Last fall, MIT Campus Dining recruited a group of students to make short videos and share their experiences as student diners on Instagram. The MIT Dining Ambassadors program is an effort to get students talking about — and helping to improve — MIT’s food services and systems. 

One of the inaugural ambassadors, Michaela Brown, a biochemical engineering major from Kingston, Jamaica, sat down to discuss what she’s learning as an ambassador, how she has adapted to dining-hall life, the best things about her mom’s cooking, what it was like to experience American Thanksgiving for the first time, and more.

Q: How did you get involved in the Dining Ambassadors program? 

A: Last October, my friend got a job. So I was like, I need to get a job. When I read the description, I said, Wait, this involves food, and talking to people, and posting on Instagram? That’s literally what I do every day. And I wanted to do my part. 

The ambassadors program has clear goals: They want to encourage students to use the dining halls, and they want us to find genuine issues MIT can work on. I wanted to be a part of that. The food at MIT is OK, but everything can be better. And you can’t make things better in any circumstance without trying. Plus — getting paid to eat food and talk? That is good money.

Q: What did you eat growing up? 

A: I love Jamaican food. On Sunday, we do a big dinner. (Well, my mom would do a big dinner; sometimes I would wash the vegetables.) She would cook rice, peas, and vegetables with a sauce, and either fried chicken with sauce or stewed chicken. We would eat that food on Sunday, and then maybe Monday, too. We call it “Sunday-Monday” in Jamaica. 

During the week, we eat flour dumplings, boiled green bananas, and lots of plantains. Sometimes, when my mom is on a health kick, she will boil everything, but plantains are so much better when you fry them! Often, she will serve that with ackee. That’s our national food. And she will cook saltfish or mackerel mixed with coconut milk. She also makes things like corned beef or tuna. On Fridays, we usually go out. 

For special occasions, sometimes we do pork or oxtail. Or sometimes we have escovitch fish; I think you fry it and you steam it. And then we have sides like dumpling, or banana, or bami, which is fried flour. And usually we eat these with okra and pickled onions, and add a little spice with Scotch bonnet pepper. 

And curry chicken! If I am home and I smell the curry, I get so happy. I genuinely feel better about myself. If you’re buying food from a vendor, like fried chicken and rice, you would ask for curry gravy because it is very essential in Jamaican culture. 

Q: What was your first project for the ambassadors program?

A: I did a video about Thanksgiving. I was excited, because it would be my first American Thanksgiving. As a kid in Jamaica, I saw it on TV. I watched Nickelodeon. Also, we learned about it in school. But we didn’t do Thanksgiving in Jamaica. So I was excited.

In the video, I was trying to cater to students who don’t normally celebrate Thanksgiving and show them the experience from a fresh perspective. I brought my friends with me and we all ate together. And luckily everyone thought the food was good. I really wanted to show the food — the mashed potatoes, the turkey, the jelly, the ham, all those things — because I think New Vassar did it really well. I wanted to show that. 

There are a lot of international students at MIT. I didn’t know what MIT was like until I got here. I wanted to show that I came here and liked it. Even while I was missing home, I was being introduced to other cultures — like the one in America — and MIT was helping me appreciate it through food.

Also, I wanted to show the community — being with my friends, giving thanks for the people around me. I really enjoyed that, and I thought it went well. My mom loved it.

Q: What have you done since then? 

A: Usually, I just try to take pictures when I’m in a dining hall and post them on Instagram. You know — regular life.

The other major thing was the global Olympics. Each day over two weeks, they had a special theme at each of the dining halls — Latin American at New Vassar, East Asian at Simmons, African at McCormick, European at Next House, Indian at Massey, and North American and Caribbean at Baker. 

My favorite was Baker, because, well, I’m a little biased. And also, I love the burgers at Baker. 
I told my friends they had to come. A lot of the cooking staff in Baker are Haitian. They would know how food from Haiti and Jamaica should taste. I knew they wouldn’t mess it up. 

I interviewed a lot of students, including two Haitians and one of my Jamaican friends. I asked about the food, about how it compared to regular dining hall meals. They were really positive. I think they liked the change. 

Q: Do you like to cook? 

A: Not really. The summer before I came here, I was like, OK, I’m gonna learn something. 
And then I proceeded to spend the summer out with my friends, and volunteering. So I wasn’t really in the kitchen. My mom would call me to come help her, and when I stepped in the kitchen it was so hot! I was like, I can’t do this, and I went back to my room. 

So I’m not really a cook, even though I live in Burton-Connor. It’s a cook-for-yourself dorm, so it doesn’t have a dining hall. A few weeks ago, I tried to do burritos. I got the beef and the seasoning. It was actually really good! I’m looking forward to it again. It’s just really hard to find the time. 


Q: When you’re posting, who do you imagine is looking at it? 

A: My friends. And my mom. Honestly, I just try to make sure you can understand what I’m saying because sometimes my Jamaican patois comes out, and I talk too fast. I also think about how the people I’m interviewing want to be seen, because this is not their job. They don’t have to be on camera, or help me. I try to make the experience as fun as possible for them. 

Q: What have you learned doing this work?

A: Walking up to strangers and getting their permission to record them is really new to me. I have learned so much about people. The other day, I was looking at a job application and it asked: Are you comfortable talking to other people and being social? This job has prepared me for all that so well. 

It also prepared me for dealing with people who might not be open to talking. I have learned to be OK with that, just walking away and handling it well. This is a skill set that I have now, and I look forward to working more and doing more interviews. I feel like, you know, a YouTuber!

Q: What dining stories do you want to tell next? 

A: I’m not sure. Dining is different for different people. For me personally, sometimes eating is a time to get together with other people. But sometimes I go to the dining hall by myself. It’s very much a time for me to decompress. Sometimes I don’t even want anyone to sit with me. I’m just trying to be with myself, watch my show, or do the learning sequence I have due at 11 o’clock. Or I just watch my TikToks. 

