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    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

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      in Energy

      Fighting for the health of the planet with AI

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      For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS), a shared position between the MIT Schwarzman College of Computing and EECS, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS) — was struck by the disparities in how people live.“It was very clear to me the extent to which inequity is a rampant issue around the world,” Donti says. “From a young age, I knew that I definitely wanted to address that issue.”That motivation was further stoked by a high school biology teacher, who focused his class on climate and sustainability.“We learned that climate change, this huge, important issue, would exacerbate inequity,” Donti says. “That really stuck with me and put a fire in my belly.”So, when Donti enrolled at Harvey Mudd College, she thought she would direct her energy toward the study of chemistry or materials science to create next-generation solar panels.Those plans, however, were jilted. Donti “fell in love” with computer science, and then discovered work by researchers in the United Kingdom who were arguing that artificial intelligence and machine learning would be essential to help integrate renewables into power grids.“It was the first time I’d seen those two interests brought together,” she says. “I got hooked and have been working on that topic ever since.”Pursuing a PhD at Carnegie Mellon University, Donti was able to design her degree to include computer science and public policy. In her research, she explored the need for fundamental algorithms and tools that could manage, at scale, power grids relying heavily on renewables.“I wanted to have a hand in developing those algorithms and tool kits by creating new machine learning techniques grounded in computer science,” she says. “But I wanted to make sure that the way I was doing the work was grounded both in the actual energy systems domain and working with people in that domain” to provide what was actually needed.While Donti was working on her PhD, she co-founded a nonprofit called Climate Change AI. Her objective, she says, was to help the community of people involved in climate and sustainability — “be they computer scientists, academics, practitioners, or policymakers” — to come together and access resources, connection, and education “to help them along that journey.”“In the climate space,” she says, “you need experts in particular climate change-related sectors, experts in different technical and social science tool kits, problem owners, affected users, policymakers who know the regulations — all of those — to have on-the-ground scalable impact.”When Donti came to MIT in September 2023, it was not surprising that she was drawn by its initiatives directing the application of computer science toward society’s biggest problems, especially the current threat to the health of the planet.“We’re really thinking about where technology has a much longer-horizon impact and how technology, society, and policy all have to work together,” Donti says. “Technology is not just one-and-done and monetizable in the context of a year.”Her work uses deep learning models to incorporate the physics and hard constraints of electric power systems that employ renewables for better forecasting, optimization, and control.“Machine learning is already really widely used for things like solar power forecasting, which is a prerequisite to managing and balancing power grids,” she says. “My focus is, how do you improve the algorithms for actually balancing power grids in the face of a range of time-varying renewables?”Among Donti’s breakthroughs is a promising solution for power grid operators to be able to optimize for cost, taking into account the actual physical realities of the grid, rather than relying on approximations. While the solution is not yet deployed, it appears to work 10 times faster, and far more cheaply, than previous technologies, and has attracted the attention of grid operators.Another technology she is developing works to provide data that can be used in training machine learning systems for power system optimization. In general, much data related to the systems is private, either because it is proprietary or because of security concerns. Donti and her research group are working to create synthetic data and benchmarks that, Donti says, “can help to expose some of the underlying problems” in making power systems more efficient.“The question is,” Donti says, “can we bring our datasets to a point such that they are just hard enough to drive progress?”For her efforts, Donti has been awarded the U.S. Department of Energy Computational Science Graduate Fellowship and the NSF Graduate Research Fellowship. She was recognized as part of MIT Technology Review’s 2021 list of “35 Innovators Under 35” and Vox’s 2023 “Future Perfect 50.”Next spring, Donti will co-teach a class called AI for Climate Action with Sara Beery, EECS assistant professor, whose focus is AI for biodiversity and ecosystems, and Abigail Bodner, assistant professor in the departments of EECS and Earth, Atmospheric and Planetary Sciences, whose focus is AI for climate and Earth science.“We’re all super-excited about it,” Donti says.Coming to MIT, Donti says, “I knew that there would be an ecosystem of people who really cared, not just about success metrics like publications and citation counts, but about the impact of our work on society.” More

