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      in Energy

      Where climate meets community

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      The MIT Living Climate Futures Lab (LCFL) centers the human dimensions of climate change, bringing together expertise from across MIT to address one of the world’s biggest challenges.The LCFL has three main goals: “addressing how climate change plays out in everyday life, focusing on community-oriented partnerships, and encouraging cross-disciplinary conversations around climate change on campus,” says Chris Walley, the SHASS Dean’s Distinguished Professor of Anthropology and head of MIT’s Anthropology Section. “We think this is a crucial direction for MIT and will make a strong statement about the kind of human-centered, interdisciplinary work needed to tackle this issue.”Walley is faculty lead of LCFL, working in collaboration with a group of 19 faculty colleagues and researchers. The LCFL began to coalesce in 2022 when MIT faculty and affiliates already working with communities dealing with climate change issues organized a symposium, inviting urban farmers, place-based environmental groups, and others to MIT. Since then, the lab has consolidated the efforts of faculty and affiliates representing disciplines from across the MIT School of Humanities, Arts, and Social Sciences (SHASS) and the Institute.Amah Edoh, a cultural anthropologist and managing director of LCFL, says the lab’s collaboration with community organizations and development of experiential learning classes aims to bridge the gap that can exist between the classroom and the real world.“Sometimes we can find ourselves in a bubble where we’re only in conversation with other people from within academia or our own field of practice. There can be a disconnect between what students are learning somewhat abstractly and the ‘real world’ experience of the issues” Edoh says. “By taking up topics from the multidimensional approach that experiential learning makes possible, students learn to take complexity as a given, which can help to foster more critical thinking in them, and inform their future practice in profound ways.”Edoh points out that the effects of climate change play out in a huge array of areas: health, food security, livelihoods, housing, and governance structures, to name a few.“The Living Climate Futures Lab supports MIT researchers in developing the long-term collaborations with community partners that are essential to adequately identifying and responding to the challenges that climate change creates in everyday life,” she says.Manduhai Buyandelger, professor of anthropology and one of the participants in LCFL, developed the class 21A.S01 (Anthro-Engineering: Decarbonization at the Million-Person Scale), which has in turn sparked related classes. The goal is “to merge technological innovation with people-centered environments.” Working closely with residents of Ulaanbaatar, Mongolia, Buyandelger and collaborator Mike Short, the Class of 1941 Professor of Nuclear Science and Engineering, helped develop a molten salt heat bank as a reusable energy source.“My work with Mike Short on energy and alternative heating in Mongolia helps to cultivate a new generation of creative and socially minded engineers who prioritize people in thinking about technical solutions,” Buyandelger says, adding, “In our course, we collaborate on creating interdisciplinary methods where we fuse anthropological methods with engineering innovations so that we can expand and deepen our approach to mitigate climate change.”

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      MIT Living Climate Futures Lab LaunchVideo: MIT Anthropology

      Iselle Barrios ’25, says 21A.S01 was her first anthropology course. She traveled to Mongolia and was able to experience firsthand all the ways in which the air pollution and heating problem was much larger and more complicated than it seemed from MIT’s Cambridge, Massachusetts, campus.“It was my first exposure to anthropological and STS critiques of science and engineering, as well as international development,” says Barrios, a chemical engineering major. “It fundamentally reshaped the way I see the role of technology and engineers in the broader social context in which they operate. It really helped me learn to think about problems in a more holistic and people-centered way.”LCFL participant Alvin Harvey, a postdoc in the MIT Media Lab’s Space Enabled Research Group and a citizen of the Navajo Nation, works to incorporate traditional knowledge in engineering and science to “support global stewardship of earth and space ecologies.””I envision the Living Climate Futures Lab as a collaborative space that can be an igniter and sustainer of relationships, especially between MIT and those whose have generational and cultural ties to land and space that is being impacted by climate change,” Harvey says. “I think everyone in our lab understands that protecting our climate future is a collective journey.”Kate Brown, the Thomas M. Siebel Distinguished Professor in History of Science, is also a participant in LCFL. Her current interest is urban food sovereignty movements, in which working-class city dwellers used waste to create “the most productive agriculture in recorded human history,” Brown says. While pursuing that work, Brown has developed relationships and worked with urban farmers in Mansfield, Ohio, as well as in Washington and Amsterdam.Brown and Susan Solomon, the Lee and Geraldine Martin Professor of Environmental Studies and Chemistry, teach a class called STS.055 (Living Dangerously: Environmental Programs from 1900 to Today) that presents the environmental problems and solutions of the 20th century, and how some “solutions” created more problems over time. Brown also plans to teach a class on the history of global food production once she gets access to a small plot of land on campus for a lab site.“The Living Climate Futures Lab gives us the structure and flexibility to work with communities that are struggling to find solutions to the problems being created by the climate crisis,” says Brown.Earlier this year, the MIT Human Insight Collaborative (MITHIC) selected the Living Climate Futures Lab as its inaugural Faculty-Driven Initiative (FDI), which comes with a $500,000 seed grant.MIT Provost Anantha Chandrakasan, co-chair of MITHIC, says the LCFL exemplifies how we can confront the climate crisis by working in true partnership with the communities most affected.“By combining scientific insight with cultural understanding and lived experience, this initiative brings a deeper dimension to MIT’s climate efforts — one grounded in collaboration, empathy, and real-world impact,” says Chandrakasan.Agustín Rayo, the Kenan Sahin Dean of SHASS and co-chair of MITHIC, says the LCFL is precisely the type of interdisciplinary collaboration the FDI program was designed to support.”By bringing together expertise from across MIT, I am confident the Living Climate Futures Lab will make significant contributions in the Institute’s effort to address the climate crisis,” says Rayo.Walley said the seed grant will support a second symposium in 2026 to be co-designed with community groups, a suite of experiential learning classes, workshops, a speaker series, and other programming. Throughout this development phase, the lab will solicit donor support to build it into an ongoing MIT initiative and a leader in the response to climate change. More

