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      in Energy

      Machine learning facilitates “turbulence tracking” in fusion reactors

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      Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.

      A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions.

      Monitoring the formation and movements of these structures, called filaments or “blobs,” is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics. Individual blob information must be tracked by marking them manually in video data. 

      The researchers built a synthetic video dataset of plasma turbulence to make this process more effective and efficient. They used it to train four computer vision models, each of which identifies and tracks blobs. They trained the models to pinpoint blobs in the same ways that humans would.

      When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved.

      Because millions of video frames are captured during just one fusion experiment, using machine-learning models to track blobs could give scientists much more detailed information.

      “Before, we could get a macroscopic picture of what these structures are doing on average. Now, we have a microscope and the computational power to analyze one event at a time. If we take a step back, what this reveals is the power available from these machine-learning techniques, and ways to use these computational resources to make progress,” says Theodore Golfinopoulos, a research scientist at the MIT Plasma Science and Fusion Center and co-author of a paper detailing these approaches.

      His fellow co-authors include lead author Woonghee “Harry” Han, a physics PhD candidate; senior author Iddo Drori, a visiting professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL), faculty associate professor at Boston University, and adjunct at Columbia University; as well as others from the MIT Plasma Science and Fusion Center, the MIT Department of Civil and Environmental Engineering, and the Swiss Federal Institute of Technology at Lausanne in Switzerland. The research appears today in Nature Scientific Reports.

      Heating things up

      For more than 70 years, scientists have sought to use controlled thermonuclear fusion reactions to develop an energy source. To reach the conditions necessary for a fusion reaction, fuel must be heated to temperatures above 100 million degrees Celsius. (The core of the sun is about 15 million degrees Celsius.)

      A common method for containing this super-hot fuel, called plasma, is to use a tokamak. These devices utilize extremely powerful magnetic fields to hold the plasma in place and control the interaction between the exhaust heat from the plasma and the reactor walls.

      However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor.

      “Knowing what the blobs are doing strongly constrains the engineering performance that your tokamak power plant needs at the edge,” adds Golfinopoulos.

      Researchers use a unique imaging technique to capture video of the plasma’s turbulent edge during experiments. An experimental campaign may last months; a typical day will produce about 30 seconds of data, corresponding to roughly 60 million video frames, with thousands of blobs appearing each second. This makes it impossible to track all blobs manually, so researchers rely on average sampling techniques that only provide broad characteristics of blob size, speed, and frequency.

      “On the other hand, machine learning provides a solution to this by blob-by-blob tracking for every frame, not just average quantities. This gives us much more knowledge about what is happening at the boundary of the plasma,” Han says.

      He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.

      Simulating blobs

      To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.

      “Sometimes they change direction or speed, sometimes multiple blobs merge, or they split apart. These kinds of events were not considered before with traditional approaches, but we could freely simulate those behaviors in the synthetic data,” Han says.

      Creating synthetic data also allowed them to label each blob, which made the training process more effective, Drori adds.

      Using these synthetic data, they trained the models to draw boundaries around blobs, teaching them to closely mimic what a human scientist would draw.

      Then they tested the models using real video data from experiments. First, they measured how closely the boundaries the models drew matched up with actual blob contours.

      But they also wanted to see if the models predicted objects that humans would identify. They asked three human experts to pinpoint the centers of blobs in video frames and checked to see if the models predicted blobs in those same locations.

      The models were able to draw accurate blob boundaries, overlapping with brightness contours which are considered ground-truth, about 80 percent of the time. Their evaluations were similar to those of human experts, and successfully predicted the theory-defined regime of the blob, which agrees with the results from a traditional method.

      Now that they have shown the success of using synthetic data and computer vision models for tracking blobs, the researchers plan to apply these techniques to other problems in fusion research, such as estimating particle transport at the boundary of a plasma, Han says.

      They also made the dataset and models publicly available, and look forward to seeing how other research groups apply these tools to study the dynamics of blobs, says Drori.

      “Prior to this, there was a barrier to entry that mostly the only people working on this problem were plasma physicists, who had the datasets and were using their methods. There is a huge machine-learning and computer-vision community. One goal of this work is to encourage participation in fusion research from the broader machine-learning community toward the broader goal of helping solve the critical problem of climate change,” he adds.