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      in Energy

      Responding to the climate impact of generative AI

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      In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint.The energy demands of generative AI are expected to continue increasing dramatically over the next decade.For instance, an April 2025 report from the International Energy Agency predicts that the global electricity demand from data centers, which house the computing infrastructure to train and deploy AI models, will more than double by 2030, to around 945 terawatt-hours. While not all operations performed in a data center are AI-related, this total amount is slightly more than the energy consumption of Japan.Moreover, an August 2025 analysis from Goldman Sachs Research forecasts that about 60 percent of the increasing electricity demands from data centers will be met by burning fossil fuels, increasing global carbon emissions by about 220 million tons. In comparison, driving a gas-powered car for 5,000 miles produces about 1 ton of carbon dioxide.These statistics are staggering, but at the same time, scientists and engineers at MIT and around the world are studying innovations and interventions to mitigate AI’s ballooning carbon footprint, from boosting the efficiency of algorithms to rethinking the design of data centers.Considering carbon emissionsTalk of reducing generative AI’s carbon footprint is typically centered on “operational carbon” — the emissions used by the powerful processors, known as GPUs, inside a data center. It often ignores “embodied carbon,” which are emissions created by building the data center in the first place, says Vijay Gadepally, senior scientist at MIT Lincoln Laboratory, who leads research projects in the Lincoln Laboratory Supercomputing Center.Constructing and retrofitting a data center, built from tons of steel and concrete and filled with air conditioning units, computing hardware, and miles of cable, consumes a huge amount of carbon. In fact, the environmental impact of building data centers is one reason companies like Meta and Google are exploring more sustainable building materials. (Cost is another factor.)Plus, data centers are enormous buildings — the world’s largest, the China Telecomm-Inner Mongolia Information Park, engulfs roughly 10 million square feet — with about 10 to 50 times the energy density of a normal office building, Gadepally adds. “The operational side is only part of the story. Some things we are working on to reduce operational emissions may lend themselves to reducing embodied carbon, too, but we need to do more on that front in the future,” he says.Reducing operational carbon emissionsWhen it comes to reducing operational carbon emissions of AI data centers, there are many parallels with home energy-saving measures. For one, we can simply turn down the lights.“Even if you have the worst lightbulbs in your house from an efficiency standpoint, turning them off or dimming them will always use less energy than leaving them running at full blast,” Gadepally says.In the same fashion, research from the Supercomputing Center has shown that “turning down” the GPUs in a data center so they consume about three-tenths the energy has minimal impacts on the performance of AI models, while also making the hardware easier to cool.Another strategy is to use less energy-intensive computing hardware.Demanding generative AI workloads, such as training new reasoning models like GPT-5, usually need many GPUs working simultaneously. The Goldman Sachs analysis estimates that a state-of-the-art system could soon have as many as 576 connected GPUs operating at once.But engineers can sometimes achieve similar results by reducing the precision of computing hardware, perhaps by switching to less powerful processors that have been tuned to handle a specific AI workload.There are also measures that boost the efficiency of training power-hungry deep-learning models before they are deployed.Gadepally’s group found that about half the electricity used for training an AI model is spent to get the last 2 or 3 percentage points in accuracy. Stopping the training process early can save a lot of that energy.“There might be cases where 70 percent accuracy is good enough for one particular application, like a recommender system for e-commerce,” he says.Researchers can also take advantage of efficiency-boosting measures.For instance, a postdoc in the Supercomputing Center realized the group might run a thousand simulations during the training process to pick the two or three best AI models for their project.By building a tool that allowed them to avoid about 80 percent of those wasted computing cycles, they dramatically reduced the energy demands of training with no reduction in model accuracy, Gadepally says.Leveraging efficiency improvementsConstant innovation in computing hardware, such as denser arrays of transistors on semiconductor chips, is still enabling dramatic improvements in the energy efficiency of AI models.Even though energy efficiency improvements have been slowing for most chips since about 2005, the amount of computation that GPUs can do per joule of energy has been improving by 50 to 60 percent each year, says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory and a principal investigator at MIT’s Initiative on the Digital Economy.“The still-ongoing ‘Moore’s Law’ trend of getting more and more transistors on chip still matters for a lot of these AI systems, since running operations in parallel is still very valuable for improving efficiency,” says Thomspon.Even more significant, his group’s research indicates that efficiency gains from new model architectures that can solve complex problems faster, consuming less energy to achieve the same or better results, is doubling every eight or nine months.Thompson coined the term “negaflop” to describe this effect. The same way a “negawatt” represents electricity saved due to energy-saving measures, a “negaflop” is a computing operation that doesn’t need to be performed due to algorithmic improvements.These could be things like “pruning” away unnecessary components of a neural network or employing compression techniques that enable users to do more with less computation.