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      in Environment

      3 Questions: How AI is helping us monitor and support vulnerable ecosystems

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      A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay are developing computer vision algorithms that carefully monitor animal populations. A member of the lab of MIT Department of Electrical Engineering and Computer Science assistant professor and CSAIL principal investigator Sara Beery, Kay is currently working on tracking salmon in the Pacific Northwest, where they provide crucial nutrients to predators like birds and bears, while managing the population of prey, like bugs.With all that wildlife data, though, researchers have lots of information to sort through and many AI models to choose from to analyze it all. Kay and his colleagues at CSAIL and the University of Massachusetts Amherst are developing AI methods that make this data-crunching process much more efficient, including a new approach called “consensus-driven active model selection” (or “CODA”) that helps conservationists choose which AI model to use. Their work was named a Highlight Paper at the International Conference on Computer Vision (ICCV) in October.That research was supported, in part, by the National Science Foundation, Natural Sciences and Engineering Research Council of Canada, and Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Here, Kay discusses this project, among other conservation efforts.Q: In your paper, you pose the question of which AI models will perform the best on a particular dataset. With as many as 1.9 million pre-trained models available in the HuggingFace Models repository alone, how does CODA help us address that challenge?A: Until recently, using AI for data analysis has typically meant training your own model. This requires significant effort to collect and annotate a representative training dataset, as well as iteratively train and validate models. You also need a certain technical skill set to run and modify AI training code. The way people interact with AI is changing, though — in particular, there are now millions of publicly available pre-trained models that can perform a variety of predictive tasks very well. This potentially enables people to use AI to analyze their data without developing their own model, simply by downloading an existing model with the capabilities they need. But this poses a new challenge: Which model, of the millions available, should they use to analyze their data? Typically, answering this model selection question also requires you to spend a lot of time collecting and annotating a large dataset, albeit for testing models rather than training them. This is especially true for real applications where user needs are specific, data distributions are imbalanced and constantly changing, and model performance may be inconsistent across samples. Our goal with CODA was to substantially reduce this effort. We do this by making the data annotation process “active.” Instead of requiring users to bulk-annotate a large test dataset all at once, in active model selection we make the process interactive, guiding users to annotate the most informative data points in their raw data. This is remarkably effective, often requiring users to annotate as few as 25 examples to identify the best model from their set of candidates. We’re very excited about CODA offering a new perspective on how to best utilize human effort in the development and deployment of machine-learning (ML) systems. As AI models become more commonplace, our work emphasizes the value of focusing effort on robust evaluation pipelines, rather than solely on training.Q: You applied the CODA method to classifying wildlife in images. Why did it perform so well, and what role can systems like this have in monitoring ecosystems in the future?A: One key insight was that when considering a collection of candidate AI models, the consensus of all of their predictions is more informative than any individual model’s predictions. This can be seen as a sort of “wisdom of the crowd:” On average, pooling the votes of all models gives you a decent prior over what the labels of individual data points in your raw dataset should be. Our approach with CODA is based on estimating a “confusion matrix” for each AI model — given the true label for some data point is class X, what is the probability that an individual model predicts class X, Y, or Z? This creates informative dependencies between all of the candidate models, the categories you want to label, and the unlabeled points in your dataset.Consider an example application where you are a wildlife ecologist who has just collected a dataset containing potentially hundreds of thousands of images from cameras deployed in the wild. You want to know what species are in these images, a time-consuming task that computer vision classifiers can help automate. You are trying to decide which species classification model to run on your data. If you have labeled 50 images of tigers so far, and some model has performed well on those 50 images, you can be pretty confident it will perform well on the remainder of the (currently unlabeled) images of tigers in your raw dataset as well. You also know that when that model predicts some image contains a tiger, it is likely to be correct, and therefore that any model that predicts a different label for that image is more likely to be wrong. You can use all these interdependencies to construct probabilistic estimates of each model’s confusion matrix, as well as a probability distribution over which model has the highest accuracy on the overall dataset. These design choices allow us to make more informed choices over which data points to label and ultimately are the reason why CODA performs model selection much more efficiently than past work.There are also a lot of exciting possibilities for building on top of our work. We think there may be even better ways of constructing informative priors for model selection based on domain expertise — for instance, if it is already known that one model performs exceptionally well on some subset of classes or poorly on others. There are also opportunities to extend the framework to support more complex machine-learning tasks and more sophisticated probabilistic models of performance. We hope our work can provide inspiration and a starting point for other researchers to keep pushing the state of the art.Q: You work in the Beerylab, led by Sara Beery, where researchers are combining the pattern-recognition capabilities of machine-learning algorithms with computer vision technology to monitor wildlife. What are some other ways your team is tracking and analyzing the natural world, beyond CODA?A: The lab is a really exciting place to work, and new projects are emerging all the time. We have ongoing projects monitoring coral reefs with drones, re-identifying individual elephants over time, and fusing multi-modal Earth observation data from satellites and in-situ cameras, just to name a few. Broadly, we look at emerging technologies for biodiversity monitoring and try to understand where the data analysis bottlenecks are, and develop new computer vision and machine-learning approaches that address those problems in a widely applicable way. It’s an exciting way of approaching problems that sort of targets the “meta-questions” underlying particular data challenges we face. The computer vision algorithms I’ve worked on that count migrating salmon in underwater sonar video are examples of that work. We often deal with shifting data distributions, even as we try to construct the most diverse training datasets we can. We always encounter something new when we deploy a new camera, and this tends to degrade the performance of computer vision algorithms. This is one instance of a general problem in machine learning called domain adaptation, but when we tried to apply existing domain adaptation algorithms to our fisheries data we realized there were serious limitations in how existing algorithms were trained and evaluated. We were able to develop a new domain adaptation framework, published earlier this year in Transactions on Machine Learning Research, that addressed these limitations and led to advancements in fish counting, and even self-driving and spacecraft analysis.One line of work that I’m particularly excited about is understanding how to better develop and analyze the performance of predictive ML algorithms in the context of what they are actually used for. Usually, the outputs from some computer vision algorithm — say, bounding boxes around animals in images — are not actually the thing that people care about, but rather a means to an end to answer a larger problem — say, what species live here, and how is that changing over time? We have been working on methods to analyze predictive performance in this context and reconsider the ways that we input human expertise into ML systems with this in mind. CODA was one example of this, where we showed that we could actually consider the ML models themselves as fixed and build a statistical framework to understand their performance very efficiently. We have been working recently on similar integrated analyses combining ML predictions with multi-stage prediction pipelines, as well as ecological statistical models. The natural world is changing at unprecedented rates and scales, and being able to quickly move from scientific hypotheses or management questions to data-driven answers is more important than ever for protecting ecosystems and the communities that depend on them. Advancements in AI can play an important role, but we need to think critically about the ways that we design, train, and evaluate algorithms in the context of these very real challenges. More