      This research is supported, in part, by the U.S. Department of Energy and the Swiss National Science Foundation. More

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      in Environment

      Coordinating climate and air-quality policies to improve public health

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      As America’s largest investment to fight climate change, the Inflation Reduction Act positions the country to reduce its greenhouse gas emissions by an estimated 40 percent below 2005 levels by 2030. But as it edges the United States closer to achieving its international climate commitment, the legislation is also expected to yield significant — and more immediate — improvements in the nation’s health. If successful in accelerating the transition from fossil fuels to clean energy alternatives, the IRA will sharply reduce atmospheric concentrations of fine particulates known to exacerbate respiratory and cardiovascular disease and cause premature deaths, along with other air pollutants that degrade human health. One recent study shows that eliminating air pollution from fossil fuels in the contiguous United States would prevent more than 50,000 premature deaths and avoid more than $600 billion in health costs each year.

      While national climate policies such as those advanced by the IRA can simultaneously help mitigate climate change and improve air quality, their results may vary widely when it comes to improving public health. That’s because the potential health benefits associated with air quality improvements are much greater in some regions and economic sectors than in others. Those benefits can be maximized, however, through a prudent combination of climate and air-quality policies.

      Several past studies have evaluated the likely health impacts of various policy combinations, but their usefulness has been limited due to a reliance on a small set of standard policy scenarios. More versatile tools are needed to model a wide range of climate and air-quality policy combinations and assess their collective effects on air quality and human health. Now researchers at the MIT Joint Program on the Science and Policy of Global Change and MIT Institute for Data, Systems and Society (IDSS) have developed a publicly available, flexible scenario tool that does just that.

      In a study published in the journal Geoscientific Model Development, the MIT team introduces its Tool for Air Pollution Scenarios (TAPS), which can be used to estimate the likely air-quality and health outcomes of a wide range of climate and air-quality policies at the regional, sectoral, and fuel-based level. 

      “This tool can help integrate the siloed sustainability issues of air pollution and climate action,” says the study’s lead author William Atkinson, who recently served as a Biogen Graduate Fellow and research assistant at the IDSS Technology and Policy Program’s (TPP) Research to Policy Engagement Initiative. “Climate action does not guarantee a clean air future, and vice versa — but the issues have similar sources that imply shared solutions if done right.”

      The study’s initial application of TAPS shows that with current air-quality policies and near-term Paris Agreement climate pledges alone, short-term pollution reductions give way to long-term increases — given the expected growth of emissions-intensive industrial and agricultural processes in developing regions. More ambitious climate and air-quality policies could be complementary, each reducing different pollutants substantially to give tremendous near- and long-term health benefits worldwide.

      “The significance of this work is that we can more confidently identify the long-term emission reduction strategies that also support air quality improvements,” says MIT Joint Program Deputy Director C. Adam Schlosser, a co-author of the study. “This is a win-win for setting climate targets that are also healthy targets.”

      TAPS projects air quality and health outcomes based on three integrated components: a recent global inventory of detailed emissions resulting from human activities (e.g., fossil fuel combustion, land-use change, industrial processes); multiple scenarios of emissions-generating human activities between now and the year 2100, produced by the MIT Economic Projection and Policy Analysis model; and emissions intensity (emissions per unit of activity) scenarios based on recent data from the Greenhouse Gas and Air Pollution Interactions and Synergies model.

      “We see the climate crisis as a health crisis, and believe that evidence-based approaches are key to making the most of this historic investment in the future, particularly for vulnerable communities,” says Johanna Jobin, global head of corporate reputation and responsibility at Biogen. “The scientific community has spoken with unanimity and alarm that not all climate-related actions deliver equal health benefits. We’re proud of our collaboration with the MIT Joint Program to develop this tool that can be used to bridge research-to-policy gaps, support policy decisions to promote health among vulnerable communities, and train the next generation of scientists and leaders for far-reaching impact.”

      The tool can inform decision-makers about a wide range of climate and air-quality policies. Policy scenarios can be applied to specific regions, sectors, or fuels to investigate policy combinations at a more granular level, or to target short-term actions with high-impact benefits.

      TAPS could be further developed to account for additional emissions sources and trends.

      “Our new tool could be used to examine a large range of both climate and air quality scenarios. As the framework is expanded, we can add detail for specific regions, as well as additional pollutants such as air toxics,” says study supervising co-author Noelle Selin, professor at IDSS and the MIT Department of Earth, Atmospheric and Planetary Sciences, and director of TPP.    

      This research was supported by the U.S. Environmental Protection Agency and its Science to Achieve Results (STAR) program; Biogen; TPP’s Leading Technology and Policy Initiative; and TPP’s Research to Policy Engagement Initiative. More

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      in Energy

      Finding community in high-energy-density physics

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      Skylar Dannhoff knew one thing: She did not want to be working alone.