“If you need to use a really powerful model today to complete your task, in just a few years, you might be able to use a significantly smaller model to do the same thing, which would carry much less environmental burden. Making these models more efficient is the single-most important thing you can do to reduce the environmental costs of AI,” Thompson says.Maximizing energy savingsWhile reducing the overall energy use of AI algorithms and computing hardware will cut greenhouse gas emissions, not all energy is the same, Gadepally adds.“The amount of carbon emissions in 1 kilowatt hour varies quite significantly, even just during the day, as well as over the month and year,” he says.Engineers can take advantage of these variations by leveraging the flexibility of AI workloads and data center operations to maximize emissions reductions. For instance, some generative AI workloads don’t need to be performed in their entirety at the same time.Splitting computing operations so some are performed later, when more of the electricity fed into the grid is from renewable sources like solar and wind, can go a long way toward reducing a data center’s carbon footprint, says Deepjyoti Deka, a research scientist in the MIT Energy Initiative.Deka and his team are also studying “smarter” data centers where the AI workloads of multiple companies using the same computing equipment are flexibly adjusted to improve energy efficiency.“By looking at the system as a whole, our hope is to minimize energy use as well as dependence on fossil fuels, while still maintaining reliability standards for AI companies and users,” Deka says.He and others at MITEI are building a flexibility model of a data center that considers the differing energy demands of training a deep-learning model versus deploying that model. Their hope is to uncover the best strategies for scheduling and streamlining computing operations to improve energy efficiency.The researchers are also exploring the use of long-duration energy storage units at data centers, which store excess energy for times when it is needed.With these systems in place, a data center could use stored energy that was generated by renewable sources during a high-demand period, or avoid the use of diesel backup generators if there are fluctuations in the grid.“Long-duration energy storage could be a game-changer here because we can design operations that really change the emission mix of the system to rely more on renewable energy,” Deka says.In addition, researchers at MIT and Princeton University are developing a software tool for investment planning in the power sector, called GenX, which could be used to help companies determine the ideal place to locate a data center to minimize environmental impacts and costs.Location can have a big impact on reducing a data center’s carbon footprint. For instance, Meta operates a data center in Lulea, a city on the coast of northern Sweden where cooler temperatures reduce the amount of electricity needed to cool computing hardware.Thinking farther outside the box (way farther), some governments are even exploring the construction of data centers on the moon where they could potentially be operated with nearly all renewable energy.AI-based solutionsCurrently, the expansion of renewable energy generation here on Earth isn’t keeping pace with the rapid growth of AI, which is one major roadblock to reducing its carbon footprint, says Jennifer Turliuk MBA ’25, a short-term lecturer, former Sloan Fellow, and former practice leader of climate and energy AI at the Martin Trust Center for MIT Entrepreneurship.The local, state, and federal review processes required for a new renewable energy projects can take years.Researchers at MIT and elsewhere are exploring the use of AI to speed up the process of connecting new renewable energy systems to the power grid.For instance, a generative AI model could streamline interconnection studies that determine how a new project will impact the power grid, a step that often takes years to complete.And when it comes to accelerating the development and implementation of clean energy technologies, AI could play a major role.“Machine learning is great for tackling complex situations, and the electrical grid is said to be one of the largest and most complex machines in the world,” Turliuk adds.For instance, AI could help optimize the prediction of solar and wind energy generation or identify ideal locations for new facilities.It could also be used to perform predictive maintenance and fault detection for solar panels or other green energy infrastructure, or to monitor the capacity of transmission wires to maximize efficiency.By helping researchers gather and analyze huge amounts of data, AI could also inform targeted policy interventions aimed at getting the biggest “bang for the buck” from areas such as renewable energy, Turliuk says.To help policymakers, scientists, and enterprises consider the multifaceted costs and benefits of AI systems, she and her collaborators developed the Net Climate Impact Score.The score is a framework that can be used to help determine the net climate impact of AI projects, considering emissions and other environmental costs along with potential environmental benefits in the future.At the end of the day, the most effective solutions will likely result from collaborations among companies, regulators, and researchers, with academia leading the way, Turliuk adds.“Every day counts. We are on a path where the effects of climate change won’t be fully known until it is too late to do anything about it. This is a once-in-a-lifetime opportunity to innovate and make AI systems less carbon-intense,” she says. More