Maybe I’ll do a day-in-my-life dining story next, and go for breakfast at a dining hall. I would have to wake up earlier, but I would do it. 

In his 1927 paper, “A law of comparative judgment,” the American psychologist L. L. Thurstone proposed that when people select one option among multiple alternatives, they are picking the one that has the highest value to them, even though they cannot assign a particular number to that choice. 

Thurstone was a pioneer of “psychometrics” — a field built upon the premise that mental processes, which we cannot see, can nevertheless be measured and quantified. His 1927 paper laid the groundwork for what are now called random utility models, which provide a mathematical framework for describing human preferences — information that can be relied upon, in turn, to make predictions about various hypothetical situations.

Random utility models (RUMs) are so named because they assess the “utility,” or benefit, that can be obtained from a given choice — such as deciding which book to read first among the stack of novels you brought back from the library. “These models are inherently random,” explains Gabriele Farina, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and principal investigator at the Laboratory for Information and Decision Systems (LIDS), “because people are different. Everyone has their own preferences, and even those preferences can vary from time to time.” For example, someone who normally picks coffee over tea in the morning, and prefers tea after dinner, may, upon occasion, mix up that order entirely.

RUMs, to be sure, are frequently used within government and industry in situations of far greater consequence than the selection of a hot (or iced) beverage. The models routinely facilitate predictions regarding what people will elect to do in so-called counterfactual (“what-if”) scenarios such as: How will they get to work or school if a major thoroughfare is shut down for construction? What routes and modes of transport will they take? Or, if a city suddenly receives a windfall of $20 million, how should those funds be disbursed to maximize the common good?

Given that RUMs have been with us for almost 100 years, growing in sophistication over time, one might imagine that, at this stage, there would be little room for improvement. That, however, is not the case. 

A paper presented in April at the International Conference on Learning Representations in Rio de Janeiro, Brazil, uncovered basic facts that show there is much more to be gleaned from these models than had traditionally been supposed. The paper was authored by Yeshwanth Cherapanamjeri, a former MIT postdoc now based at Nanyang Technological University in Singapore; Farina, also core faculty in MIT’s Operations Research Center (ORC); Constantinos Daskalakis, the Avanessians Professor of Computer Science at MIT and a member of MIT's Computer Science and Artificial Intelligence Laboratory; and Sobhan Mohammadpour, an MIT PhD student in computer science based at LIDS and EECS.

The group’s findings stem, in part, from a deficiency in the way RUMs are commonly estimated in practice, which has persisted since the days of Thurstone. The data upon which the models are estimated have been largely drawn from so-called pairwise-comparisons: In a choice between items A and B — whether it pertains to movies on Netflix, competing products on Amazon.com, news stories posted on Google, and so forth — which one would you pick? One reason this approach has been so pervasive, explains Daskalakis, is that “assigning a precise numerical score, such as 4.37, to the benefit you get from a single item is very hard. Whereas comparing two things, and deciding which one you like better, is cognitively much easier to do.” But therein lies the rub, he adds. “With this way of assessing people’s preferences, looking at just two things at a time, it is impossible to find correlations between the numerous choices.”

The standard way of applying RUMs assumes that the utilities derived from A and B are independent, but they may, in fact, be linked, and that would be important to know. If someone campaigning for elective office finds out that a potential voter favors gun control, for instance, there is a reasonable chance that same person also favors government-sponsored child care. Similarly, a fan of independent movies might also be partial to foreign films, but less enthusiastic about Hollywood action blockbusters. “If a digital platform has a blind eye to the existence of such correlations, it will not be able to estimate preferences very accurately,” Daskalakis notes. “And if Netflix regularly shows you an assortment of movies you don’t care about, you might sign off and cancel your subscription.”

The MIT team proved that it is impossible to get information about correlations from two-way comparisons alone. Correlations can be discerned, however, when large numbers of people rate three alternatives in their order of preference. The same information can also be obtained from a combination of best-of-three and best-of-two choices. In practice, Mohammadpour explains, “you would get a bunch of people to rank three items. You could then utilize the method we developed for merging those individual results into one big model that can provide us with the big picture.”

Their research effort, according to Farina, is focused on the computational side of RUMs, devising algorithms that can extract preference information and figuring out how much data is needed to do so or, equivalently, how many experiments need to be run. The good news, he says, is that efficient algorithms are, indeed, possible for this purpose. The requisite number of experiments does not grow exponentially with the number of items in the catalog or database that’s under review.

“This paper provides a crucial breakthrough,” comments Emma Frejinger, a computer scientist at the University of Montreal. “It mathematically proves why traditional data collection fails and demonstrates that simply asking users for their best-of-three [choices] unlocks the ability to accurately train these powerful models. This finding provides a highly practical roadmap for collecting better data to drive more accurate optimizations.”

“Building utility models is going to remain a very active area,” Daskalakis insists. “Just as RUMs have been critical to the internet economy since the late 1990s, they are, and will remain to be, critical to the alignment of AI models going forward.” More importantly, he adds, “RUMs play a central role in the commercial viability and usefulness of large language models [LLMs].” During the training period, people are typically asked to rank the various candidate outputs of these LLMs, from which the models can gain a better sense as to the kind of text — in terms of tone, style, and content — that is preferred. 

Given that we’re constantly “besieged with a vast sea of options in so many different domains,” Daskalakis says, “you cannot possibly ask people to communicate all their personal preferences for all possible scenarios. So what you can do instead is build a model that predicts what people think about the different possible outcomes. And you have to keep improving and updating your model in an iterative process until, hopefully, you can make good predictions.”

One September day, it started to snow inside MIT’s Pierce Laboratory. 

