling them up through our labs,” Martella says. “MIT excels at coming up with those ideas and being a sort of time machine that thinks outside the box to create the future. That’s why this such a great fit: We both have a commitment to research, innovation, and technology.”

The alliance is the latest in MIT’s rapidly growing portfolio of research and innovation initiatives around sustainable energy systems, which also includes the Climate Project at MIT. Separate from, but complementary to, the MIT-GE Vernova Alliance, the Climate Project is a campus-wide effort to develop technological, behavioral, and policy solutions to some of the toughest problems impeding an effective global climate response.

It’s tricky to predict precisely what the impacts of climate change will be, given the many variables involved. To predict the impacts of a warmer world on plant life, some researchers look at urban “heat islands,” where, because of the effects of urban structures, temperatures consistently run a few degrees higher than those of the surrounding rural areas. This enables side-by-side comparisons of plant responses.

But a new study by researchers at MIT and Harvard University has found that, at least for forests, urban heat islands are a poor proxy for global warming, and this may have led researchers to underestimate the impacts of warming in some cases. The discrepancy, they found, has a lot to do with the limited genetic diversity of urban tree species.

The findings appear in the journal PNAS, in a paper by MIT postdoc Meghan Blumstein, professor of civil and environmental engineering David Des Marais, and four others.

“The appeal of these urban temperature gradients is, well, it’s already there,” says Des Marais. “We can’t look into the future, so why don’t we look across space, comparing rural and urban areas?” Because such data is easily obtainable, methods comparing the growth of plants in cities with similar plants outside them have been widely used, he says, and have been quite useful. Researchers did recognize some shortcomings to this approach, including significant differences in availability of some nutrients such as nitrogen. Still, “a lot of ecologists recognized that they weren’t perfect, but it was what we had,” he says.

Most of the research by Des Marais’ group is lab-based, under conditions tightly controlled for temperature, humidity, and carbon dioxide concentration. While there are a handful of experimental sites where conditions are modified out in the field, for example using heaters around one or a few trees, “those are super small-scale,” he says. “When you’re looking at these longer-term trends that are occurring over space that’s quite a bit larger than you could reasonably manipulate, an important question is, how do you control the variables?”

Temperature gradients have offered one approach to this problem, but Des Marais and his students have also been focusing on the genetics of the tree species involved, comparing those sampled in cities to the same species sampled in a natural forest nearby. And it turned out there were differences, even between trees that appeared similar.

“So, lo and behold, you think you’re only letting one variable change in your model, which is the temperature difference from an urban to a rural setting,” he says, “but in fact, it looks like there was also a genotypic diversity that was not being accounted for.”

The genetic differences meant that the plants being studied were not representative of those in the natural environment, and the researchers found that the difference was actually masking the impact of warming. The urban trees, they found, were less affected than their natural counterparts in terms of when the plants’ leaves grew and unfurled, or “leafed out,” in the spring.

The project began during the pandemic lockdown, when Blumstein was a graduate student. She had a grant to study red oak genotypes across New England, but was unable to travel because of lockdowns. So, she concentrated on trees that were within reach in Cambridge, Massachusetts. She then collaborated with people doing research at the Harvard Forest, a research forest in rural central Massachusetts. They collected three years of data from both locations, including the temperature profiles, the leafing-out timing, and the genetic profiles of the trees. Though the study was looking at red oaks specifically, the researchers say the findings are likely to apply to trees broadly.

At the time, researchers had just sequenced the oak tree genome, and that allowed Blumstein and her colleagues to look for subtle differences among the red oaks in the two locations. The differences they found showed that the urban trees were more resistant to the effects of warmer temperatures than were those in the natural environment.

“Initially, we saw these results and we were sort of like, oh, this is a bad thing,” Des Marais says. “Ecologists are getting this heat island effect wrong, which is true.” Fortunately, this can be easily corrected by factoring in genomic data. “It’s not that much more work, because sequencing genomes is so cheap and so straightforward. Now, if someone wants to look at an urban-rural gradient and make these kinds of predictions, well, that’s fine. You just have to add some information about the genomes.”

It's not surprising that this genetic variation exists, he says, since growers have learned by trial and error over the decades which varieties of trees tend to thrive in the difficult urban environment, with typically poor soil, poor drainage, and pollution. “As a result, there’s just not much genetic diversity in our trees within cities.”