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      in Environment

      Simpler models can outperform deep learning at climate prediction

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      Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models.Their analysis also reveals that a benchmarking technique commonly used to evaluate machine-learning techniques for climate predictions can be distorted by natural variations in the data, like fluctuations in weather patterns. This could lead someone to believe a deep-learning model makes more accurate predictions when that is not the case.The researchers developed a more robust way of evaluating these techniques, which shows that, while simple models are more accurate when estimating regional surface temperatures, deep-learning approaches can be the best choice for estimating local rainfall.They used these results to enhance a simulation tool known as a climate emulator, which can rapidly simulate the effect of human activities onto a future climate.The researchers see their work as a “cautionary tale” about the risk of deploying large AI models for climate science. While deep-learning models have shown incredible success in domains such as natural language, climate science contains a proven set of physical laws and approximations, and the challenge becomes how to incorporate those into AI models.“We are trying to develop models that are going to be useful and relevant for the kinds of things that decision-makers need going forward when making climate policy choices. While it might be attractive to use the latest, big-picture machine-learning model on a climate problem, what this study shows is that stepping back and really thinking about the problem fundamentals is important and useful,” says study senior author Noelle Selin, a professor in the MIT Institute for Data, Systems, and Society (IDSS) and the Department of Earth, Atmospheric and Planetary Sciences (EAPS).Selin’s co-authors are lead author Björn Lütjens, a former EAPS postdoc who is now a research scientist at IBM Research; senior author Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in EAPS and co-director of the Lorenz Center; and Duncan Watson-Parris, assistant professor at the University of California at San Diego. Selin and Ferrari are also co-principal investigators of the Bringing Computation to the Climate Challenge project, out of which this research emerged. The paper appears today in the Journal of Advances in Modeling Earth Systems.Comparing emulatorsBecause the Earth’s climate is so complex, running a state-of-the-art climate model to predict how pollution levels will impact environmental factors like temperature can take weeks on the world’s most powerful supercomputers.Scientists often create climate emulators, simpler approximations of a state-of-the art climate model, which are faster and more accessible. A policymaker could use a climate emulator to see how alternative assumptions on greenhouse gas emissions would affect future temperatures, helping them develop regulations.But an emulator isn’t very useful if it makes inaccurate predictions about the local impacts of climate change. While deep learning has become increasingly popular for emulation, few studies have explored whether these models perform better than tried-and-true approaches.The MIT researchers performed such a study. They compared a traditional technique called linear pattern scaling (LPS) with a deep-learning model using a common benchmark dataset for evaluating climate emulators.Their results showed that LPS outperformed deep-learning models on predicting nearly all parameters they tested, including temperature and precipitation.“Large AI methods are very appealing to scientists, but they rarely solve a completely new problem, so implementing an existing solution first is necessary to find out whether the complex machine-learning approach actually improves upon it,” says Lütjens.Some initial results seemed to fly in the face of the researchers’ domain knowledge. The powerful deep-learning model should have been more accurate when making predictions about precipitation, since those data don’t follow a linear pattern.They found that the high amount of natural variability in climate model runs can cause the deep learning model to perform poorly on unpredictable long-term oscillations, like El Niño/La Niña. This skews the benchmarking scores in favor of LPS, which averages out those oscillations.Constructing a new evaluationFrom there, the researchers constructed a new evaluation with more data that address natural climate variability. With this new evaluation, the deep-learning model performed slightly better than LPS for local precipitation, but LPS was still more accurate for temperature predictions.“It is important to use the modeling tool that is right for the problem, but in order to do that you also have to set up the problem the right way in the first place,” Selin says.Based on these results, the researchers incorporated LPS into a climate emulation platform to predict local temperature changes in different emission scenarios.“We are not advocating that LPS should always be the goal. It still has limitations. For instance, LPS doesn’t predict variability or extreme weather events,” Ferrari adds.Rather, they hope their results emphasize the need to develop better benchmarking techniques, which could provide a fuller picture of which climate emulation technique is best suited for a particular situation.“With an improved climate emulation benchmark, we could use more complex machine-learning methods to explore problems that are currently very hard to address, like the impacts of aerosols or estimations of extreme precipitation,” Lütjens says.Ultimately, more accurate benchmarking techniques will help ensure policymakers are making decisions based on the best available information.The researchers hope others build on their analysis, perhaps by studying additional improvements to climate emulation methods and benchmarks. Such research could explore impact-oriented metrics like drought indicators and wildfire risks, or new variables like regional wind speeds.This research is funded, in part, by Schmidt Sciences, LLC, and is part of the MIT Climate Grand Challenges team for “Bringing Computation to the Climate Challenge.” More