Researchers depressurized a tank of liquid carbon dioxide (CO2), instantly freezing it and releasing solid flakes. These were blended into cement paste and pressed into discs roughly the size of a dime, each sealed with a thin layer of vegetable oil to keep water in and air out. The team trained lasers on each, observing for the first time the transient chemical reaction that might explain why CO2-injected cement paste gains its strength faster.

Injecting CO2 into cement products like concrete is one way to store it and keep it out of the atmosphere. The process has attracted commercial interest, with a growing number of companies offering CO2-injected concrete mixes. But until now, the underlying cement chemistry hadn't been directly visualized.

A new open-access paper in the Journal of the American Ceramic Society — led by Associate Professor Admir Masic and first-authored by graduate student Marcin Hajduczek, both of the MIT Concrete Sustainability Hub and MIT Department of Civil and Environmental Engineering — describes the chemical sequence that unfolds after CO2 meets fresh cement paste. Co-authors include MIT colleagues Santiago El Awad and Franz-Josef Ulm, alongside researchers from IIT Jodhpur and CarbonCure Technologies.

Previous studies had pieced together a story about CO2 injection’s chemical impacts from theory and indirect evidence; the key reactions simply moved too fast, and vanished too completely, for conventional techniques to catch them in the act. Raman confocal microscopy could — and it works on a simple principle: Illuminate a molecule with a laser, and the scattered light will reveal its identity. The light interacts with each material’s unique chemical bonds, shifting in energy to produce a distinct spectral “fingerprint.” Even the most fleeting and amorphous phases leave a readable trace.

“We’ve used Raman spectroscopy to better understand some of the most interesting materials in history, from the Dead Sea Scrolls to Ancient Roman concrete,” says Masic. “Cement paste may seem less glamorous in comparison, but pointing a laser at CO2-injected cement paste as it hardens allows us to visualize things that haven’t been seen before.”

What they saw, unfolding during 24 hours of continuous scanning, was a three-act chemical drama.

Act One: Capturing calcium

The moment that CO2 is added to the fresh cement paste, it goes to work. It dissolves into the pore solution and reacts with calcium released by the dissolving clinker, precipitating as various forms of calcium carbonate. Clinker is produced by heating limestone and aluminosilicate materials in a kiln, forming the primary ingredient ground into a fine powder to make cement. This happens within the first hour, temporarily slowing the normal hydration reaction, which requires calcium to proceed. 

In contrast, when CO2 is not present, the calcium released by the dissolving clinker remains available locally, supporting the gradual formation of the material’s binding phases as it sets.

Left without calcium, the silicates released by the clinker dissolve into the pore solution and precipitate far from their source, linking together into chains that form an interconnected silica gel network throughout the paste. This amorphous, fleeting gel sets the stage for what follows.

Act Two: The ghostly gel

Once the injected CO2 is fully mineralized — around four to five hours after mixing — normal hydration resumes. Calcium hydroxide begins to precipitate into the pore space, and when it does, it encounters the silica gel network waiting for it.

The reaction between the two phases begins immediately, producing calcium silicate hydrate (C-S-H), the compound that gives cement its binding ability. What makes this form of C-S-H distinct is where and how it forms: not clustered around clinker particles as in conventional hydration, but distributed throughout the entire matrix, wherever the silica gel had spread.

The CO2 had temporarily suppressed the paste’s alkalinity, and that lower pH was the only thing keeping the silica-gel intact. As hydration reasserts itself and produces standard hydration products, namely C-S-H and calcium hydroxide, the latter drives pH back up to typical levels in a self-reinforcing loop; the silica-gel reacts with calcium hydroxide through a so-called pozzolanic reaction. Within eight hours, the silica gel is almost entirely gone — the previously well-distributed gel network turns rapidly into additional C-S-H during this critical early window. 

“At first, the fleeting nature of the silica gel looked like a fluke in the Raman data. But it quickly became clear that its sudden disappearance was a consistent, undeniable feature of every CO2-injected sample,” says Hajduczek.

Act Three: A rewired matrix

With the silica gel consumed, the paste settles into conventional hydration, but what it leaves behind is measurably different. Because the new binder was distributed more evenly throughout the cement matrix, the resulting microstructure is stronger and more uniform at an early age. In the study, paste mixed with CO2 at 1 percent by cement weight achieved, on average, 13 percent higher compressive strength at 24 hours, compared to reference mixes.

“We’ve been injecting CO2 into cement products for years without fully understanding what it was doing inside. Now that we can see it and understand the underlying mechanism that leads to improved performance, we can start to control it. And there’s a lot of room to push,” says Masic.

The findings also refine a leading explanation for CO2-injected cement paste’s higher early age strength: the calcium carbonate crystals, previously suspected to seed C-S-H growth, turn out to be passive bystanders embedded in the silica gel template rather than reacting to form C-S-H. 

Where the chemistry goes next

Knowing the mechanism gives researchers a more specific set of questions to pursue. The silica gel template explains the distribution of the new C-S-H, but directly measuring its mechanical properties remains a next step.

On the practical side, dosage matters: Flood the system with too much CO2 and calcium gets locked into carbonate before the gel can form and react. If the paste used here forms abundant C-S-H, it could theoretically offset up to 40 percent of the carbon emissions from cement production, excluding emissions associated with the fossil fuels used in the process. In practice, however, the achievable offset is likely to be only a fraction of that value, although still potentially significant.

But even with these open questions, the ghostly gel has been caught. And now that researchers know what to look for, the chemistry that unfolds in those first eight hours is no longer invisible.

When a friend buys you a cup of coffee, it’s likely that next time, you’ll return the gesture. This type of reciprocal generosity has been well-documented in behavioral economic studies.

However, anthropologists and other social scientists have known for decades that in the context of relationships where one person has more power, status, or influence, reciprocal generosity is usually not the norm. 

Researchers at MIT have now experimentally demonstrated, for the first time, that small changes to the relationship context can dramatically change people’s actions and expectations of reciprocal generosity. 