      As an undergraduate at Case Western Reserve University, she had committed to a senior project that often felt like solitary lab work, a feeling heightened by the pandemic. Though it was an enriching experience, she was determined to find a graduate school environment that would foster community, one “with lots of people, lots of collaboration; where it’s impossible to work until 3 a.m. without anyone noticing.” A unique group at the Plasma Science and Fusion Center (PSFC) looked promising: the High-Energy-Density Physics (HEDP) division, a lead partner in the National Nuclear Security Administration’s Center for Excellence at MIT.

      “It was a shot in the dark, just more of a whim than anything,” she says of her request to join HEDP on her application to MIT’s Department of Physics. “And then, somehow, they reached out to me. I told them I’m willing to learn about plasma. I didn’t know anything about it.”

      What she did know was that the HEDP group collaborates with other U.S. laboratories on an approach to creating fusion energy known as inertial confinement fusion (ICF). One version of the technique, known as direct-drive ICF, aims multiple laser beams symmetrically onto a spherical capsule filled with nuclear fuel. The other, indirect-drive ICF, instead aims multiple lasers beams into a gold cylindrical cavity called a hohlraum, within which the spherical fuel capsule is positioned. The laser beams are configured to hit the inner hohlraum wall, generating a “bath” of X-rays, which in turn compress the fuel capsule.

      Imploding the capsule generates intense fusion energy within a tiny fraction of a second (an order of tens of picoseconds). In August 2021, the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) used this method to produce an historic fusion yield of 1.3 megajoules, putting researchers within reach of “ignition,” the point where the self-sustained fusion burn spreads into the surrounding fuel, leading to a high fusion-energy gain.  

      Joining the group just a month before this long-sought success, Dannhoff was impressed more with the response of her new teammates and the ICF community than with the scientific milestone. “I got a better appreciation for people who had spent their entire careers working on this project, just chugging along doing their best, ignoring the naysayers. I was excited for the people.”

      Dannhoff is now working toward extending the success of NIF and other ICF experiments, like the OMEGA laser at the University of Rochester’s Laboratory for Laser Energetics. Under the supervision of Senior Research Scientist Chikang Li, she is studying what happens to the flow of plasma within the hohlraum cavity during indirect ICF experiments, particularly for hohlraums with inner-wall aerogel foam linings. Experiments, over the last decade, have shown just how excruciatingly precise the symmetry in ICF targets must be. The more symmetric the X-ray drive, the more effective the implosion, and it is possible that these foam linings will improve the X-ray symmetry and drive efficiency.

      Dannhoff is specifically interested in studying the behavior of silicon and tantalum-based foam liners. She is as concerned with the challenges of the people at General Atomics (GA) and LLNL who are creating these targets as she is with the scientific outcome.

      “I just had a meeting with GA yesterday,” she notes. “And it’s a really tricky process. It’s kind of pushing the boundaries of what is doable at the moment. I got a much better sense of how demanding this project is for them, how much we’re asking of them.”

      What excites Dannhoff is the teamwork she observes, both at MIT and between ICF institutions around the United States. With roughly 10 graduate students and postdocs down the hall, each with an assigned lead role in lab management, she knows she can consult an expert on almost any question. And collaborators across the country are just an email away. “Any information that people can give you, they will give you, and usually very freely,” she notes. “Everyone just wants to see this work.”

      That Dannhoff is a natural team player is also evidenced in her hobbies. A hockey goalie, she prioritizes playing with MIT’s intramural teams, “because goalies are a little hard to come by. I just play with whoever needs a goalie on that night, and it’s a lot of fun.”

      She is also a member of the radio community, a fellowship she first embraced at Case Western — a moment she describes as a turning point in her life. “I literally don’t know who I would be today if I hadn’t figured out radio is something I’m interested in,” she admits. The MIT Radio Society provided the perfect landing pad for her arrival in Cambridge, full of the kinds of supportive, interesting, knowledgeable students she had befriended as an undergraduate. She credits radio with helping her realize that she could make her greatest contributions to science by focusing on engineering.

      Danhoff gets philosophical as she marvels at the invisible waves that surround us.

      “Not just radio waves: every wave,” she asserts. “The voice is the everywhere. Music, signal, space phenomena: it’s always around. And all we have to do is make the right little device and have the right circuit elements put in the right order to unmix and mix the signals and amplify them. And bada-bing, bada-boom, we’re talking with the universe.”