The implications could be significant, Des Marais says. When the Intergovernmental Panel on Climate Change (IPCC) releases its regular reports on the status of the climate, “one of the tools the IPCC has to predict future responses to climate change with respect to temperature are these urban-to-rural gradients.” He hopes that these new findings will be incorporated into their next report, which is just being drafted. “If these results are generally true beyond red oaks, this suggests that the urban heat island approach to studying plant response to temperature is underpredicting how strong that response is.”

The research team included Sophie Webster, Robin Hopkins, and David Basler from Harvard University and Jie Yun from MIT. The work was supported by the National Science Foundation, the Bullard Fellowship at the Harvard Forest, and MIT.

As a college student in Serbia with a passion for math and physics, Ana Trišović found herself drawn to computer science and its practical, problem-solving approaches. It was then that she discovered MIT OpenCourseWare, part of MIT Open Learning, and decided to study a course on Data Analytics with Python in 2012 — something her school didn’t offer.

That experience was transformative, says Trišović, who is now a research scientist at the FutureTech lab within MIT’s Computer Science and Artificial Intelligence Laboratory.

“That course changed my life,” she says. “Throughout my career, I have considered myself a Python coder, and MIT OpenCourseWare made it possible. I was in my hometown on another continent, learning from MIT world-class resources. When I reflect on my path, it’s incredible.”

Over time, Trišović's path led her to explore a range of OpenCourseWare resources. She recalls that, as a non-native English speaker, some of the materials were challenging. But thanks to the variety of courses and learning opportunities available on OpenCourseWare, she was always able to find ones that suited her. She encourages anyone facing that same challenge to be persistent.

“If the first course doesn’t work for you, try another,” she says. “Being persistent and investing in yourself is the best thing a young person can do.”

In her home country of Serbia, Trišović earned undergraduate degrees in computer science and mechanical engineering before going on to Cambridge University and CERN, where she contributed to work on the Large Hadron Collider and completed her PhD in computer science in 2018. She has also done research at the University of Chicago and Harvard University.

“I like that computer science allows me to make an impact in a range of fields, but physics remains close to my heart, and I’m constantly inspired by it,” she says.

MIT FutureTech, an interdisciplinary research group, draws on computer science, economics, and management to identify computing trends that create risk and opportunities for sustainable economic growth. There, Trišović studies the democratization of AI, including the implications of open-source AI and how that will impact science. Her work at MIT is a chance to build on research she has been pursuing since she was in graduate school.

“My work focuses on computational social science. For many years, I’ve been looking at what's known as 'the science of science' — investigating issues like research reproducibility," Trišović explains. “Now, as AI becomes increasingly prevalent and introduces new challenges, I’m interested in examining a range of topics — from AI democratization to its effects on the scientific method and the broader landscape of science.”

Trišović is grateful that, way back in 2012, she made the decision to try something new and learn with an OpenCourseWare course.

“I instantly fell in love with Python the moment I took that course. I have such a soft spot for OpenCourseWare — it shaped my career,” she says. “Every day at MIT is inspiring. I work with people who are excited to talk about AI and other fascinating topics.”

You’re an aerospace engineer on a tight timeline to develop a component for a rocket engine. No sweat, you think — you know the concepts by heart, and the model looks appropriate in CAD. But you inspect the 3D-printed part that you’ve outsourced for manufacturing, and something is wrong. The impeller blade angle is off, and the diameter is larger than the design intent. The vendor won’t get back to you. Suddenly you’re over budget. Something is leaking. Running the pump test rig, you’re not sure where that vibration is coming from.

Successfully navigating nightmares like this can make or break an engineer, but real-time problem-solving during assembly is something few undergraduates experience as part of their curriculum. Enter class 16.811 (Advanced Manufacturing for Aerospace Engineers), a new communication-intensive laboratory course that allows juniors and seniors to drive a full engineering cycle, gaining experience that mirrors the challenges they’ll face as practicing engineers.

In just 13 weeks, students design, build, and test a laboratory-scale electric turbopump, the type of pump used in liquid rocket propulsion systems to deliver fuel and oxidizer to the combustion chamber under high pressure. Teams of two or three students work through the entire production process while balancing budgets, documenting, and testing.

The course was developed and taught by Zachary Cordero, Esther and Harold E. Edgerton Associate Professor, and Zoltán Spakovszky, the T. Wilson Professor in Aeronautics, along with a team of teaching assistants (TAs), technical instructors, and communication experts. It ran for the first time last fall, open to students who had completed Unified Engineering, the foundational Course 16 curriculum covering the four disciplines at the core of aerospace engineering. It generated so much interest upon its announcement that spots were allocated via lottery.