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      in Environment

      Eco-driving measures could significantly reduce vehicle emissions

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      Any motorist who has ever waited through multiple cycles for a traffic light to turn green knows how annoying signalized intersections can be. But sitting at intersections isn’t just a drag on drivers’ patience — unproductive vehicle idling could contribute as much as 15 percent of the carbon dioxide emissions from U.S. land transportation.A large-scale modeling study led by MIT researchers reveals that eco-driving measures, which can involve dynamically adjusting vehicle speeds to reduce stopping and excessive acceleration, could significantly reduce those CO2 emissions.Using a powerful artificial intelligence method called deep reinforcement learning, the researchers conducted an in-depth impact assessment of the factors affecting vehicle emissions in three major U.S. cities.Their analysis indicates that fully adopting eco-driving measures could cut annual city-wide intersection carbon emissions by 11 to 22 percent, without slowing traffic throughput or affecting vehicle and traffic safety.Even if only 10 percent of vehicles on the road employ eco-driving, it would result in 25 to 50 percent of the total reduction in CO2 emissions, the researchers found.In addition, dynamically optimizing speed limits at about 20 percent of intersections provides 70 percent of the total emission benefits. This indicates that eco-driving measures could be implemented gradually while still having measurable, positive impacts on mitigating climate change and improving public health.

      An animated GIF compares what 20% eco-driving adoption looks like to 100% eco-driving adoption.Image: Courtesy of the researchers