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      in Energy

      Battery-powered appliances make it easy to switch from gas to electric

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      As batteries have gotten cheaper and more powerful, they have enabled the electrification of everything from vehicles to lawn equipment, power tools, and scooters. But electrifying homes has been a slower process. That’s because switching from gas appliances often requires ripping out drywall, running new wires, and upgrading the electrical box.Now the startup Copper, founded by Sam Calisch SM ’14, PhD ’19, has developed a battery-equipped kitchen range that can plug into a standard 120-volt wall outlet. The induction range features a lithium iron phosphate battery that charges when energy is cheapest and cleanest, then delivers power when you’re ready to cook.“We’re making ‘going electric’ like an appliance swap instead of a construction project,” says Calisch. “If you have a gas stove today, there is almost certainly an outlet within reach because the stove has an oven light, clock, or electric igniters. That’s big if you’re in a single-family home, but in apartments it’s an existential factor. Rewiring a 100-unit apartment building is such an expensive proposition that basically no one’s doing it.”Copper has shipped about 1,000 of its battery-powered ranges to date, often to developers and owners of large apartment complexes. The company also has an agreement with the New York City Housing Authority for at least 10,000 units.Once installed, the ranges can contribute to a distributed, cleaner, and more resilient energy network. In fact, Copper recently piloted a program in California to offer cheap, clean power to the grid from its home batteries when it would otherwise need to fire up a gas-powered plant to meet spiking electricity demand.“After these appliances are installed, they become a grid asset,” Calisch says. “We can manage the fleet of batteries to help provide firm power and help grids deliver more clean electricity. We use that revenue, in turn, to further drive down the cost of electrification.”Finding a missionCalisch has been working on climate technologies his entire career. It all started at the clean technology incubator Otherlab that was founded by Saul Griffith SM ’01, PhD ’04.“That’s where I caught the bug for technology and product development for climate impact,” Calisch says. “But I realized I needed to up my game, so I went to grad school in [MIT Professor] Neil Gershenfeld’s lab, the Center for Bits and Atoms. I got to dabble in software engineering, mechanical engineering, electrical engineering, mathematical modeling, all with the lens of building and iterating quickly.”Calisch stayed at MIT for his PhD, where he worked on approaches in manufacturing that used fewer materials and less energy. After finishing his PhD in 2019, Calisch helped start a nonprofit called Rewiring America focused on advocating for electrification. Through that work, he collaborated with U.S. Senate offices on the Inflation Reduction Act.The cost of lithium ion batteries has decreased by about 97 percent since their commercial debut in 1991. As more products have gone electric, the manufacturing process for everything from phones to drones, robots, and electric vehicles has converged around an electric tech stack of batteries, electric motors, power electronics, and chips. The countries that master the electric tech stack will be at a distinct manufacturing advantage.Calisch started Copper to boost the supply chain for batteries while contributing to the electrification movement.“Appliances can help deploy batteries, and batteries help deploy appliances,” Calisch says. “Appliances can also drive down the installed cost of batteries.”The company is starting with the kitchen range because its peak power draw is among the highest in the home. Flattening that peak brings big benefits. Ranges are also meaningful: It’s where people gather around and cook each night. People take pride in their kitchen ranges more than, say, a water heater.Copper’s 30-inch induction range heats up more quickly and reaches more precise temperatures than its gas counterpart. Installing it is as easy as swapping a fridge or dishwasher. Thanks to its 5-kilowatt-hour battery, the range even works when the power goes out.“Batteries have become 10 times cheaper and are now both affordable and create tangible improvements in quality of life,” Calisch says. “It’s a new notion of climate impact that isn’t about turning down thermostats and suffering for the planet, it’s about adopting new technologies that are better.”Scaling impactCalisch says there’s no way for the U.S. to maintain resilient energy systems in the future without a lot of batteries. Because of power transmission and regulatory limitations, those batteries can’t all be located out on the grid.“We see an analog to the internet,” Calisch says. “In order to deliver millions of times more information across the internet, we didn’t add millions of times more wires. We added local storage and caching across the network. That’s what increased throughput. We’re doing the same thing for the electric grid.”This summer, Copper raised $28 million to scale its production to meet growing demand for its battery equipped appliances. Copper is also working to license its technology to other appliance manufacturers to help speed the electric transition.“These electric technologies have the potential to improve people’s lives and, as a byproduct, take us off of fossil fuels,” Calisch says. “We’re in the business of identifying points of friction for that transition. We are not an appliance company; we’re an energy company.”Looking back, Calisch credits MIT with equipping him with the knowledge needed to run a technical business.“My time at MIT gave me hands-on experience with a variety of engineering systems,” Calisch. “I can talk to our embedded engineering team or electrical engineering team or mechanical engineering team and understand what they’re saying. That’s been enormously useful for running a company.”He adds: “I also developed an expansive view of infrastructure at MIT, which has been instrumental in launching Copper and thinking about the electrical grid not just as wires on the street, but all of the loads in our buildings. It’s about making homes not just consumers of electricity, but participants in this broader network.” More