“Sometimes it’s assumed that students will get hands-on experience through their extracurriculars, but they may not. Students in this class gain that experience through exposure to cutting-edge design and manufacturing tools, like metal 3D printing,” says Cordero. “They don’t just learn how to solve a problem set — they learn how to be an engineer.”

Training for a rapidly evolving field

The course was born out of feedback from participants at an annual workshop that Cordero organizes each summer addressing materials challenges in reusable rocket engines. Attendees representing industry, government, and academic sectors consistently emphasized the need for the next generation of engineers to be familiar with advanced engineering concepts, in addition to having strong fundamentals. Experience with new computational design tools and processes like additive manufacturing is becoming essential for success in the aerospace industry. “Our mission is to train, inspire, and motivate the next generation of aerospace engineers. We have to listen to what our industry partners want from engineers and adapt our curriculum to meet those needs,” says Cordero.

Spakovszky, Cordero, and the team built the course over two years of Independent Activities Period workshops, developing independent modules that teach concepts for constructing the turbopump. The first set of labs focuses on the impellers — the rotating bladed-disk component that draws fluid into the pump to pressurize it. The second lab breaks down the rotor system that supports the pump impeller, and the third covers integration of the rotor assembly into the casing and final testing.

Throughout the course, students receive instruction in technical communication and training on the full array of machine shop tools available in the Arthur and Linda Gelb Laboratory. Beyond learning the concepts and tools, the majority of the design and implementation is up to the students.

“They are pushed to learn how to learn on their own,” says Spakovszky. “The key differentiator here is that there is no solution. In other classes, you have a problem, and the instructor has the solution. This is open ended, and every team has a different design.” Project management is left up to each team, with instructors and TAs serving as resources, rather than leads. Each team works with vendors to help bring their designs to life. The students conducted their machinery analysis using the Agile Engineering Design System (AEDS) and Advanced Rotating Machine Dynamics (ARMD) software tools from Concepts NREC. Impellers were printed at the MIT SHED (Safety Health Environmental Discovery lab), with support from Tolga Durak, managing director of environment, health and safety, and by industry collaborators at Desktop Metal.

“A lot of the design questions we were working with don’t have firm answers,” says junior Danishell Destefano. “I learned a lot about how to read technical literature and compare design trade-offs to make my own decisions.”

On the floor

“Making things is really hard,” says Spakovszky. “In addition to manufacturing parts and components, the assembly of rotating machinery requires careful tolerancing of the part dimensions and precision manufacturing of the interfaces to meet design specification.”

At the core of the curriculum is the manufacturing process itself, with its myriad components posing a unique challenge for students who may not have experienced the kind of rapid design cycle that is becoming more and more common in the field. The course uses concurrent engineering as a methodology to emphasize the close connections between fundamental concepts, functional requirements, design, materials, and manufacturing.

Student teams document their lab results in written reports and give regular progress presentations. Lecturer Jessie Stickgold-Sarah instructed the class on professional communication. At the end of the semester, students walk away with the ability to not only create new things, but communicate about their creations.

“I really enjoyed working with this group of students,” says Stickgold-Sarah. “The main paper and presentations required students to express the reasoning using the design-build-test sequence, and to explain and justify their choices based on their technical understanding of core topics. They were incredibly hard-working and dedicated, and the papers and presentations they produced exceeded my expectations.”

The course culminates in a final presentation, where teams showcase their findings and get feedback from their MIT instructors and industry representatives — potential future colleagues and employers.

Whether or not students go directly into a career in rocket or jet propulsion, the breadth of skills they learn in class has applications across disciplines. “The biggest skill I’ve gained is time and project management. To build a pump in a semester is a pretty tough timeline challenge, and learning how to manage my time and work with a team has been a great soft skill to learn,” says Destafano.

The course drives home the reality that the manufacturing process can be just as important as the product. “I hope through this, they gain confidence to explore the unknown and deal with uncertainty in engineering systems,” says Cordero. “In the real world, things are leaking. Things aren’t as you initially anticipated or behaving as you thought they would behave. And the students had to react and respond. That's real life. It's kind of intuitive, kind of common sense, sure — but you can hone that skill, and develop confidence in that skill.”