During interactions between people of different social status, people tend to expect that generosity will flow one way, and it can be either up or down. It may be that a professor always buys coffee for her students, or that a student always offers to help carry groceries for his resident advisor. Once the precedent is established, it is expected to continue.

One interpretation of the findings is that keeping track of whose turn it is to do a favor is the exception in social interactions, not the rule. That is, it is extra work that we do when we want to maintain equal relationships.

“In many intimate relationships, hierarchical relationships, or other kinds of role-based relationships, you don’t put in the work of trying to keep track of turns,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and associate dean of science at MIT. “Under this interpretation, we just follow precedent because following a precedent is easier. We all know what to expect, and we don’t have to keep track of what happened last time.”

Saxe is the senior author of the study, which appears in the journal Open Mind. MIT graduate student Alicia Chen is the paper’s lead author.

Changing expectations

Most experimental studies of generosity have been done in the context of behavioral economics and game theory. In such experiments, people are usually paired with a stranger and asked to play games that require coordination. Such studies have found that people tend to use turn-taking and reciprocity as their default strategies. These scenarios, however, are stripped from any social context that might exist between people in the real world.

Saxe and Chen wanted to see if they could measure the effects of social context by incorporating relationships into the type of experiments used to evaluate people’s expectations regarding generosity.

“Where generosity becomes hard and complicated is when it starts to occur in the context of existing relationships, because it changes the terms of the relationships,” Saxe says. “What’s expected of you is very different within a relationship than outside of one.”

To study these effects, the researchers designed experiments in which participants read stories about different types of interactions. In some of the scenarios, the subjects of the stories were described as having either symmetric or asymmetric relationships. In others, they were given specific social relationships such as aunt-niece or manager-employee.

Each story described interactions that might be seen in typical daily life, such as buying coffee for a co-worker or preparing a meal for one’s family. Participants were then asked to predict what would happen the next time the interaction occurred.

In all of these scenarios, the researchers found that people expected that generous acts would be reciprocated when they occurred between individuals in symmetric relationships such as friends, cousins, or co-workers of equal rank. However, their expectations changed for asymmetric relationships, where each person has a different social status. In those cases, people expected that any precedent that was set would continue in the future.

One possible explanation for this is that reciprocity is not the norm but an exception that only occurs in the interactions between equals or strangers, the researchers say. Many of our interactions are with people with whom we have asymmetric relationship, and to maintain those relationships, it’s simply easier to follow precedent.

“If there’s no need to keep track of our equal status, then in some ways it’s the default to fall back on following precedents,” Saxe says.

Maintaining relationships

The study showed that in asymmetric relationships, generosity could flow in either direction. Once that direction was established, it was expected to continue. For example, after an older brother bought concert tickets for a much younger brother, the study participants expected that the older brother would also buy the tickets for the next concert. 

“We found that when people know the relationship is asymmetric, they don’t expect reciprocity; they expect the same action to keep on going,” Chen says. “If the lower-rank person acts generously, people expect that to continue, and if the higher-rank person acts generously, people expect that to continue.”

Following precedents is not only easier, but keeping up these actions may help solidify and define existing relationships. For example, anthropologists have long known that gift-giving helps to construct and maintain social relationships. 

“Following a precedent can be a way of actively maintaining relationships and hierarchies, when the asymmetry of the exchange truly reflects the asymmetry of the relationship,” Saxe says. 

The researchers are now working on creating computational models that could be used to analyze different factors that people take into account when they’re considering whether someone might reciprocate a generous act. In addition to the factors examined in this study, others could include how much each person will benefit, what type of relationship they’re in, and culturally specific expectations of how people should act in different situations.

“One really powerful thing about these models is that we can build in existing theories, add things to the models, and then compare how much these extra factors, like considerations related to social relationships, matter in terms of explaining what people are doing,” Chen says. “This allows us to quantitatively compare the different theories to each other.”

The research was funded by the Simons Foundation Autism Research Initiative and the Patrick J. McGovern Foundation.

For remotely operated underwater vehicles, cloudy and turbulent waters are often a no-go. When vehicles settle on the seafloor or dig through a sandbed, they can kick up clouds of sediment that make it tough for onboard cameras to see through. Often, the only thing to do is to wait until the marine dust settles before a vehicle can safely proceed. 

But a new underwater mapping technique developed by engineers at MIT and the Woods Hole Oceanographic Institution (WHOI) may allow vehicles to see through murky, low-visibility waters. 

The method fuses visual images from optical cameras with acoustic data from sonar sensors. The combination enables a vehicle to quickly map the general shape of its surroundings using sonar, even in low-visibility waters. A vehicle can move toward certain shapes in the sonar-mapped environment, coming close enough for optical cameras to visually resolve specific objects in detail. 

The technique is akin to pairing a dolphin’s echolocation with a sea turtle’s close-range vision to see and navigate through murky water, in real-time. 

The researchers tested the method in tank experiments where they could control the water’s degree of visibility. Even in the cloudiest conditions, the system was able to see through the sediment to map the tank’s environment and visualize centimeter-scale details of objects in the tank. 

The team is further improving the technique, which they’ve named Sonar-MASt3R. They envision that the mapping method could safely guide underwater vehicles through murky environments for a range of applications, including scientific exploration, underwater construction and maintenance, and deep-sea recovery. 

“We hope that this work enables us to do more operations in those challenging, low-visibility environments, and helps provide more coverage in areas that are difficult to operate in today,” says Amy Phung, a graduate student in MIT’s Department of Aeronautics and Astronautics, who led the work. 

Phung presented a paper detailing Sonar-MASt3R this week at the IEEE International Conference on Robotics and Automation (ICRA). The paper’s co-author is Richard Camilli, senior scientist of applied ocean physics and engineering at WHOI. 