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      in Energy

      The brain power behind sustainable AI

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      How can you use science to build a better gingerbread house?That was something Miranda Schwacke spent a lot of time thinking about. The MIT graduate student in the Department of Materials Science and Engineering (DMSE) is part of Kitchen Matters, a group of grad students who use food and kitchen tools to explain scientific concepts through short videos and outreach events. Past topics included why chocolate “seizes,” or becomes difficult to work with when melting (spoiler: water gets in), and how to make isomalt, the sugar glass that stunt performers jump through in action movies.Two years ago, when the group was making a video on how to build a structurally sound gingerbread house, Schwacke scoured cookbooks for a variable that would produce the most dramatic difference in the cookies.“I was reading about what determines the texture of cookies, and then tried several recipes in my kitchen until I got two gingerbread recipes that I was happy with,” Schwacke says.She focused on butter, which contains water that turns to steam at high baking temperatures, creating air pockets in cookies. Schwacke predicted that decreasing the amount of butter would yield denser gingerbread, strong enough to hold together as a house.“This hypothesis is an example of how changing the structure can influence the properties and performance of material,” Schwacke said in the eight-minute video.That same curiosity about materials properties and performance drives her research on the high energy cost of computing, especially for artificial intelligence. Schwacke develops new materials and devices for neuromorphic computing, which mimics the brain by processing and storing information in the same place. She studies electrochemical ionic synapses — tiny devices that can be “tuned” to adjust conductivity, much like neurons strengthening or weakening connections in the brain.“If you look at AI in particular — to train these really large models — that consumes a lot of energy. And if you compare that to the amount of energy that we consume as humans when we’re learning things, the brain consumes a lot less energy,” Schwacke says. “That’s what led to this idea to find more brain-inspired, energy-efficient ways of doing AI.”Her advisor, Bilge Yildiz, underscores the point: One reason the brain is so efficient is that data doesn’t need to be moved back and forth.“In the brain, the connections between our neurons, called synapses, are where we process information. Signal transmission is there. It is processed, programmed, and also stored in the same place,” says Yildiz, the Breene M. Kerr (1951) Professor in the Department of Nuclear Science and Engineering and DMSE. Schwacke’s devices aim to replicate that efficiency.Scientific rootsThe daughter of a marine biologist mom and an electrical engineer dad, Schwacke was immersed in science from a young age. Science was “always a part of how I understood the world.”“I was obsessed with dinosaurs. I wanted to be a paleontologist when I grew up,” she says. But her interests broadened. At her middle school in Charleston, South Carolina, she joined a FIRST Lego League robotics competition, building robots to complete tasks like pushing or pulling objects. “My parents, my dad especially, got very involved in the school team and helping us design and build our little robot for the competition.”Her mother, meanwhile, studied how dolphin populations are affected by pollution for the National Oceanic and Atmospheric Administration. That had a lasting impact.“That was an example of how science can be used to understand the world, and also to figure out how we can improve the world,” Schwacke says. “And that’s what I’ve always wanted to do with science.”Her interest in materials science came later, in her high school magnet program. There, she was introduced to the interdisciplinary subject, a blend of physics, chemistry, and engineering that studies the structure and properties of materials and uses that knowledge to design new ones.“I always liked that it goes from this very basic science, where we’re studying how atoms are ordering, all the way up to these solid materials that we interact with in our everyday lives — and how that gives them their properties that we can see and play with,” Schwacke says.As a senior, she participated in a research program with a thesis project on dye-sensitized solar cells, a low-cost, lightweight solar technology that uses dye molecules to absorb light and generate electricity.“What drove me was really understanding, this is how we go from light to energy that we can use — and also seeing how this could help us with having more renewable energy sources,” Schwacke says.After high school, she headed across the country to Caltech. “I wanted to try a totally new place,” she says, where she studied materials science, including nanostructured materials thousands of times thinner than a human hair. She focused on materials properties and microstructure — the tiny internal structure that governs how materials behave — which led her to electrochemical systems like batteries and fuel cells.AI energy challengeAt MIT, she continued exploring energy technologies. She met Yildiz during a Zoom meeting in her first year of graduate school, in fall 2020, when the campus was still operating under strict Covid-19 protocols. Yildiz’s lab studies how charged atoms, or ions, move through materials in technologies like fuel cells, batteries, and electrolyzers.The lab’s research into brain-inspired computing fired Schwacke’s imagination, but she was equally drawn to Yildiz’s way of talking about science.“It wasn’t based on jargon and emphasized a very basic understanding of what was going on — that ions are going here, and electrons are going here — to understand fundamentally what’s happening in the system,” Schwacke says.That mindset shaped her approach to research. Her early projects focused on the properties these devices need to work well — fast operation, low energy use, and compatibility with semiconductor technology — and on using magnesium ions instead of hydrogen, which can escape into the environment and make devices unstable.Her current project, the focus of her PhD thesis, centers on understanding how the insertion of magnesium ions into tungsten oxide, a metal oxide whose electrical properties can be precisely tuned, changes its electrical resistance. In these devices, tungsten oxide serves as a channel layer, where resistance controls signal strength, much like synapses regulate signals in the brain.“I am trying to understand exactly how these devices change the channel conductance,” Schwacke says.Schwacke’s research was recognized with a MathWorks Fellowship from the School of Engineering in 2023 and 2024. The fellowship supports graduate students who leverage tools like MATLAB or Simulink in their work; Schwacke applied MATLAB for critical data analysis and visualization.Yildiz describes Schwacke’s research as a novel step toward solving one of AI’s biggest challenges.“This is electrochemistry for brain-inspired computing,” Yildiz says. “It’s a new context for electrochemistry, but also with an energy implication, because the energy consumption of computing is unsustainably increasing. We have to find new ways of doing computing with much lower energy, and this is one way that can help us move in that direction.”Like any pioneering work, it comes with challenges, especially in bridging the concepts between electrochemistry and semiconductor physics.“Our group comes from a solid-state chemistry background, and when we started this work looking into magnesium, no one had used magnesium in these kinds of devices before,” Schwacke says. “So we were looking at the magnesium battery literature for inspiration and different materials and strategies we could use. When I started this, I wasn’t just learning the language and norms for one field — I was trying to learn it for two fields, and also translate between the two.”She also grapples with a challenge familiar to all scientists: how to make sense of messy data.“The main challenge is being able to take my data and know that I’m interpreting it in a way that’s correct, and that I understand what it actually means,” Schwacke says.She overcomes hurdles by collaborating closely with colleagues across fields, including neuroscience and electrical engineering, and sometimes by just making small changes to her experiments and watching what happens next.Community mattersSchwacke is not just active in the lab. In Kitchen Matters, she and her fellow DMSE grad students set up booths at local events like the Cambridge Science Fair and Steam It Up, an after-school program with hands-on activities for kids.“We did ‘pHun with Food’ with ‘fun’ spelled with a pH, so we had cabbage juice as a pH indicator,” Schwacke says. “We let the kids test the pH of lemon juice and vinegar and dish soap, and they had a lot of fun mixing the different liquids and seeing all the different colors.”She has also served as the social chair and treasurer for DMSE’s graduate student group, the Graduate Materials Council. As an undergraduate at Caltech, she led workshops in science and technology for Robogals, a student-run group that encourages young women to pursue careers in science, and assisted students in applying for the school’s Summer Undergraduate Research Fellowships.For Schwacke, these experiences sharpened her ability to explain science to different audiences, a skill she sees as vital whether she’s presenting at a kids’ fair or at a research conference.“I always think, where is my audience starting from, and what do I need to explain before I can get into what I’m doing so that it’ll all make sense to them?” she says.Schwacke sees the ability to communicate as central to building community, which she considers an important part of doing research. “It helps with spreading ideas. It always helps to get a new perspective on what you’re working on,” she says. “I also think it keeps us sane during our PhD.”Yildiz sees Schwacke’s community involvement as an important part of her resume. “She’s doing all these activities to motivate the broader community to do research, to be interested in science, to pursue science and technology, but that ability will help her also progress in her own research and academic endeavors.”After her PhD, Schwacke wants to take that ability to communicate with her to academia, where she’d like to inspire the next generation of scientists and engineers. Yildiz has no doubt she’ll thrive.“I think she’s a perfect fit,” Yildiz says. “She’s brilliant, but brilliance by itself is not enough. She’s persistent, resilient. You really need those on top of that.” More