      “Maybe that epitomizes physics to me,” she adds. “We’re trying to listen to the universe, and it’s talking to us. We just have to come up with the right tools and hear what it’s trying to say.” More

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      in Environment

      Small eddies play a big role in feeding ocean microbes

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      Subtropical gyres are enormous rotating ocean currents that generate sustained circulations in the Earth’s subtropical regions just to the north and south of the equator. These gyres are slow-moving whirlpools that circulate within massive basins around the world, gathering up nutrients, organisms, and sometimes trash, as the currents rotate from coast to coast.

      For years, oceanographers have puzzled over conflicting observations within subtropical gyres. At the surface, these massive currents appear to host healthy populations of phytoplankton — microbes that feed the rest of the ocean food chain and are responsible for sucking up a significant portion of the atmosphere’s carbon dioxide.

      But judging from what scientists know about the dynamics of gyres, they estimated the currents themselves wouldn’t be able to maintain enough nutrients to sustain the phytoplankton they were seeing. How, then, were the microbes able to thrive?

      Now, MIT researchers have found that phytoplankton may receive deliveries of nutrients from outside the gyres, and that the delivery vehicle is in the form of eddies — much smaller currents that swirl at the edges of a gyre. These eddies pull nutrients in from high-nutrient equatorial regions and push them into the center of a gyre, where the nutrients are then taken up by other currents and pumped to the surface to feed phytoplankton.

      Ocean eddies, the team found, appear to be an important source of nutrients in subtropical gyres. Their replenishing effect, which the researchers call a “nutrient relay,” helps maintain populations of phytoplankton, which play a central role in the ocean’s ability to sequester carbon from the atmosphere. While climate models tend to project a decline in the ocean’s ability to sequester carbon over the coming decades, this “nutrient relay” could help sustain carbon storage over the subtropical oceans.

      “There’s a lot of uncertainty about how the carbon cycle of the ocean will evolve as climate continues to change, ” says Mukund Gupta, a postdoc at Caltech who led the study as a graduate student at MIT. “As our paper shows, getting the carbon distribution right is not straightforward, and depends on understanding the role of eddies and other fine-scale motions in the ocean.”

      Gupta and his colleagues report their findings this week in the Proceedings of the National Academy of Sciences. The study’s co-authors are Jonathan Lauderdale, Oliver Jahn, Christopher Hill, Stephanie Dutkiewicz, and Michael Follows at MIT, and Richard Williams at the University of Liverpool.

      A snowy puzzle

      A cross-section of an ocean gyre resembles a stack of nesting bowls that is stratified by density: Warmer, lighter layers lie at the surface, while colder, denser waters make up deeper layers. Phytoplankton live within the ocean’s top sunlit layers, where the microbes require sunlight, warm temperatures, and nutrients to grow.

      When phytoplankton die, they sink through the ocean’s layers as “marine snow.” Some of this snow releases nutrients back into the current, where they are pumped back up to feed new microbes. The rest of the snow sinks out of the gyre, down to the deepest layers of the ocean. The deeper the snow sinks, the more difficult it is for it to be pumped back to the surface. The snow is then trapped, or sequestered, along with any unreleased carbon and nutrients.

      Oceanographers thought that the main source of nutrients in subtropical gyres came from recirculating marine snow. But as a portion of this snow inevitably sinks to the bottom, there must be another source of nutrients to explain the healthy populations of phytoplankton at the surface. Exactly what that source is “has left the oceanography community a little puzzled for some time,” Gupta says.

      Swirls at the edge

      In their new study, the team sought to simulate a subtropical gyre to see what other dynamics may be at work. They focused on the North Pacific gyre, one of the Earth’s five major gyres, which circulates over most of the North Pacific Ocean, and spans more than 20 million square kilometers. 

      The team started with the MITgcm, a general circulation model that simulates the physical circulation patterns in the atmosphere and oceans. To reproduce the North Pacific gyre’s dynamics as realistically as possible, the team used an MITgcm algorithm, previously developed at NASA and MIT, which tunes the model to match actual observations of the ocean, such as ocean currents recorded by satellites, and temperature and salinity measurements taken by ships and drifters.  

      “We use a simulation of the physical ocean that is as realistic as we can get, given the machinery of the model and the available observations,” Lauderdale says.