The best of both

To see underwater, scientists have generally taken an either/or approach, using either optical cameras or sonar sensors to guide the way. Optical cameras can provide detailed visual imagery of a scene, but only in waters that are relatively clear and well-lit. In contrast, sonar sensors perform just as well in clear and murky water; by emitting acoustic waves and measuring the time and angle at which they return, sonar sensors can determine the exact shape, distance, and depth of objects in the environment, though a sonar map lacks any visual detail. 

To get the best of both modes, scientists have looked to combine the two in a new approach known as “opti-acoustic fusion.” In a handful of prior works, research groups have merged sonar and optical data in mapping techniques that are mostly geared toward object recognition and reconstructing workplace environments. Most techniques require time to sync and process the data and therefore do not work in real-time, while only a few can map an environment in 3D. None have been applied to high-resolution mapping underwater in murky, turbid conditions. 

Phung, who is a student in the MIT-WHOI Joint Program, and Camilli, her advisor, aimed to develop an opti-acoustic fusion technique that would generate detailed 3D maps of underwater environments in real time and in low-visibility conditions. The team was motivated, in part, by challenges in safely recovering unexploded underwater mines.

“There can be old explosives in areas that make it unsafe for ships to be in, and the ability to get rid of those safely is best done by robotics,” Camilli says. “But a lot of these explosives are set in surf zone environments where visibility adds to the challenge of doing this safely. That’s one of many applications that our technique can be used for.”

Cloudy, with a chance of mapping

The new method, Sonar-MASt3R, builds on an existing technique, MASt3R, that was developed by researchers in France. MASt3R is an image matching algorithm that is trained to take in visual images of the same scene and quickly estimate the relative depth of each pixel in the scene. In this way, MASt3R can generate a 3D map of the environment in real-time, based on a camera’s 2D images. 

“The downside is that there is no sense of scale,” Phung says. “It will say ‘this pixel is five units closer than this pixel,’ but it can’t say whether that’s 5 meters or 5 feet.”

Luckily, sonar provides absolute measurements of scale. The timing of sonar reflections can be translated directly into a specific depth and distance of objects that the signals bounced off, as well as their shape and contour. 

In their new work, Phung and Camilli used sonar data to correct MASt3R’s scaling and generate precise 3D maps of underwater environments. Even in murky water, the method’s sonar-corrected map would enable a vehicle to know the precise location of objects, and therefore how far to safely move in for a closer inspection, which the vehicle could then do using conventional optical cameras.

The team tested Sonar-MASt3R in experiments with a tank that they filled with water, sediment, and a variety of objects such as a small boulder, a coffee mug, and a packing crate. Inside the tank, they also set up a robotic arm, onto which they mounted an underwater camera, and a sonar sensor. 

For each experimental run, they first carried out a sweep trajectory, in which the robotic arm slowly swept from one side of the tank to the other to capture sonar and visual data. With this first sweep, Sonar-MASt3R quickly creates a coarse sonar-based map of the shapes and contours of the tank and its objects. The coarse map is then used to record close-up camera images of the objects, which are used to improve the map resolution. A “keyframe” approach quickly compares each new image frame to the last keyframe. If a frame provides new information not contained in the last keyframe, the image is added as a new keyframe to the map. If it is similar, it is immediately discarded. In this way, the approach can quickly fill in the map with relevant visual detail, in real-time. 

The researchers tested their new approach underwater, testing eight different levels of turbidity, which they created by stirring up the tank’s sediment. Compared with other opti-acoustic fusion approaches, Sonar-MASt3R generated more accurate 3D maps and resolved smaller, centimeter-scale details, and in cloudier conditions. In the cloudiest condition, which the robotic arm’s cameras could not see through, its sonar sensors were able to generate a rough map of the tank’s hidden objects. This initial map enabled the arm to move safely through the murk and closer to specific objects, which its underwater camera could then visualize in more detail. 

“An analogy would be if you were to go into a china shop in the dark, and try to pick your way around to find a specific coffee mug without knocking things over,” Camilli offers. “This would allow you to do that.”

The team plans to test the approach in natural underwater conditions, where they suspect that the mapping task should be more straightforward. 

“In a tank, it’s like an echo chamber,” Camilli says. “It’s like trying to do this in a funhouse mirror setting where you get all these distortions and reverberations and ghost images that really complicates the processing. If you put it in the real world, it should be easier.”

Then, they say, Sonar-MASt3R could help scientists safely explore in cloudy, turbid, and murky underwater regions.

“The real value in this effort is so we can use this technology in mission scenarios that are untractable right now,” Phung says. “And there are plenty of untractable missions because we don’t have the observational or perception capabilities.”

This research was supported, in part, by NASA, and the National Science Foundation.

Myriam Heiman, the John and Dorothy Wilson Professor of Neuroscience at MIT, will become the director of MIT’s Picower Institute for Learning and Memory, effective July 1. She succeeds Picower Professor Li-Huei Tsai, who is stepping down after leading the institute for 16 years.

Heiman, a molecular neurobiologist and geneticist, studies the neurodegenerative diseases of the brain’s basal ganglia, including Huntington’s disease and Parkinson’s disease. Using cutting-edge techniques, including single-cell genomics and a powerful transcriptomic technique she helped invent, called translating ribosome affinity purification, she aims to understand the molecular changes that eventually lead to cell death in these diseases. 

“Myriam is an extraordinary scientist, a proven leader within MIT, and a deeply caring and generous mentor. Her research to determine why specific brain cell types are particularly vulnerable to diseases such as Huntington’s has produced studies that are both deep in their insight and sweeping in their scope,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “I firmly believe that Myriam will be an excellent leader during the Picower Institute’s next chapter.”

“I am honored to take on this role to support the institute’s exceptional scientists and trainees as they pursue discoveries that deepen our understanding of the brain and improve human health,” says Heiman, a professor in MIT’s Department of Brain and Cognitive Sciences (BCS). 