Metamaterials are artificially-structured materials with extraordinary properties not easily found in nature. With engineered three-dimensional (3D) geometries at the micro- and nanoscale, these architected materials achieve unique mechanical and physical properties with capabilities beyond those of conventional materials — and have emerged over the past decade as a promising way to engineering challenges where all other existing materials have lacked success.

Architected materials exhibit unique mechanical and functional properties, but their full potential remains untapped due to challenges in design, fabrication, and characterization. Improvements and scalability in this space could help transform a range of industries, from biomedical implants, sports equipment, automotive and aerospace, and energy and electronics.

“Advances in scalable fabrication, high-throughput testing, and AI-driven design optimization could revolutionize the mechanics and materials science disciplines, enabling smarter, more adaptive materials that redefine engineering and everyday technologies,” says Carlos Portela, the Robert N. Noyce Career Development Professor and assistant professor of mechanical engineering at MIT.

In a Perspective published this month in the journal Nature Materials, Portela and James Surjadi, a postdoc in mechanical engineering, discuss key hurdles, opportunities, and future applications in the field of mechanical metamaterials. The paper is titled “Enabling three-dimensional architected materials across length scales and timescales.”

“The future of the field requires innovation in fabricating these materials across length scales, from nano to macro, and progress in understanding them at a variety of time scales, from slow deformation to dynamic impact,” says Portela, adding that it also demands interdisciplinary collaboration.

A Perspective is a peer-reviewed content type that the journal uses to invite reflection or discussion on matters that may be speculative, controversial, or highly technical, and where the subject matter may not meet the criteria for a Review.

“We felt like our field, following substantial progress over the last decade, is still facing two bottlenecks: issues scaling up, and no knowledge or understanding of properties under dynamic conditions,” says Portela, discussing the decision to write the piece.

Portela and Surjadi’s paper summarizes state-of-the-art approaches and highlights existing knowledge gaps in material design, fabrication, and characterization. It also proposes a roadmap to accelerate the discovery of architected materials with programmable properties via the synergistic combination of high-throughput experimentation and computational efforts, toward leveraging emerging artificial intelligence and machine learning techniques for their design and optimization.

“High-throughput miniaturized experiments, non-contact characterization, and benchtop extreme-condition methods will generate rich datasets for the implementation of data-driven models, accelerating the optimization and discovery of metamaterials with unique properties,” says Surjadi.

The Portela Lab’s motto is “architected mechanics and materials across scales.” The Perspective aims to bridge the gap between fundamental research and real-world applications of next-generation architected materials, and it presents a vision the lab has been working toward for the past four years.

Six current MIT affiliates and 27 additional MIT alumni have been elected as fellows of the American Association for the Advancement of Science (AAAS). 

The 2024 class of AAAS Fellows includes 471 scientists, engineers, and innovators, spanning all 24 of AAAS disciplinary sections, who are being recognized for their scientifically and socially distinguished achievements.

Noubar Afeyan PhD ’87, life member of the MIT Corporation, was named a AAAS Fellow “for outstanding leadership in biotechnology, in particular mRNA therapeutics, and for advocacy for recognition of the contributions of immigrants to economic and scientific progress.” Afeyan is the founder and CEO of the venture creation company Flagship Pioneering, which has built over 100 science-based companies to transform human health and sustainability. He is also the chairman and cofounder of Moderna, which was awarded a 2024 National Medal of Technology and Innovation for the development of its Covid-19 vaccine. Afeyan earned his PhD in biochemical engineering at MIT in 1987 and was a senior lecturer at the MIT Sloan School of Management for 16 years, starting in 2000. Among other activities at the Institute, he serves on the advisory board of the MIT Abdul Latif Jameel Clinic for Machine Learning and delivered MIT’s 2024 Commencement address.

Cynthia Breazeal SM ’93, ScD ’00 is a professor of media arts and sciences at MIT, where she founded and directs the Personal Robots group in the MIT Media Lab. At MIT Open Learning, she is the MIT dean for digital learning, and in this role, she leverages her experience in emerging digital technologies and business, research, and strategic initiatives to lead Open Learning’s business and research and engagement units. She is also the director of the MIT-wide Initiative on Responsible AI for Social Empowerment and Education (raise.mit.edu). She co-founded the consumer social robotics company, Jibo, Inc., where she served as chief scientist and chief experience officer. She is recognized for distinguished contributions in the field of artificial intelligence education, particularly around the use of social robots, and learning at scale.