      Play video

      An animation of the North Pacific Ocean shows phosphate nutrient concentrations at 500 meters below the ocean surface. The swirls represent small eddies transporting phosphate from the nutrient-rich equator (lighter colors), northward toward the nutrient-depleted subtropics (darker colors). This nutrient relay mechanism helps sustain biological activity and carbon sequestration in the subtropical ocean. Credit: Oliver Jahn

      The realistic model captured finer details, at a resolution of less than 20 kilometers per pixel, compared to other models that have a more limited resolution. The team combined the simulation of the ocean’s physical behavior with the Darwin model — a simulation of microbe communities such as phytoplankton, and how they grow and evolve with ocean conditions.

      The team ran the combined simulation of the North Pacific gyre over a decade, and created animations to visualize the pattern of currents and the nutrients they carried, in and around the gyre. What emerged were small eddies that ran along the edges of the enormous gyre and appeared to be rich in nutrients.

      “We were picking up on little eddy motions, basically like weather systems in the ocean,” Lauderdale says. “These eddies were carrying packets of high-nutrient waters, from the equator, north into the center of the gyre and downwards along the sides of the bowls. We wondered if these eddy transfers made an important delivery mechanism.”

      Surprisingly, the nutrients first move deeper, away from the sunlight, before being returned upwards where the phytoplankton live. The team found that ocean eddies could supply up to 50 percent of the nutrients in subtropical gyres.

      “That is very significant,” Gupta says. “The vertical process that recycles nutrients from marine snow is only half the story. The other half is the replenishing effect of these eddies. As subtropical gyres contribute a significant part of the world’s oceans, we think this nutrient relay is of global importance.”

      This research was supported, in part, by the Simons Foundation and NASA. More

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      in Environment

      Divorce is more common in albatross couples with shy males, study finds

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      The wandering albatross is the poster bird for avian monogamy. The graceful glider is known to mate for life, partnering up with the same bird to breed, season after season, between long flights at sea.

      But on rare occasions, an albatross pair will “divorce” — a term ornithologists use for instances when one partner leaves the pair for another mate while the other partner remains in the flock. Divorce rates vary widely across the avian world, and the divorce rate for wandering albatrosses is relatively low.

      Nevertheless, the giant drifters can split up. Scientists at MIT and the Woods Hole Oceanographic Institution (WHOI) have found that, at least for one particular population of wandering albatross, whether a pair will divorce boils down to one important factor: personality. 

      In a study appearing today in the journal Biology Letters, the team reports that an albatross couple’s chance of divorce is highly influenced by the male partner’s “boldness.” The bolder and more aggressive the male, the more likely the pair is to stay together. The shyer the male, the higher the chance that the pair will divorce.

      The researchers say their study is the first to link personality and divorce in a wild animal species.

      “We thought that bold males, being more aggressive, would be more likely to divorce, because they would be more likely to take the risk of switching partners to improve future reproductive outcomes,” says study senior author Stephanie Jenouvrier, an associate scientist and seabird ecologist in WHOI’s FLEDGE Lab. “Instead we find the shy divorce more because they are more likely to be forced to divorce by a more competitive intruder. We expect personality may impact divorce rates in many species, but in different ways.”

      Lead author Ruijiao Sun, a graduate student in the MIT-WHOI Joint Program and MIT’s Department of Earth, Atmospheric and Planetary Sciences, says that this new evidence of a link between personality and divorce in the wandering albatross may help scientists predict the resilience of the population.

      “The wandering albatross is a vulnerable species,” Sun says. “Understanding the effect of personality on divorce is important because it can help researchers predict the consequences for population dynamics, and implement conservation efforts.”

      The study’s co-authors include Joanie Van de Walle of WHOI, Samantha Patrick of the University of Liverpool, and Christophe Barbraud, Henri Weimerskirch, and Karine Delord of CNRS- La Rochelle University in France.

      Repeat divorcées

      The new study concentrates on a population of wandering albatross that return regularly to Possession Island in the Southern Indian Ocean to breed. This population has been the focus of a long-term study dating back to the 1950s, in which researchers have been monitoring the birds each breeding season and recording the pairings and breakups of individuals through the years.

      This particular population is skewed toward more male individuals than females because the foraging grounds of female albatrosses overlap with fishing vessels, where they are more prone to being accidentally caught in fishing lines as bycatch.  

      In earlier research, Sun analyzed data from this long-term study and picked up a curious pattern: Those individuals that divorced were more likely to do so again and again.

      “Then we wanted to know, what drives divorce, and why are some individuals divorcing more often,” Jenouvrier says. “In humans, you see this repetitive divorce pattern as well, linked to personality. And the wandering albatross is one of the rare species for which we have both demographic and personality data.”