The Picower Institute is a community of 16 neuroscience labs dedicated to understanding the mechanisms that drive learning and memory and related functions such as cognition, emotion, perception, behavior, and consciousness. Institute neuroscientists explore the brain and nervous system at multiple scales, from genes and molecules to cells and synapses to circuits and systems, producing novel insights into how disruptions in these mechanisms can lead to developmental, psychiatric, or neurodegenerative disease. 

Picower Professor Susumu Tonegawa founded the institute as a center in 1994 before a transformative gift from Barbara and Jeffry Picower enabled it to become an institute in 2002. Li-Huei Tsai has served as director since 2009, but announced in March that she would step down after more than 16 years to focus on her research.

Heiman joined the Picower Institute, BCS, and the Broad Institute of Harvard and MIT in 2011, after completing her postdoctoral training at The Rockefeller University. She holds a PhD from Johns Hopkins University and a BA from Princeton University. 

“Ever since joining the institute, Heiman’s research has been guided by the principle that fundamental understanding can lead to breakthroughs in addressing disease,” Tsai says. “Myriam has made it her mission to address these kinds of urgent questions in neuroscience.”

Heiman employs sophisticated DNA and RNA analysis technologies to gain detailed insights into how brain cell states change amid disease, revealing molecular pathways that contribute to the particular vulnerability of different cell types. In 2020, Heiman published the results of an innovative in vivo screening of every mouse gene’s impact on the survival of neurons in the brain, identifying hundreds necessary for sustaining neurons and highlighting a specific gene that promoted their resilience in the context of Huntington’s disease. 

Other studies, both in mice and in postmortem human brain samples, have revealed errant immune responses in neurons and in the brain’s blood vessels that contribute to the disease’s progression. The latter finding arose in a 2022 paper, published with MIT Computer Science and Artificial Intelligence Laboratory colleague Manolis Kellis, that also provided the field one of the first cellular atlases of the brain’s vasculature.  

Her research has also produced insights into other neurodegenerative and psychiatric disorders, including ALS and frontotemporal dementia. In 2024, together with Kellis, Heiman published a paper in Cell showing the diseases have remarkable overlaps at the cellular and molecular levels, revealing potential targets that could yield therapies applicable to both disorders. Heiman’s latest research is also producing new insights into substance use disorders and schizophrenia.

Her research program has garnered many awards. In 2021, Heiman became co-recipient of a National Institutes of Health Transformative Research Award, which “promotes cross-cutting, interdisciplinary approaches that could potentially create or challenge existing paradigms” as part of the NIH’s High-Risk, High-Reward Research program. The next year she also received a prestigious NIH R35 grant to find early triggers of disease progression.

Heiman is also a dedicated teacher and mentor. In 2017, she earned the Department of BCS award for excellence in graduate mentoring; and in 2020, she received the department’s award for excellence in undergraduate teaching. In 2024, she was named one of 23 faculty across MIT who are “committed to caring” — an award given out by MIT’s Office of Graduate Education to faculty members who have served as exceptional mentors to graduate students.

Beyond MIT, Heiman serves on editorial boards and the scientific advisory board of the nonprofit Huntington’s Disease Foundation, an organization that supports research aimed at finding treatments and a cure for Huntington’s and related disorders.. 

Heiman says she is looking forward to her new role in service to MIT by leading the Picower Institute.

“I approach this role with humility and enormous enthusiasm,” Heiman says. “The Picower Institute has an extraordinary legacy, and I’m eager to do everything I can to help support the next generation of transformative research.”

A new kernel, or core program within an operating system, gives researchers a cleaner view of what’s happening inside a processor. Called Fractal and developed at MIT, the kernel has already surfaced previously unknown behavior in Apple’s M1.

When security researchers want to understand what a modern processor is really doing with the kind of detail that determines whether attacks like Spectre and Meltdown are possible, they usually run their experiments on top of an operating system that was never built for the job. They open up macOS or Linux, patch the kernel by hand, and hope the modifications hold. The approach is unstable, hard to reproduce, and on Apple’s platforms, slated for deprecation.

A team at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) decided to build something different. Fractal, an operating system kernel written from the ground up, treats the hardware itself as the object of study. Its first major use, a deep look at branch predictors — a CPU’s way of guessing what code to run next, before it knows for certain, so it doesn’t have to waste time waiting to find out — inside Apple’s M1 processor, has already turned up findings that prior work missed, including the first evidence that a class of speculative attack known as “Phantom” affects Apple Silicon.

“We’re using hardware in ways it wasn’t designed for,” says Joseph Ravichandran, the MIT PhD student in electrical engineering and computer science (EECS) who led the project. “It’s not even obvious that this is a possible thing you could do with the hardware. But we found a way to pull all these different primitives off. It’s like a microscope. If you’ve got a hand magnifying glass, you can see a little bit. But if you had an electron microscope, now we’re really talking. That’s what Fractal is. The electron microscope of operating systems.”

A clean room for chip research

The core problem Fractal solves is one that researchers have worked around for years. Modern processors keep state in many internal structures: branch predictors, caches, translation lookaside buffers, and more. To study how those structures behave across the boundary between user code and kernel code, two domains the chip is supposed to keep isolated, researchers need to run nearly identical experiments on each side of that boundary. On a general-purpose operating system, that is very difficult. The system itself manages privilege levels, address spaces, and scheduling, and it injects its own activity into every measurement.

Fractal inverts the model. It boots directly on bare metal, with no other software running, and exposes primitives that let a single experiment switch privilege levels at runtime while executing the same instructions in the same address space. The team calls the underlying technique multi-privilege concurrency, and it relies on a new construct they introduced: the outer kernel thread, which sits inside a user process’s memory but executes with kernel privileges.

The result is an experimental setup with almost no background noise. Where measurements taken under macOS or Linux are blurred by interrupts, scheduler activity, and address-space management, Fractal produces flat baselines and clean signals.