Alan Edelman PhD ’89 is an applied mathematics professor for the Department of Mathematics and leads the Applied Computing Group of the Computer Science and Artificial Intelligence Laboratory, the MIT Julia Lab. He is recognized as a 2024 AAAS fellow for distinguished contributions and outstanding breakthroughs in high-performance computing, linear algebra, random matrix theory, computational science, and in particular for the development of the Julia programming language. Edelman has been elected a fellow of five different societies — AMS, the Society for Industrial and Applied Mathematics, the Association for Computing Machinery, the Institute of Electrical and Electronics Engineers, and AAAS.

Robert B. Millard '73, life member and chairman emeritus of the MIT Corporation, was named a 2024 AAAS Fellow for outstanding contributions to the scientific community and U.S. higher education "through exemplary leadership service to such storied institutions as AAAS and MIT." Millard joined the MIT Corporation as a term member in 2003 and was elected a life member in 2013. He served on the Executive Committee for 10 years and on the Investment Company Management Board for seven years, including serving as its chair for the last four years. He served as a member of the Visiting Committees for Physics, Architecture, and Chemistry. In addition, Millard has served as a member of the Linguistics and Philosophy Visiting Committee, the Corporation Development Committee, and the Advisory Council for the Council for the Arts. In 2011, Millard received the Bronze Beaver Award, the MIT Alumni Association’s highest honor for distinguished service.

Jagadeesh S. Moodera is a senior research scientist in the Department of Physics. His research interests include experimental condensed matter physics: spin polarized tunneling and nano spintronics; exchange coupled ferromagnet/superconductor interface, triplet pairing, nonreciprocal current transport and memory toward superconducting spintronics for quantum technology; and topological insulators/superconductors, including Majorana bound state studies in metallic systems. His research in the area of spin polarized tunneling led to a breakthrough in observing tunnel magnetoresistance (TMR) at room temperature in magnetic tunnel junctions. This resulted in a huge surge in this area of research, currently one of the most active areas. TMR effect is used in all ultra-high-density magnetic data storage, as well as for the development of nonvolatile magnetic random access memory (MRAM) that is currently being advanced further in various electronic devices, including for neuromorphic computing architecture. For his leadership in spintronics, the discovery of TMR, the development of MRAM, and for mentoring the next generation of scientists, Moodera was named a 2024 AAAS Fellow. For his TMR discovery he was awarded the Oliver Buckley Prize (2009) by the American Physical Society (APS), named an American National Science Foundation Competitiveness and Innovation Fellow (2008-10), won IBM and TDK Research Awards (1995-98), and became a Fellow of APS (2000).

Noelle Eckley Selin, the director of the MIT Center for Sustainability Science and Strategy and a professor in the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences, uses atmospheric chemistry modeling to inform decision-making strategies on air pollution, climate change, and toxic substances, including mercury and persistent organic pollutants. She has also published articles and book chapters on the interactions between science and policy in international environmental negotiations, in particular focusing on global efforts to regulate hazardous chemicals and persistent organic pollutants. She is named a 2024 AAAS Fellow for world-recognized leadership in modeling the impacts of air pollution on human health, in assessing the costs and benefits of related policies, and in integrating technology dynamics into sustainability science.

Additional MIT alumni honored as 2024 AAAS Fellows include: Danah Boyd SM ’02 (Media Arts and Sciences); Michael S. Branicky ScD ’95 (EECS); Jane P. Chang SM ’95, PhD ’98 (Chemical Engineering); Yong Chen SM '99 (Mathematics); Roger Nelson Clark PhD '80 (EAPS); Mark Stephen Daskin ’74, PhD ’78 (Civil and Environmental Engineering); Marla L. Dowell PhD ’94 (Physics); Raissa M. D’Souza PhD ’99 (Physics); Cynthia Joan Ebinger SM '86, PhD '88 (EAPS/WHOI); Thomas Henry Epps III ’98, SM ’99 (Chemical Engineering); Daniel Goldman ’94 (Physics); Kenneth Keiler PhD ’96 (Biology); Karen Jean Meech PhD '87 (EAPS); Christopher B. Murray PhD ’95 (Chemistry); Jason Nieh '89 (EECS); William Nordhaus PhD ’67 (Economics); Milica Radisic PhD '04 (Chemical Engineering); James G. Rheinwald PhD ’76 (Biology); Adina L. Roskies PhD ’04 (Philosophy); Linda Rothschild (Preiss) PhD '70 (Mathematics); Soni Lacefield Shimoda PhD '03 (Biology); Dawn Y. Sumner PhD ’95 (EAPS); Tina L. Tootle PhD ’04 (Biology); Karen Viskupic PhD '03 (EAPS); Brant M. Weinstein PhD ’92 (Biology); Chee Wei Wong SM ’01, ScD ’03 (Mechanical Engineering; and Fei Xu PhD ’95 (Brain and Cognitive Sciences). 