      “Vehicle-based control strategies like eco-driving can move the needle on climate change reduction. We’ve shown here that modern machine-learning tools, like deep reinforcement learning, can accelerate the kinds of analysis that support sociotechnical decision making. This is just the tip of the iceberg,” says senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of the Laboratory for Information and Decision Systems (LIDS).She is joined on the paper by lead author Vindula Jayawardana, an MIT graduate student; as well as MIT graduate students Ao Qu, Cameron Hickert, and Edgar Sanchez; MIT undergraduate Catherine Tang; Baptiste Freydt, a graduate student at ETH Zurich; and Mark Taylor and Blaine Leonard of the Utah Department of Transportation. The research appears in Transportation Research Part C: Emerging Technologies.A multi-part modeling studyTraffic control measures typically call to mind fixed infrastructure, like stop signs and traffic signals. But as vehicles become more technologically advanced, it presents an opportunity for eco-driving, which is a catch-all term for vehicle-based traffic control measures like the use of dynamic speeds to reduce energy consumption.In the near term, eco-driving could involve speed guidance in the form of vehicle dashboards or smartphone apps. In the longer term, eco-driving could involve intelligent speed commands that directly control the acceleration of semi-autonomous and fully autonomous vehicles through vehicle-to-infrastructure communication systems.“Most prior work has focused on how to implement eco-driving. We shifted the frame to consider the question of should we implement eco-driving. If we were to deploy this technology at scale, would it make a difference?” Wu says.To answer that question, the researchers embarked on a multifaceted modeling study that would take the better part of four years to complete.They began by identifying 33 factors that influence vehicle emissions, including temperature, road grade, intersection topology, age of the vehicle, traffic demand, vehicle types, driver behavior, traffic signal timing, road geometry, etc.“One of the biggest challenges was making sure we were diligent and didn’t leave out any major factors,” Wu says.Then they used data from OpenStreetMap, U.S. geological surveys, and other sources to create digital replicas of more than 6,000 signalized intersections in three cities — Atlanta, San Francisco, and Los Angeles — and simulated more than a million traffic scenarios.The researchers used deep reinforcement learning to optimize each scenario for eco-driving to achieve the maximum emissions benefits.Reinforcement learning optimizes the vehicles’ driving behavior through trial-and-error interactions with a high-fidelity traffic simulator, rewarding vehicle behaviors that are more energy-efficient while penalizing those that are not.The researchers cast the problem as a decentralized cooperative multi-agent control problem, where the vehicles cooperate to achieve overall energy efficiency, even among non-participating vehicles, and they act in a decentralized manner, avoiding the need for costly communication between vehicles.However, training vehicle behaviors that generalize across diverse intersection traffic scenarios was a major challenge. The researchers observed that some scenarios are more similar to one another than others, such as scenarios with the same number of lanes or the same number of traffic signal phases.As such, the researchers trained separate reinforcement learning models for different clusters of traffic scenarios, yielding better emission benefits overall.But even with the help of AI, analyzing citywide traffic at the network level would be so computationally intensive it could take another decade to unravel, Wu says.Instead, they broke the problem down and solved each eco-driving scenario at the individual intersection level.“We carefully constrained the impact of eco-driving control at each intersection on neighboring intersections. In this way, we dramatically simplified the problem, which enabled us to perform this analysis at scale, without introducing unknown network effects,” she says.Significant emissions benefitsWhen they analyzed the results, the researchers found that full adoption of eco-driving could result in intersection emissions reductions of between 11 and 22 percent.These benefits differ depending on the layout of a city’s streets. A denser city like San Francisco has less room to implement eco-driving between intersections, offering a possible explanation for reduced emission savings, while Atlanta could see greater benefits given its higher speed limits.Even if only 10 percent of vehicles employ eco-driving, a city could still realize 25 to 50 percent of the total emissions benefit because of car-following dynamics: Non-eco-driving vehicles would follow controlled eco-driving vehicles as they optimize speed to pass smoothly through intersections, reducing their carbon emissions as well.In some cases, eco-driving could also increase vehicle throughput by minimizing emissions. However, Wu cautions that increasing throughput could result in more drivers taking to the roads, reducing emissions benefits.And while their analysis of widely used safety metrics known as surrogate safety measures, such as time to collision, suggest that eco-driving is as safe as human driving, it could cause unexpected behavior in human drivers. More research is needed to fully understand potential safety impacts, Wu says.Their results also show that eco-driving could provide even greater benefits when combined with alternative transportation decarbonization solutions. For instance, 20 percent eco-driving adoption in San Francisco would cut emission levels by 7 percent, but when combined with the projected adoption of hybrid and electric vehicles, it would cut emissions by 17 percent.“This is a first attempt to systematically quantify network-wide environmental benefits of eco-driving. This is a great research effort that will serve as a key reference for others to build on in the assessment of eco-driving systems,” says Hesham Rakha, the Samuel L. Pritchard Professor of Engineering at Virginia Tech, who was not involved with this research.And while the researchers focus on carbon emissions, the benefits are highly correlated with improvements in fuel consumption, energy use, and air quality.“This is almost a free intervention. We already have smartphones in our cars, and we are rapidly adopting cars with more advanced automation features. For something to scale quickly in practice, it must be relatively simple to implement and shovel-ready. Eco-driving fits that bill,” Wu says.This work is funded, in part, by Amazon and the Utah Department of Transportation. More