What Fractal found on the M1

Apple’s M1 implements an ARM specification called CSV2, which is supposed to prevent code running in one privilege level from steering speculation in another. Using Fractal, the MIT team confirmed that the protection works for the execute stage of indirect branch prediction: a user-mode program cannot make the kernel speculatively execute a chosen target through the indirect branch predictor.

But the team also found something the chip’s designers may not have intended. The CPU still fetches the target into the instruction cache before the protection kicks in. That fetch is observable through a side channel, which means user code can still influence what the kernel pulls into its caches across the privilege boundary. The same pattern appeared between processes assigned different address space identifiers.

The team also produced the first evidence that Apple Silicon exhibits Phantom speculation, a class of misprediction previously demonstrated only on AMD and Intel processors. In Phantom, ordinary instructions, including a no-op, can be misinterpreted by the CPU as branches, triggering speculative behavior the program never asked for. On the M1, Fractal showed that Phantom fetches succeed across both privilege levels and address spaces, though the execute phase remains blocked.

A separate Fractal experiment overturned a finding from earlier work on the M1’s conditional branch predictor, which had reported that cross-privilege training worked on Apple’s performance cores, but not its efficiency cores. The Fractal team showed that the conditional branch predictor has no privilege isolation at all, on either core type, and that the earlier result was likely an artifact of macOS quietly migrating threads between cores during system calls.

“For us, it is a true independent variable,” Ravichandran says. “You change the privilege level, nothing else changes. The only thing that could explain whether the attack succeeds or not is the privilege level.”

A tool, not a one-off

Fractal supports x86_64, ARM64, and RISC-V, and consists of more than 31,000 lines of code. The team designed it as infrastructure rather than as a single experiment, with familiar POSIX system calls, a C library, and ports of standard tools like vim, GCC, and the dash shell, so that researchers can move existing experiment code over with minimal friction.

The MIT team disclosed its M1 findings to Apple’s product security team. In an unusual reversal, Apple’s engineers also examined Fractal. 

The longer-term ambition is bigger than any single result. Ravichandran wants Fractal to become to microarchitecture research what tools like QEMU and FFmpeg are to their fields: shared infrastructure that the whole community builds on. 

“My hope is that our results as a community get significantly more reliable, significantly more accurate,” says Ravichadran. “With this reduced noise, this clarity, and this guarantee that you’re running on the right core, on the right system.”

“Fractal is a strong architecture contribution because it turns an often ad hoc microarchitectural reverse-engineering workflow into reusable research infrastructure,” says University of Southern California assistant professor Mengyuan Li, who wasn’t involved in the paper. “By reducing software noise and giving researchers tighter control across privilege boundaries, it makes difficult hardware experiments much easier to interpret.”

Ravichandran worked with Mengjia Yan, an MIT associate professor of EECS and CSAIL principal investigator, on the paper. Their work was supported, in part, by the National Science Foundation, the U.S. Air Force Office of Scientific Research, and ACE, which is part of a program sponsored by the U.S. Defense Advanced Research Projects Agency. They presented their work at the IEEE Symposium on Security and Privacy in San Francisco, California.

MIT professor of physics Pablo Jarillo-Herrero is among 10 researchers worldwide to receive this year’s prestigious Kavli Prize. 

Jarillo-Herrero is co-recipient of the 2026 Kavli Prize in Nanoscience “for foundational work that established the field of twistronics.” His co-recipients are professors Eva Y. Andrei at Rutgers University and Allan MacDonald from the University of Texas at Austin.

These three physicists are being honored for the theoretical foundation and experimental validation of a new field of “twistronics,” where superconductivity, magnetism, and other properties can be obtained by rotating two-dimensional materials such as graphene to a “magic angle.”

A partnership among the Norwegian Academy of Science and Letters, the Norwegian Ministry of Education and Research, and the Kavli Foundation, the Kavli Prizes are awarded every two years to “honor scientists for breakthroughs in astrophysics, nanoscience and neuroscience that transform our understanding of the big, the small and the complex.” The laureates in each field will share $1 million.

“Pablo’s groundbreaking research has once again been given well-deserved recognition,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “Pablo and his co-recipients have pioneered twistronics, very fundamental scientific research that has opened up a new field with myriad possibilities for novel quantum materials.”

In 2009, using scanning tunneling microscopy and spectroscopy on graphene, most commonly found as a single layer of carbon atoms arranged in hexagons resembling a honeycomb structure, Andrei and her research group demonstrated that small variations in twist angle profoundly modified the electronic structure. This demonstration — that geometric control, rather than chemical composition, could modify a material’s electronic structure — represented a fundamental advance in materials design and arguably launched the field now known as “twistronics.”

In 2011, MacDonald quantitatively explained the emergence of this electronic structure by geometries at discrete magic angles. This framework has since become the theoretical foundation of what are known as moiré materials, and has guided subsequent experimental and theoretical developments across a wide range of twisted and layered systems. 

In 2018, Jarillo-Herrero’s group observed correlated insulating phases and superconductivity in magic-angle twisted bilayer graphene devices. The resulting platform, “combining atomic-scale structural simplicity with electronic tunability, has enabled systematic investigations has had broad and lasting impact across nanoscience and quantum material research,” according to the Kavli Prize citation.

“It was a big surprise, because the technique we used, though conceptually straightforward, was hard to pull off in the lab,” said Jarillo-Herrero recently. He is also the Cecil and Ida Green Professor of Physics at MIT and a member of the Research Laboratory of Electronics. 

“I’m humbled and incredibly honored to be sharing this award with [Andrei and MacDonald],” Jarillo-Herrero noted in an essay describing his journey to the Kavli Prize. “I want to also emphasize that this award honors fundamental physics research in nanoscience. It is incredibly important for society to continue to support fundamental research: Although it often doesn’t have a direct near-term application, in the long run it happens to be the most transformative and impactful in society.”