Taking out a loan to attend college is an investment in your future. But unlike in the United States, students in Pakistan don’t have easy access to college loans. Instead, most families must stomach higher interest rates for personal loans that can require collateral like land or homes. As a result, college is inaccessible for many students. It’s one reason why only about 13 percent of Pakistani students attend college.

Now EduFi, founded by Aleena Nadeem ’16, is offering low-interest student loans to a broader swath of Pakistanis. EduFi, which is short for “education finance,” uses an artificial intelligence-based credit scoring system to qualify borrowers and pay colleges directly. The borrowers then make monthly payments to EduFi along with a service fee of 1.4 percent — far lower than what is available for most students today.

“The fees for college are extremely unaffordable for the average middle-class person right now,” Nadeem explains. “With our ‘Study Now, Pay Later’ system, we’re breaking that big upfront cost into installments, which makes it more affordable for both existing college students and a new group of people that never thought higher education was possible.”

EduFi was incorporated in 2021, and after gaining regulatory approval, the company began disbursing loans to people across Pakistan last year. In the first six months, EduFi disbursed more than half a million dollars in loans. Since then, the company’s inclusive approach to qualifying applicants has been validated: Today, less than 1 in 10,000 of those loans are not being repaid.

As awareness about EduFi grows, Nadeem believes the company can contribute to Pakistan’s modernization and development more broadly.

“We are accepting so many more people that would not have been able to get a bank loan,” Nadeem says. “That gets more people to go to college. The impact of directing cheap and fast credit to the educational sector on a developing country like Pakistan is huge.”

Better credit

At the British international high school Nadeem attended, no one had ever gotten into an Ivy League school. That made her acceptance into MIT a big deal.

“It was my first choice by far,” Nadeem says.

When she arrived on campus, Nadeem took classes at MIT that taught her about auctions, risk, and credit.

“In the work I’m doing with EduFi now, I’m applying what I learned in my classes in the real world,” Nadeem says.

Nadeem worked in the credit division at Goldman Sachs in London after graduation, but barriers to accessing higher education in her home country still bothered her.

In Pakistan, some targeted programs offer financial support for students with exceptionally high grades who can’t afford college, but the vast majority of families must find other ways to finance college.

“Most students and their families have to get personal loans from standard banks, but that requires them to open a bank account, which could take two months,” Nadeem explains. “Fees in Pakistan’s education sector must be paid soon after the requests are sent, and by the time banks accept or reject you, the payment could already be late.”

Private loans in Pakistan come with much higher interest rates than student loans in America. Many loans also require borrowers to put up property as collateral. Those challenges prevent many promising students from attending college at all.

EduFi is using technology to improve the loan qualification process. In Pakistan, the parent is the primary borrower. EduFi has developed an algorithmic credit scoring system that considers the borrower’s financial history then makes payments directly to the college on their behalf. EduFi also works directly with colleges to consider the students’ grades and payment history to the school.

Borrowers pay back the loan in monthly installments with a 1.4 percent service fee. No collateral is required.

“We are the first movers in student lending and currently hold the largest student loan portfolio in the country,” Nadeem says. “We’re offering extremely subsidized rates to a lot of people. Our rates are way cheaper than the bank alternatives. We still make a profit, but we’re impact-focused, so we make profit through disbursing to a larger number of people rather than increasing the margin per person.”

Nadeem says EduFi’s approach qualifies far more people for loans compared to banks and does so five times faster. That makes college more accessible for students across Pakistan.

“Banks charge high interest rates to the people with the best credit scores,” Nadeem says. “By not taking collateral, we really open up the credit space to new people who would not have been able to get a bank loan. Easier credit gives the average middle-class individual the ability to change their families’ lives.”

Helping countries by helping people

EduFi received its non-banking financial license in February 2024. The company gained early traction last year through word of mouth and soon opened to borrowers across the country. Since then, Nadeem says many people have traveled long distances to EduFi’s headquarters to confirm they’re a credible operation. Nadeem also regularly receives messages from students across Pakistan thanking EduFi for helping them attend college.