      That personality data comes from an ongoing study that began in 2008 and is led by co-author Patrick, who has been measuring the personality of individuals among the same population of wandering albatross on Possession Island. In the study of animal behavior, personality is defined as a consistent behavioral difference displayed by an individual. Biologists mainly measure personality in animals as a gradient between shy and bold, or less to more aggressive.

      In Patrick’s study, researchers have measured boldness in albatrosses by gauging a bird’s reaction to a human approaching its nest, from a distance of about 5 meters. A bird is assigned a score depending on how it reacts (a bird that does not respond scores a zero, being the most shy, while a bird that lifts its head, and even stands up, can score higher, being the most bold).

      Patrick has made multiple personality assessments of the same individuals over multiple years. Sun and Jenouvrier wondered: Could an individual’s personality have anything to do with their chance to divorce?

      “We had seen this repetitive divorce pattern, and then talked with Sam (Patrick) to see, could this be related to personality?” Sun recalls. “We know that personality predicts divorce in human beings, and it would be intuitive to make the link between personality and divorce in wild populations.”

      Shy birds

      In their new study, the team used data from both the demographic and personality studies to see whether any patterns between the two emerged. They applied a statistical model to both datasets, to test whether the personality of individuals in an albatross pair affected the fate of that pair.

      They found that for females, personality had little to do with whether the birds divorced. But in males, the pattern was clear: Those that were identified as shy were more likely to divorce, while bolder males stayed with their partner.

      “Divorce does not happen very often,” Jenouvrier says. “But we found that the shyer a bird is, the more likely they are to divorce.”

      But why? In their study, the team puts forth an explanation, which ecologists call “forced divorce.” They point out that, in this particular population of wandering albatross, males far outnumber females and therefore are more likely to compete with each other for mates. Males that are already partnered up, therefore, may be faced with a third “intruder” — a male who is competing for a place in the pair.

      “When there is a third intruder that competes, shy birds could step away and give away their mates, where bolder individuals are aggressive and will guard their partner and secure their partnership,” Sun explains. “That’s why shyer individuals may have higher divorce rates.”

      The team is planning to extend their work to examine how the personality of individuals can affect how the larger population changes and evolves. 

      “Now we’re talking about a connection between personality and divorce at the individual level,” Sun says. “But we want to understand the impact at the population level.”

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

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      in Energy

      Computing for the health of the planet

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      The health of the planet is one of the most important challenges facing humankind today. From climate change to unsafe levels of air and water pollution to coastal and agricultural land erosion, a number of serious challenges threaten human and ecosystem health.

      Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects. New computational methods can play a critical role by providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better-preserved biodiversity, and sustainable sources of energy.

      The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its climate action plan. This year the college undertook searches with several departments in the schools of Engineering and Science for shared faculty in computing for health of the planet, one of the six strategic areas of inquiry identified in an MIT-wide planning process to help focus shared hiring efforts. The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

      The searches are part of an ongoing effort by the MIT Schwarzman College of Computing to hire 50 new faculty — 25 shared with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in departments.

      Four interdisciplinary scholars were hired in these searches. They will join the MIT faculty in the coming year to engage in research and teaching that will advance physical understanding of low-carbon energy solutions, Earth-climate modeling, biodiversity monitoring and conservation, and agricultural management through high-performance computing, transformational numerical methods, and machine-learning techniques.

      “By coordinating hiring efforts with multiple departments and schools, we were able to attract a cohort of exceptional scholars in this area to MIT. Each of them is developing and using advanced computational methods and tools to help find solutions for a range of climate and environmental issues,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Ellis Professor of Electrical Engineering and Computer Science. “They will also help strengthen cross-departmental ties in computing across an important, critical area for MIT and the world.”

      “These strategic hires in the area of computing for climate and the environment are an incredible opportunity for the college to deepen its academic offerings and create new opportunity for collaboration across MIT,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “The college plays a pivotal role in MIT’s overarching effort to hire climate-focused faculty — introducing the critical role of computing to address the health of the planet through innovative research and curriculum.”

      The four new faculty members are:

      Sara Beery will join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023. Beery received her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide and works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.

      Priya Donti will join MIT as an assistant professor in the faculties of Electrical Engineering and Artificial Intelligence and Decision-Making in EECS in academic year 2023-24. Donti recently finished her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is also co-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning that is currently running through the Cornell Tech Runway Startup Postdoc Program.