“Pablo’s research has helped spark a revolution in condensed matter physics and nanoscience, inspiring physicists worldwide to explore superconductivity and other emergent phenomena in engineered quantum materials. This work could potentially lead to the creation of superconductors at room temperature, which would would have an enormous technological impact,” says Deepto Chakrabarty, physics department head and William A. M. Burden Professor in Astrophysics.

Jarillo-Herrero's win brings the number of all-time MIT faculty recipients of the Kavli Prize to nine. Prior winners include Nancy Kanwisher in neuroscience (2024), Bob Langer in nanoscience (2024), Sara Seager in astrophysics (2024), Rainer Weiss in astrophysics (2016), Alan Guth in astrophysics (2014), Mildred Dresselhaus in nanoscience (2012), Ann Graybiel in neuroscience (2012), and Jane Luu in astrophysics (2012).

Interpreting medical ultrasound images is a difficult task, requiring a technician to look at 2D images and mentally arrange them into a 3D representation of what the tissue looks like. 

To make that job easier, MIT researchers developed a new approach to ultrasound imaging that allows the user to visualize a 3D augmented-reality image of the object being scanned. Using a virtual-reality headset, they can see a precise 3D digital representation of what the object actually looks like, making it easier to identify and analyze.

This technique could help speed up the training process for ultrasound technicians and other health care providers who use ultrasound. It could also be deployed for use in hospitals, for tasks such as using ultrasound to place a needle in the right location for a biopsy.

“For training, this could make ultrasound more intuitive and more understandable. On the clinical side, it could be less time-consuming, more accurate, and also give health care providers more peace of mind. They wouldn’t have to wonder if they missed anything,” says Canan Dagdeviren, an associate professor of media arts and sciences at MIT and the senior author of the study.

MIT graduate students Jason Hou and Shrihari Viswanath are the lead authors of the paper, which appears today in Nature Communications Engineering. Other authors of the paper include Bowen Wu ’24 and two MIT Summer Research Program students, Cinay Dilibal, a senior at Dartmouth College, and Tanisha Shende, a senior at Oberlin College.

3D representations

Ultrasound imaging works by bouncing high-frequency sound waves off tissues in the body, which are then reflected back to an ultrasound transducer. The transducer converts these sound waves to electrical signals, which are used to create a 2D image of the tissue. Ultrasound technicians are trained to convert these images into a 3D mental representation of the tissue.

“It's a difficult skill to master, and there are long learning curves,” says Hou. “The hardest thing is this mental tomography bottleneck where you’re trained to reconstruct the 2D slices in your 3D mental space. That is a cognitive burden that can lead to inaccuracies in scanning.”

To reduce that cognitive load, the MIT team thought it could be helpful to combine two technologies: 3D ultrasound imaging and augmented reality (AR). 

Three-dimensional ultrasound imaging is occasionally used in fields such as fetal imaging and echocardiography, which is used to image the heart, but most 3D ultrasound imaging systems are expensive and not widely available. For this study, the MIT team used a real-time 3D system they developed recently for use in breast-cancer detection.

Their new system includes an ultrasound probe, slightly smaller than a deck of cards, that transmits information using a chirped data acquisition system (cDAQ). The probe contains an ultrasound array arranged in the shape of an empty square, a configuration that allows the array to take 3D images of the tissue below.

Because this system has fewer ultrasound elements than a typical 3D ultrasound system, it requires less power and is less expensive to build.

The data collected by the ultrasound probe can then be compressed and streamed into a 3D computer graphics engine called Unreal Engine, which converts the voxel data from the ultrasound image into a direct 3D representation of the object, with no loss of information. Wearing an AR/VR headset, the user can see this 3D rendering representing the internal structure, superimposed over the object’s actual location — like X-ray vision. By tilting their head or approaching from a different direction, the user can see different views of the object, making it easier to identify.

Easier to use

The researchers tested their new technology, which they call AR-VIU (augmented real-time volumetric imaging in ultrasound), with a group of 18 participants. Nine of the subjects were experts in ultrasound technology (including sonographers and physicians), and nine had never used ultrasound before.

Each user performed identification tasks using four different ultrasound technologies. In one condition, they viewed 2D images on a regular screen, which is the way that most ultrasounds are now performed. They also viewed 3D images on a regular screen, as well as two augmented reality conditions: one 2D and one 3D (AR-VIU).

In one round of experiments, users were asked to identify an object embedded in gelatin — such as a spring, a ball, or a screw — inside an opaque container that was scanned with ultrasound. In a second set, they were asked to use a pen to mark the location of “tissue phantom” — a gel-like material engineered to mimic human tissue. This simulates the task of locating the right spot for a needle during a biopsy.

The researchers found that the AR-VIU system significantly improved all users’ ability to identify and locate objects. The effect was especially strong for novices, who performed nearly as well as experts when using AR-VIU. When using the traditional 2D imaging system, experts performed much better than novices.

“Overlaying images with the anatomy and providing 3D visual context makes ultrasound significantly easier for novices to understand,” Viswanath says.

In interviews after the experiments, most of the novices reported that they preferred the AR-VIU approach, with many saying that it made the tasks easier.

“The 3D system imposes less brain drain, it’s more intuitive, and it’s easier to understand what is happening in the targeted region,” Dagdeviren says.

Many of the experts said they preferred the traditional 2D imaging because that is what they were accustomed to and had been trained to use. However, those experts also said they could see the benefits of the AR-VIU system in some situations, such as placing a needle for a biopsy or visualizing the movement of the heart wall during echocardiography.

The researchers are now working on further improving the resolution of the imaging and doing additional tests to demonstrate the accuracy of the AR-VIU technology.

The research was funded by the MIT Media Lab Consortium, the National Science Foundation, an MIT HEALS graduate fellowship, and an MIT-Tata graduate fellowship.