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      in Security

      Imaging technique removes the effect of water in underwater scenes

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      The ocean is teeming with life. But unless you get up close, much of the marine world can easily remain unseen. That’s because water itself can act as an effective cloak: Light that shines through the ocean can bend, scatter, and quickly fade as it travels through the dense medium of water and reflects off the persistent haze of ocean particles. This makes it extremely challenging to capture the true color of objects in the ocean without imaging them at close range.Now a team from MIT and the Woods Hole Oceanographic Institution (WHOI) has developed an image-analysis tool that cuts through the ocean’s optical effects and generates images of underwater environments that look as if the water had been drained away, revealing an ocean scene’s true colors. The team paired the color-correcting tool with a computational model that converts images of a scene into a three-dimensional underwater “world,” that can then be explored virtually.The researchers have dubbed the new tool “SeaSplat,” in reference to both its underwater application and a method known as 3D gaussian splatting (3DGS), which takes images of a scene and stitches them together to generate a complete, three-dimensional representation that can be viewed in detail, from any perspective.“With SeaSplat, it can model explicitly what the water is doing, and as a result it can in some ways remove the water, and produces better 3D models of an underwater scene,” says MIT graduate student Daniel Yang.The researchers applied SeaSplat to images of the sea floor taken by divers and underwater vehicles, in various locations including the U.S. Virgin Islands. The method generated 3D “worlds” from the images that were truer and more vivid and varied in color, compared to previous methods.The team says SeaSplat could help marine biologists monitor the health of certain ocean communities. For instance, as an underwater robot explores and takes pictures of a coral reef, SeaSplat would simultaneously process the images and render a true-color, 3D representation, that scientists could then virtually “fly” through, at their own pace and path, to inspect the underwater scene, for instance for signs of coral bleaching.“Bleaching looks white from close up, but could appear blue and hazy from far away, and you might not be able to detect it,” says Yogesh Girdhar, an associate scientist at WHOI. “Coral bleaching, and different coral species, could be easier to detect with SeaSplat imagery, to get the true colors in the ocean.”Girdhar and Yang will present a paper detailing SeaSplat at the IEEE International Conference on Robotics and Automation (ICRA). Their study co-author is John Leonard, professor of mechanical engineering at MIT.Aquatic opticsIn the ocean, the color and clarity of objects is distorted by the effects of light traveling through water. In recent years, researchers have developed color-correcting tools that aim to reproduce the true colors in the ocean. These efforts involved adapting tools that were developed originally for environments out of water, for instance to reveal the true color of features in foggy conditions. One recent work accurately reproduces true colors in the ocean, with an algorithm named “Sea-Thru,” though this method requires a huge amount of computational power, which makes its use in producing 3D scene models challenging.In parallel, others have made advances in 3D gaussian splatting, with tools that seamlessly stitch images of a scene together, and intelligently fill in any gaps to create a whole, 3D version of the scene. These 3D worlds enable “novel view synthesis,” meaning that someone can view the generated 3D scene, not just from the perspective of the original images, but from any angle and distance.But 3DGS has only successfully been applied to environments out of water. Efforts to adapt 3D reconstruction to underwater imagery have been hampered, mainly by two optical underwater effects: backscatter and attenuation. Backscatter occurs when light reflects off of tiny particles in the ocean, creating a veil-like haze. Attenuation is the phenomenon by which light of certain wavelengths attenuates, or fades with distance. In the ocean, for instance, red objects appear to fade more than blue objects when viewed from farther away.Out of water, the color of objects appears more or less the same regardless of the angle or distance from which they are viewed. In water, however, color can quickly change and fade depending on one’s perspective. When 3DGS methods attempt to stitch underwater images into a cohesive 3D whole, they are unable to resolve objects due to aquatic backscatter and attenuation effects that distort the color of objects at different angles.“One dream of underwater robotic vision that we have is: Imagine if you could remove all the water in the ocean. What would you see?” Leonard says.A model swimIn their new work, Yang and his colleagues developed a color-correcting algorithm that accounts for the optical effects of backscatter and attenuation. The algorithm determines the degree to which every pixel in an image must have been distorted by backscatter and attenuation effects, and then essentially takes away those aquatic effects, and computes what the pixel’s true color must be.Yang then worked the color-correcting algorithm into a 3D gaussian splatting model to create SeaSplat, which can quickly analyze underwater images of a scene and generate a true-color, 3D virtual version of the same scene that can be explored in detail from any angle and distance.The team applied SeaSplat to multiple underwater scenes, including images taken in the Red Sea, in the Carribean off the coast of Curaçao, and the Pacific Ocean, near Panama. These images, which the team took from a pre-existing dataset, represent a range of ocean locations and water conditions. They also tested SeaSplat on images taken by a remote-controlled underwater robot in the U.S. Virgin Islands.From the images of each ocean scene, SeaSplat generated a true-color 3D world that the researchers were able to virtually explore, for instance zooming in and out of a scene and viewing certain features from different perspectives. Even when viewing from different angles and distances, they found objects in every scene retained their true color, rather than fading as they would if viewed through the actual ocean.“Once it generates a 3D model, a scientist can just ‘swim’ through the model as though they are scuba-diving, and look at things in high detail, with real color,” Yang says.For now, the method requires hefty computing resources in the form of a desktop computer that would be too bulky to carry aboard an underwater robot. Still, SeaSplat could work for tethered operations, where a vehicle, tied to a ship, can explore and take images that can be sent up to a ship’s computer.“This is the first approach that can very quickly build high-quality 3D models with accurate colors, underwater, and it can create them and render them fast,” Girdhar says. “That will help to quantify biodiversity, and assess the health of coral reef and other marine communities.”This work was supported, in part, by the Investment in Science Fund at WHOI, and by the U.S. National Science Foundation. More