      Ericmoore Jossou will join MIT as an assistant professor in a shared position between the Department of Nuclear Science and Engineering and the faculty of electrical engineering in EECS in July 2023. He is currently an assistant scientist at the Brookhaven National Laboratory, a U.S. Department of Energy-affiliated lab that conducts research in nuclear and high energy physics, energy science and technology, environmental and bioscience, nanoscience, and national security. His research at MIT will focus on understanding the processing-structure-properties correlation of materials for nuclear energy applications through advanced experiments, multiscale simulations, and data science. Jossou obtained his PhD in mechanical engineering in 2019 from the University of Saskatchewan.

      Sherrie Wang will join MIT as an assistant professor in a shared position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society in academic year 2023-24. Wang is currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California at Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for Earth observation data. Her primary application areas are improving agricultural management and forecasting climate phenomena. She obtained her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobell. More

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      in Energy

      MIT students contribute to success of historic fusion experiment

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      For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition in a laboratory, a grand challenge of the 21st century. The High-Energy-Density Physics (HEDP) group at MIT’s Plasma Science and Fusion Center has focused on an approach called inertial confinement fusion (ICF), which uses lasers to implode a pellet of fuel in a quest for ignition. This group, including nine former and current MIT students, was crucial to an historic ICF ignition experiment performed in 2021; the results were published on the anniversary of that success.

      On Aug. 8, 2021, researchers at the National Ignition Facility (NIF), Lawrence Livermore National Laboratory (LLNL), used 192 laser beams to illuminate the inside of a tiny gold cylinder encapsulating a spherical capsule filled with deuterium-tritium fuel in their quest to produce significant fusion energy. Although researchers had followed this process many times before, using different parameters, this time the ensuing implosion produced an historic fusion yield of 1.37 megaJoules, as measured by a suite of neutron diagnostics. These included the MIT-developed and analyzed Magnetic Recoil Spectrometer (MRS). This result was published in Physical Review Letters on Aug. 8, the one-year anniversary of the ground-breaking development, unequivocally indicating that the first controlled fusion experiment reached ignition.

      Governed by the Lawson criterion, a plasma ignites when the internal fusion heating power is high enough to overcome the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop that very rapidly increases the plasma temperature. In the case of ICF, ignition is a state where the fusion plasma can initiate a “fuel burn propagation” into the surrounding dense and cold fuel, enabling the possibility of high fusion-energy gain.

      “This historic result certainly demonstrates that the ignition threshold is a real concept, with well-predicted theoretical calculations, and that a fusion plasma can be ignited in a laboratory” says HEDP Division Head Johan Frenje.

      The HEDP division has contributed to the success of the ignition program at the NIF for more than a decade by providing and using a dozen diagnostics, implemented by MIT PhD students and staff, which have been critical for assessing the performance of an implosion. The hundreds of co-authors on the paper attest to the collaborative effort that went into this milestone. MIT’s contributors included the only student co-authors.

      “The students are responsible for implementing and using a diagnostic to obtain data important to the ICF program at the NIF, says Frenje. “Being responsible for running a diagnostic at the NIF has allowed them to actively participate in the scientific dialog and thus get directly exposed to cutting-edge science.”

      Students involved from the MIT Department of Physics were Neel Kabadi, Graeme Sutcliffe, Tim Johnson, Jacob Pearcy, and Ben Reichelt; students from the Department of Nuclear Science and Engineering included Brandon Lahmann, Patrick Adrian, and Justin Kunimune.

      In addition, former student Alex Zylstra PhD ’15, now a physicist at LLNL, was the experimental lead of this record implosion experiment. More

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      in Security

      These neurons have food on the brain

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      A gooey slice of pizza. A pile of crispy French fries. Ice cream dripping down a cone on a hot summer day. When you look at any of these foods, a specialized part of your visual cortex lights up, according to a new study from MIT neuroscientists.

      This newly discovered population of food-responsive neurons is located in the ventral visual stream, alongside populations that respond specifically to faces, bodies, places, and words. The unexpected finding may reflect the special significance of food in human culture, the researchers say. 

      “Food is central to human social interactions and cultural practices. It’s not just sustenance,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience and a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines. “Food is core to so many elements of our cultural identity, religious practice, and social interactions, and many other things that humans do.”

      The findings, based on an analysis of a large public database of human brain responses to a set of 10,000 images, raise many additional questions about how and why this neural population develops. In future studies, the researchers hope to explore how people’s responses to certain foods might differ depending on their likes and dislikes, or their familiarity with certain types of food.