After further proving out its model this year, EduFi plans to expand to Saudi Arabia. Eventually, it plans to offer its loans to students throughout the Middle East, and Nadeem believes the global student loan system could be improved using EduFi’s approach.

“EduFi is modeled after SoFi in San Francisco,” Nadeem says of the large finance company that started by offering student loans and expanded to mortgages, credit cards, and other banking services. “I’m trying to build the SoFi of Pakistan and the Middle East. But it’s really a combination of SoFi and Grameen Bank [in Bangladesh], which extends credit to lower-income people to lift them out of poverty.”

By helping people extend their education and reach their full potential, Nadeem believes EduFi will one day accelerate the development of entire nations.

“Education is the core pillar from which a country stands,” Nadeem says. “You can’t progress as a country without making education as accessible and affordable as possible. EduFi is achieving that by directing capital at what is frankly a starving education sector.”

Earle Leonard Lomon PhD ’54, MIT professor emeritus of physics, died on March 7 in Newton, Massachusetts, at the age of 94.  

A longtime member of the Center for Theoretical Physics, Lomon was interested primarily in the forces between protons and neutrons at low energies, where the effects of quarks and gluons are hidden by their confinement.

His research focused on the interactions of hadrons — protons, neutrons, mesons, and nuclei — before it was understood that they were composed of quarks and gluons. 

“Earle developed an R-matrix formulation of scattering theory that allowed him to separate known effects at long distance from then-unknown forces at short distances,” says longtime colleague Robert Jaffe, the Jane and Otto Morningstar Professor of Physics.

“When QCD [quantum chromodynamics] emerged as the correct field theory of hadrons, Earle moved quickly to incorporate the effects of quarks and gluons at short distance and high energies,” says Jaffe. “Earle’s work can be interpreted as a precursor to modern chiral effective field theory, where the pertinent degrees of freedom at low energy, which are hadrons, are matched smoothly onto the quark and gluon degrees of freedom that dominate at higher energy.”

“He was a truly cosmopolitan scientist, given his open mind and deep kindness,” says Bruno Coppi, MIT professor emeritus of physics.

Early years

Born Nov. 15, 1930, in Montreal, Quebec, Earle was the only son of Harry Lomon and Etta Rappaport. At Montreal High School, he met his future wife, Ruth Jones. Their shared love for classical music drew them both to the school's Classical Music Club, where Lomon served as president and Ruth was an accomplished musician.

While studying at McGill University, he was a research physicist for the Canada Defense Research Board from 1950 to 1951. After graduating in 1951, he married Jones, and they moved to Cambridge, where he pursued his doctorate at MIT in theoretical physics, mentored by Professor Hermann Feshbach.

Lomon spent 1954 to 1955 at the Institute for Theoretical Physics (now the Niels Bohr Institute) in Copenhagen. “With the presence of Niels Bohr, Aage Bohr, Ben Mottelson, and Willem V.R. Malkus, there were many physicists from Europe and elsewhere, including MIT’s Dave Frisch, making the Institute for Physics an exciting place to be,” recalled Lomon.

In 1956-57, he was a research associate at the Laboratory for Nuclear Studies at Cornell University. He received his PhD from MIT in 1954, and did postdoctoral work at the Institute of Theoretical Physics in Denmark, the Weizmann Institute of Science in Israel, and Cornell. He was an associate professor at McGill from 1957 until 1960, when he joined the MIT faculty.

In 1965, Lomon was awarded a Guggenheim Memorial Foundation Fellowship and was a visiting scientist at CERN. In 1968, he joined the newly formed MIT Center for Theoretical Physics. He became a full professor in 1970 and retired in 1999.

Los Alamos and math theory

From 1968 to 2015, Lomon was an affiliate researcher at the Los Alamos National Laboratory. During this time, he collaborated with Fred Begay, a Navajo nuclear physicist and medicine man. New Mexico became the Lomon family’s second home, and Lomon enjoyed the area hiking trails and climbing Baldy Mountain.   

Lomon also developed educational materials for mathematical theory. He developed textbooks, educational tools, research, and a creative problem-solving curriculum for the Unified Science and Mathematics for Elementary Schools. His children recall when Earle would review the educational tools with them at the dinner table. From 2001 to 2013, he was program director for mathematical theory for the U.S. National Science Foundation’s Theoretical Physics research hub.

Lomon was an American Physical Society Fellow and a member of the Canadian Association of Physicists.