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      in Environment

      Over 1,000 MIT students inspired to work toward climate solutions

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      Recently, more than 1,000 MIT students stepped into the shoes of global decision-makers by trying out En-ROADS, a simulation tool developed to test climate policies, explore solutions, and envision a cleaner and safer environmental future.MIT is committed to climate action, and this year’s new student orientation showcased that commitment. For the first time ever, incoming Leaders for Global Operations (LGO), Executive MBA, Sloan Fellow MBA, MBA, and undergraduate students all explored the capabilities of En-ROADS.“The goal is for MIT to become one of the world’s most prolific, collaborative, and interdisciplinary sources of technological, behavioral, and policy solutions for the global climate challenge over the next decade,” MIT Provost Anantha P. Chandrakasan told an audience of about 300 undergraduates from the Class of 2029. “It is something we need to do urgently, and today is your opportunity to play a role in that bold mission.”Connecting passion with science for changeIn group workshop sessions, students collaborated to create a world in which global warming stays well below 2 degrees Celsius above preindustrial levels — the goal of the 2015 Paris Agreement. Backed by the latest science, the En-ROADS simulator let them explore firsthand how policies like carbon pricing and clean energy investments affect our climate, economy, and health. Over 450 incoming MBA students even role-played as delegates at a global climate summit conference, tasked with negotiating a global agreement to address the harm caused by climate change.For first-year MBA student Allison Somuk, who played the role of President Xi Jinping of China, the workshop was not only eye-opening about climate, but also altered how she plans to approach her future work and advocacy.“Before the simulation, I didn’t have data on climate change, so I was surprised to see how close we are to catastrophic temperature increases. What surprised me most was how difficult it was to slow that trajectory. It required significant action and compromise from nearly every sector, not just a few. As someone passionate about improving maternal health care in developing nations, my view of contributing factors has broadened. I now see how maternal health may be affected by a larger system where climate policy decisions directly affect women’s health outcomes.”MIT Sloan Research Affiliate Andrew Jones, who is also executive director and co-founder of Climate Interactive and co-creator of the En-ROADS tool, presented several sessions during orientation. Looking back on the week, he found the experience personally rewarding.  “Engaging with hundreds of students, I was inspired by the powerful alignment between their passion for climate action and MIT’s increased commitment to delivering on climate goals. This is a pivotal moment for breakthroughs on our campus.”Other presenters included Jennifer Graham, MIT Sustainability Initiative senior associate director; Jason Jay, MIT Sustainability Initiative director; Krystal Noiseux, MIT Climate Pathways Project associate director; Bethany Patten, MIT Climate Policy Center executive director; and John Sterman, Jay W. Forrester Professor of Management, professor in the MIT Institute for Data, Systems, and Society, and director of the MIT System Dynamics Group.Chris Rabe, the MIT Climate Project’s Education Program director, was impressed, but not surprised, by how much students learned so quickly as they worked together to solve the problem with En-ROADS.“By integrating reflection, emotional dynamics, multi-generational perspectives, group work, and inquiry, the En-ROADS simulation provides an ideal foundation for first-year students to explore the breadth of climate and sustainability opportunities at MIT. In the process, students came to recognize the many levers and multi-solving approaches required to address the complex challenges of climate change.”Inspiring climate leadersThe En-ROADS workshops were a true team effort, made possible with the help of senior staff at MIT Sloan School of Management and the MBA program office, and members of the MIT Sloan Sustainability Initiative, Climate Pathways Project, Climate Policy Center, the Climate Project, Office of the First Year, and entire undergraduate Orientation team.“Altogether, over a thousand of the newest members of the MIT community have now had a chance to learn for themselves about the climate crisis,” says Sterman, “and what we can do to create a healthier, safer, more prosperous, and more sustainable world — and how they can get involved to bring that world into being, even as first-year undergrads and MBAs.” By the end of the workshops, the students’ spirits were buoyed. They all had successfully found ways to keep global warming to below 2 C.  When asked, “What would you love about being part of this new future you’ve created?,”  a more positive, optimistic word cloud came into view. Answers included:breathing cleaner air;giving my children a better and safer environment;my hometown would not be flooded constantly;rich marine life and protection of reefs;exciting, new clean industries;increased socioeconomic equality; andproof that we as a global community can work together to save ourselves. First-year MBA student Ruby Eisenbud sums up the sentiment many new MIT students came away with after their workshop.“Coming to Sloan, one of the questions on my mind was: How can we, as future leaders, make a positive impact related to climate change? While En-ROADS is a simulation, it felt like we experienced, in the smallest way, what it could be like to be a leader navigating the diverging interests of all stakeholders involved in mitigating the impacts of the climate crisis. While the simulation prompted us to face the difficult reality of climate change, it also reinforced my motivation to emphasize climate in my work at Sloan and beyond.” More