      MIT postdoc Meenakshi Khosla is the lead author of the paper, along with MIT research scientist N. Apurva Ratan Murty. The study appears today in the journal Current Biology.

      Visual categories

      More than 20 years ago, while studying the ventral visual stream, the part of the brain that recognizes objects, Kanwisher discovered cortical regions that respond selectively to faces. Later, she and other scientists discovered other regions that respond selectively to places, bodies, or words. Most of those areas were discovered when researchers specifically set out to look for them. However, that hypothesis-driven approach can limit what you end up finding, Kanwisher says.

      “There could be other things that we might not think to look for,” she says. “And even when we find something, how do we know that that’s actually part of the basic dominant structure of that pathway, and not something we found just because we were looking for it?”

      To try to uncover the fundamental structure of the ventral visual stream, Kanwisher and Khosla decided to analyze a large, publicly available dataset of full-brain functional magnetic resonance imaging (fMRI) responses from eight human subjects as they viewed thousands of images.

      “We wanted to see when we apply a data-driven, hypothesis-free strategy, what kinds of selectivities pop up, and whether those are consistent with what had been discovered before. A second goal was to see if we could discover novel selectivities that either haven’t been hypothesized before, or that have remained hidden due to the lower spatial resolution of fMRI data,” Khosla says.

      To do that, the researchers applied a mathematical method that allows them to discover neural populations that can’t be identified from traditional fMRI data. An fMRI image is made up of many voxels — three-dimensional units that represent a cube of brain tissue. Each voxel contains hundreds of thousands of neurons, and if some of those neurons belong to smaller populations that respond to one type of visual input, their responses may be drowned out by other populations within the same voxel.

      The new analytical method, which Kanwisher’s lab has previously used on fMRI data from the auditory cortex, can tease out responses of neural populations within each voxel of fMRI data.

      Using this approach, the researchers found four populations that corresponded to previously identified clusters that respond to faces, places, bodies, and words. “That tells us that this method works, and it tells us that the things that we found before are not just obscure properties of that pathway, but major, dominant properties,” Kanwisher says.

      Intriguingly, a fifth population also emerged, and this one appeared to be selective for images of food.

      “We were first quite puzzled by this because food is not a visually homogenous category,” Khosla says. “Things like apples and corn and pasta all look so unlike each other, yet we found a single population that responds similarly to all these diverse food items.”

      The food-specific population, which the researchers call the ventral food component (VFC), appears to be spread across two clusters of neurons, located on either side of the FFA. The fact that the food-specific populations are spread out between other category-specific populations may help explain why they have not been seen before, the researchers say.

      “We think that food selectivity had been harder to characterize before because the populations that are selective for food are intermingled with other nearby populations that have distinct responses to other stimulus attributes. The low spatial resolution of fMRI prevents us from seeing this selectivity because the responses of different neural population get mixed in a voxel,” Khosla says.

      “The technique which the researchers used to identify category-sensitive cells or areas is impressive, and it recovered known category-sensitive systems, making the food category findings most impressive,” says Paul Rozin, a professor of psychology at the University of Pennsylvania, who was not involved in the study. “I can’t imagine a way for the brain to reliably identify the diversity of foods based on sensory features. That makes this all the more fascinating, and likely to clue us in about something really new.”

      Food vs non-food

      The researchers also used the data to train a computational model of the VFC, based on previous models Murty had developed for the brain’s face and place recognition areas. This allowed the researchers to run additional experiments and predict the responses of the VFC. In one experiment, they fed the model matched images of food and non-food items that looked very similar — for example, a banana and a yellow crescent moon.

      “Those matched stimuli have very similar visual properties, but the main attribute in which they differ is edible versus inedible,” Khosla says. “We could feed those arbitrary stimuli through the predictive model and see whether it would still respond more to food than non-food, without having to collect the fMRI data.”

      They could also use the computational model to analyze much larger datasets, consisting of millions of images. Those simulations helped to confirm that the VFC is highly selective for images of food.

      From their analysis of the human fMRI data, the researchers found that in some subjects, the VFC responded slightly more to processed foods such as pizza than unprocessed foods like apples. In the future they hope to explore how factors such as familiarity and like or dislike of a particular food might affect individuals’ responses to that food.

      They also hope to study when and how this region becomes specialized during early childhood, and what other parts of the brain it communicates with. Another question is whether this food-selective population will be seen in other animals such as monkeys, who do not attach the cultural significance to food that humans do.

      The research was funded by the National Institutes of Health, the National Eye Institute, and the National Science Foundation through the MIT Center for Brains, Minds, and Machines. More