Husband of the late Ruth Lomon, he is survived by his daughters Glynis Lomon and Deirdre Lomon; his son, Dylan Lomon; grandchildren Devin Lomon, Alexia Layne-Lomon, and Benjamin Garner; and six great-grandchildren. There will be a memorial service at a later date; instead of flowers, please consider donating to the Los Alamos National Laboratory Foundation. 

Around 11 billion tons of goods, or about 1.5 tons per person worldwide, are transported by sea each year, representing about 90 percent of global trade by volume. Internationally, the merchant shipping fleet numbers around 110,000 vessels. These ships, and the ports that service them, are significant contributors to the local and global economy — and they’re significant contributors to greenhouse gas emissions.

A new consortium, formalized in a signing ceremony at MIT last week, aims to address climate-harming emissions in the maritime shipping industry, while supporting efforts for environmentally friendly operation in compliance with the decarbonization goals set by the International Maritime Organization.

“This is a timely collaboration with key stakeholders from the maritime industry with a very bold and interdisciplinary research agenda that will establish new technologies and evidence-based standards,” says Themis Sapsis, the William Koch Professor of Marine Technology at MIT and the director of MIT’s Center for Ocean Engineering. “It aims to bring the best from MIT in key areas for commercial shipping, such as nuclear technology for commercial settings, autonomous operation and AI methods, improved hydrodynamics and ship design, cybersecurity, and manufacturing.” 

Co-led by Sapsis and Fotini Christia, the Ford International Professor of the Social Sciences; director of the Institute for Data, Systems, and Society (IDSS); and director of the MIT Sociotechnical Systems Research Center, the newly-launched MIT Maritime Consortium (MC) brings together MIT collaborators from across campus, including the Center for Ocean Engineering, which is housed in the Department of Mechanical Engineering; IDSS, which is housed in the MIT Schwarzman College of Computing; the departments of Nuclear Science and Engineering and Civil and Environmental Engineering; MIT Sea Grant; and others, with a national and an international community of industry experts.

The Maritime Consortium’s founding members are the American Bureau of Shipping (ABS), Capital Clean Energy Carriers Corp., and HD Korea Shipbuilding and Offshore Engineering. Innovation members are Foresight-Group, Navios Maritime Partners L.P., Singapore Maritime Institute, and Dorian LPG.

“The challenges the maritime industry faces are challenges that no individual company or organization can address alone,” says Christia. “The solution involves almost every discipline from the School of Engineering, as well as AI and data-driven algorithms, and policy and regulation — it’s a true MIT problem.”

Researchers will explore new designs for nuclear systems consistent with the techno-economic needs and constraints of commercial shipping, economic and environmental feasibility of alternative fuels, new data-driven algorithms and rigorous evaluation criteria for autonomous platforms in the maritime space, cyber-physical situational awareness and anomaly detection, as well as 3D printing technologies for onboard manufacturing. Collaborators will also advise on research priorities toward evidence-based standards related to MIT presidential priorities around climate, sustainability, and AI.

MIT has been a leading center of ship research and design for over a century, and is widely recognized for contributions to hydrodynamics, ship structural mechanics and dynamics, propeller design, and overall ship design, and its unique educational program for U.S. Navy Officers, the Naval Construction and Engineering Program. Research today is at the forefront of ocean science and engineering, with significant efforts in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. The consortium’s academic home at MIT also opens the door to cross-departmental collaboration across the Institute.

The MC will launch multiple research projects designed to tackle challenges from a variety of angles, all united by cutting-edge data analysis and computation techniques. Collaborators will research new designs and methods that improve efficiency and reduce greenhouse gas emissions, explore feasibility of alternative fuels, and advance data-driven decision-making, manufacturing and materials, hydrodynamic performance, and cybersecurity.

“This consortium brings a powerful collection of significant companies that, together, has the potential to be a global shipping shaper in itself,” says Christopher J. Wiernicki SM ’85, chair and chief executive officer of ABS. 

“The strength and uniqueness of this consortium is the members, which are all world-class organizations and real difference makers. The ability to harness the members’ experience and know-how, along with MIT’s technology reach, creates real jet fuel to drive progress,” Wiernicki says. “As well as researching key barriers, bottlenecks, and knowledge gaps in the emissions challenge, the consortium looks to enable development of the novel technology and policy innovation that will be key. Long term, the consortium hopes to provide the gravity we will need to bend the curve.”