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      in Environment

      Book reviews technologies aiming to remove carbon from the atmosphere

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      Two leading experts in the field of carbon capture and sequestration (CCS) — Howard J. Herzog, a senior research engineer in the MIT Energy Initiative, and Niall Mac Dowell, a professor in energy systems engineering at Imperial College London — explore methods for removing carbon dioxide already in the atmosphere in their new book, “Carbon Removal.” Published in October, the book is part of the Essential Knowledge series from the MIT Press, which consists of volumes “synthesizing specialized subject matter for nonspecialists” and includes Herzog’s 2018 book, “Carbon Capture.”Burning fossil fuels, as well as other human activities, cause the release of carbon dioxide (CO2) into the atmosphere, where it acts like a blanket that warms the Earth, resulting in climate change. Much attention has focused on mitigation technologies that reduce emissions, but in their book, Herzog and Mac Dowell have turned their attention to “carbon dioxide removal” (CDR), an approach that removes carbon already present in the atmosphere.In this new volume, the authors explain how CO2 naturally moves into and out of the atmosphere and present a brief history of carbon removal as a concept for dealing with climate change. They also describe the full range of “pathways” that have been proposed for removing CO2 from the atmosphere. Those pathways include engineered systems designed for “direct air capture” (DAC), as well as various “nature-based” approaches that call for planting trees or taking steps to enhance removal by biomass or the oceans. The book offers easily accessible explanations of the fundamental science and engineering behind each approach.The authors compare the “quality” of the different pathways based on the following metrics:Accounting. For public acceptance of any carbon-removal strategy, the authors note, the developers need to get the accounting right — and that’s not always easy. “If you’re going to spend money to get CO2 out of the atmosphere, you want to get paid for doing it,” notes Herzog. It can be tricky to measure how much you have removed, because there’s a lot of CO2 going in and out of the atmosphere all the time. Also, if your approach involves, say, burning fossil fuels, you must subtract the amount of CO2 that’s emitted from the total amount you claim to have removed. Then there’s the timing of the removal. With a DAC device, the removal happens right now, and the removed CO2 can be measured. “But if I plant a tree, it’s going to remove CO2 for decades. Is that equivalent to removing it right now?” Herzog queries. How to take that factor into account hasn’t yet been resolved.Permanence. Different approaches keep the CO2 out of the atmosphere for different durations of time. How long is long enough? As the authors explain, this is one of the biggest issues, especially with nature-based solutions, where events such as wildfires or pestilence or land-use changes can release the stored CO2 back into the atmosphere. How do we deal with that?Cost. Cost is another key factor. Using a DAC device to remove CO2 costs far more than planting trees, but it yields immediate removal of a measurable amount of CO2 that can then be locked away forever. How does one monetize that trade-off?Additionality. “You’re doing this project, but would what you’re doing have been done anyway?” asks Herzog. “Is your effort additional to business as usual?” This question comes into play with many of the nature-based approaches involving trees, soils, and so on.Permitting and governance. These issues are especially important — and complicated — with approaches that involve doing things in the ocean. In addition, Herzog points out that some CCS projects could also achieve carbon removal, but they would have a hard time getting permits to build the pipelines and other needed infrastructure.The authors conclude that none of the CDR strategies now being proposed is a clear winner on all the metrics. However, they stress that carbon removal has the potential to play an important role in meeting our climate change goals — not by replacing our emissions-reduction efforts, but rather by supplementing them. However, as Herzog and Mac Dowell make clear in their book, many challenges must be addressed to move CDR from today’s speculation to deployment at scale, and the book supports the wider discussion about how to move forward. Indeed, the authors have fulfilled their stated goal: “to provide an objective analysis of the opportunities and challenges for CDR and to separate myth from reality.” More

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      in Environment

      MIT engineers solve the sticky-cell problem in bioreactors and other industries

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      To help mitigate climate change, companies are using bioreactors to grow algae and other microorganisms that are hundreds of times more efficient at absorbing CO2 than trees. Meanwhile, in the pharmaceutical industry, cell culture is used to manufacture biologic drugs and other advanced treatments, including lifesaving gene and cell therapies.Both processes are hampered by cells’ tendency to stick to surfaces, which leads to a huge amount of waste and downtime for cleaning. A similar problem slows down biofuel production, interferes with biosensors and implants, and makes the food and beverage industry less efficient.Now, MIT researchers have developed an approach for detaching cells from surfaces on demand, using electrochemically generated bubbles. In an open-access paper published in Science Advances, the researchers demonstrated their approach in a lab prototype and showed it could work across a range of cells and surfaces without harming the cells.“We wanted to develop a technology that could be high-throughput and plug-and-play, and that would allow cells to attach and detach on demand to improve the workflow in these industrial processes,” says Professor Kripa Varanasi, senior author of the study. “This is a fundamental issue with cells, and we’ve solved it with a process that can scale. It lends itself to many different applications.”Joining Varanasi on the study are co-first authors Bert Vandereydt, a PhD student in mechanical engineering, and former postdoc Baptiste Blanc.Solving a sticky problem

      Credit: Joy Zheng

      The researchers began with a mission.“We’ve been working on figuring out how we can efficiently capture CO2 across different sources and convert it into valuable products for various end markets,” Varanasi says. “That’s where this photobioreactor and cell detachment comes into the picture.”Photobioreactors are used to grow carbon-absorbing algae cells by creating tightly controlled environments involving water and sunlight. They feature long, winding tubes with clear surfaces to let in the light algae need to grow. When algae stick to those surfaces, they block out the light, requiring cleaning.“You have to shut down and clean up the entire reactor as frequently as every two weeks,” Varanasi says. “It’s a huge operational challenge.”The researchers realized other industries have similar problem due to many cells’ natural adhesion, or stickiness. Each industry has its own solution for cell adhesion depending on how important it is that the cells survive. Some people scrape the surfaces clean, while others use special coatings that are toxic to cells.In the pharmaceutical and biotech industries, cell detachment is typically carried out using enzymes. However, this method poses several challenges — it can damage cell membranes, is time-consuming, and requires large amounts of consumables, resulting in millions of liters of biowaste.To create a better solution, the researchers began by studying other efforts to clear surfaces with bubbles, which mainly involved spraying bubbles onto surfaces and had been largely ineffective.“We realized we needed the bubbles to form on the surfaces where we don’t want these cells to stick, so when the bubbles detach it creates a local fluid flow that creates shear stress at the interface and removes the cells,” Varanasi explains.Electric currents generate bubbles by splitting water into hydrogen and oxygen. But previous attempts at using electricity to detach cells were hampered because the cell culture mediums contain sodium chloride, which turns into bleach when combined with an electric current. The bleach damages the cells, making it impractical for many applications.“The culprit is the anode — that’s where the sodium chloride turns to bleach,” Vandereydt explained. “We figured if we could separate that electrode from the rest of the system, we could prevent bleach from being generated.”To make a better system, the researchers built a 3-square-inch glass surface and deposited a gold electrode on top of it. The layer of gold is so thin it doesn’t block out light. To keep the other electrode separate, the researchers integrated a special membrane that only allows protons to pass through. The set up allowed the researchers to send a current through without generating bleach.To test their setup, they allowed algae cells from a concentrated solution to stick to the surfaces. When they applied a voltage, the bubbles separated the cells from the surfaces without harming them.The researchers also studied the interaction between the bubbles and cells, finding the higher the current density, the more bubbles were created and the more algae was removed. They developed a model for understanding how much current would be needed to remove algae in different settings and matched it with results from experiments involving algae as well as cells from ovarian cancer and bones.“Mammalian cells are orders of magnitude more sensitive than algae cells, but even with those cells, we were able to detach them with no impact to the viability of the cell,” Vandereydt says.Getting to scaleThe researchers say their system could represent a breakthrough in applications where bleach or other chemicals would harm cells. That includes pharmaceutical and food production.“If we can keep these systems running without fouling and other problems, then we can make them much more economical,” Varanasi says.For cell culture plates used in the pharmaceutical industry, the team envisions their system comprising an electrode that could be robotically moved from one culture plate to the next, to detach cells as they’re grown. It could also be coiled around algae harvesting systems.“This has general applicability because it doesn’t rely on any specific biological or chemical treatments, but on a physical force that is system-agnostic,” Varanasi says. “It’s also highly scalable to a lot of different processes, including particle removal.”Varanasi cautions there is much work to be done to scale up the system. But he hopes it can one day make algae and other cell harvesting more efficient.“The burning problem of our time is to somehow capture CO2 in a way that’s economically feasible,” Varanasi says. “These photobioreactors could be used for that, but we have to overcome the cell adhesion problem.”The work was supported, in part, by Eni S.p.A through the MIT Energy Initiative, the Belgian American Educational Foundation Fellowship, and the Maria Zambrano Fellowship. More

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      in Security

      Optimizing food subsidies: Applying digital platforms to maximize nutrition

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      Oct. 16 is World Food Day, a global campaign to celebrate the founding of the Food and Agriculture Organization 80 years ago, and to work toward a healthy, sustainable, food-secure future. More than 670 million people in the world are facing hunger. Millions of others are facing rising obesity rates and struggle to get healthy food for proper nutrition. World Food Day calls on not only world governments, but business, academia, the media, and even the youth to take action to promote resilient food systems and combat hunger. This year, the Abdul Latif Jameel Water and Food Systems Laboratory (J-WAFS) is spotlighting an MIT researcher who is working toward this goal by studying food and water systems in the Global South.J-WAFS seed grants provide funding to early-stage research projects that are unique to prior work. In an 11th round of seed grant funding in 2025, 10 MIT faculty members received support to carry out their cutting-edge water and food research. Ali Aouad PhD ’17, assistant professor of operations management at the MIT Sloan School of Management, was one of those grantees. “I had searched before joining MIT what kind of research centers and initiatives were available that tried to coalesce research on food systems,” Aouad says. “And so, I was very excited about J-WAFS.” Aouad gathered more information about J-WAFS at the new faculty orientation session in August 2024, where he spoke to J-WAFS staff and learned about the program’s grant opportunities for water and food research. Later that fall semester, he attended a few J-WAFS seminars on agricultural economics and water resource management. That’s when Aouad knew that his project was perfectly aligned with the J-WAFS mission of securing humankind’s water and food.Aouad’s seed project focuses on food subsidies. With a background in operations research and an interest in digital platforms, much of his work has centered on aligning supply-side operations with heterogeneous customer preferences. Past projects include ones on retail and matching systems. “I started thinking that these types of demand-driven approaches may be also very relevant to important social challenges, particularly as they relate to food security,” Aouad says. Before starting his PhD at MIT, Aouad worked on projects that looked at subsidies for smallholder farmers in low- and middle-income countries. “I think in the back of my mind, I’ve always been fascinated by trying to solve these issues,” he noted.His seed grant project, Optimal subsidy design: Application to food assistance programs, aims to leverage data on preferences and purchasing habits from local grocery stores in India to inform food assistance policy and optimize the design of subsidies. Typical data collection systems, like point-of-sales, are not as readily available in India’s local groceries, making this type of data hard to come by for low-income individuals. “Mom-and-pop stores are extremely important last-mile operators when it comes to nutrition,” he explains. For this project, the research team gave local grocers point-of-sale scanners to track purchasing habits. “We aim to develop an algorithm that converts these transactions into some sort of ‘revelation’ of the individuals’ latent preferences,” says Aouad. “As such, we can model and optimize the food assistance programs — how much variety and flexibility is offered, taking into account the expected demand uptake.” He continues, “now, of course, our ability to answer detailed design questions [across various products and prices] depends on the quality of our inference from  the data, and so this is where we need more sophisticated and robust algorithms.”Following the data collection and model development, the ultimate goal of this research is to inform policy surrounding food assistance programs through an “optimization approach.” Aouad describes the complexities of using optimization to guide policy. “Policies are often informed by domain expertise, legacy systems, or political deliberation. A lot of researchers build rigorous evidence to inform food policy, but it’s fair to say that the kind of approach that I’m proposing in this research is not something that is commonly used. I see an opportunity for bringing a new approach and methodological tradition to a problem that has been central for policy for many decades.” The overall health of consumers is the reason food assistance programs exist, yet measuring long-term nutritional impacts and shifts in purchase behavior is difficult. In past research, Aouad notes that the short-term effects of food assistance interventions can be significant. However, these effects are often short-lived. “This is a fascinating question that I don’t think we will be able to address within the space of interventions that we will be considering. However, I think it is something I would like to capture in the research, and maybe develop hypotheses for future work around how we can shift nutrition-related behaviors in the long run.”While his project develops a new methodology to calibrate food assistance programs, large-scale applications are not promised. “A lot of what drives subsidy mechanisms and food assistance programs is also, quite frankly, how easy it is and how cost-effective it is to implement these policies in the first place,” comments Aouad. Cost and infrastructure barriers are unavoidable to this kind of policy research, as well as sustaining these programs. Aouad’s effort will provide insights into customer preferences and subsidy optimization in a pilot setup, but replicating this approach on a real scale may be costly. Aouad hopes to be able to gather proxy information from customers that would both feed into the model and provide insight into a more cost-effective way to collect data for large-scale implementation.There is still much work to be done to ensure food security for all, whether it’s advances in agriculture, food-assistance programs, or ways to boost adequate nutrition. As the 2026 seed grant deadline approaches, J-WAFS will continue its mission of supporting MIT faculty as they pursue innovative projects that have practical and real impacts on water and food system challenges. More