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      in Energy

      Rooftop panels, EV chargers, and smart thermostats could chip in to boost power grid resilience

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      There’s a lot of untapped potential in our homes and vehicles that could be harnessed to reinforce local power grids and make them more resilient to unforeseen outages, a new study shows.In response to a cyber attack or natural disaster, a backup network of decentralized devices — such as residential solar panels, batteries, electric vehicles, heat pumps, and water heaters — could restore electricity or relieve stress on the grid, MIT engineers say.Such devices are “grid-edge” resources found close to the consumer rather than near central power plants, substations, or transmission lines. Grid-edge devices can independently generate, store, or tune their consumption of power. In their study, the research team shows how such devices could one day be called upon to either pump power into the grid, or rebalance it by dialing down or delaying their power use.In a paper appearing this week in the Proceedings of the National Academy of Sciences, the engineers present a blueprint for how grid-edge devices could reinforce the power grid through a “local electricity market.” Owners of grid-edge devices could subscribe to a regional market and essentially loan out their device to be part of a microgrid or a local network of on-call energy resources.In the event that the main power grid is compromised, an algorithm developed by the researchers would kick in for each local electricity market, to quickly determine which devices in the network are trustworthy. The algorithm would then identify the combination of trustworthy devices that would most effectively mitigate the power failure, by either pumping power into the grid or reducing the power they draw from it, by an amount that the algorithm would calculate and communicate to the relevant subscribers. The subscribers could then be compensated through the market, depending on their participation.The team illustrated this new framework through a number of grid attack scenarios, in which they considered failures at different levels of a power grid, from various sources such as a cyber attack or a natural disaster. Applying their algorithm, they showed that various networks of grid-edge devices were able to dissolve the various attacks.The results demonstrate that grid-edge devices such as rooftop solar panels, EV chargers, batteries, and smart thermostats (for HVAC devices or heat pumps) could be tapped to stabilize the power grid in the event of an attack.“All these small devices can do their little bit in terms of adjusting their consumption,” says study co-author Anu Annaswamy, a research scientist in MIT’s Department of Mechanical Engineering. “If we can harness our smart dishwashers, rooftop panels, and EVs, and put our combined shoulders to the wheel, we can really have a resilient grid.”The study’s MIT co-authors include lead author Vineet Nair and John Williams, along with collaborators from multiple institutions including the Indian Institute of Technology, the National Renewable Energy Laboratory, and elsewhere.Power boostThe team’s study is an extension of their broader work in adaptive control theory and designing systems to automatically adapt to changing conditions. Annaswamy, who leads the Active-Adaptive Control Laboratory at MIT, explores ways to boost the reliability of renewable energy sources such as solar power.“These renewables come with a strong temporal signature, in that we know for sure the sun will set every day, so the solar power will go away,” Annaswamy says. “How do you make up for the shortfall?”The researchers found the answer could lie in the many grid-edge devices that consumers are increasingly installing in their own homes.“There are lots of distributed energy resources that are coming up now, closer to the customer rather than near large power plants, and it’s mainly because of individual efforts to decarbonize,” Nair says. “So you have all this capability at the grid edge. Surely we should be able to put them to good use.”While considering ways to deal with drops in energy from the normal operation of renewable sources, the team also began to look into other causes of power dips, such as from cyber attacks. They wondered, in these malicious instances, whether and how the same grid-edge devices could step in to stabilize the grid following an unforeseen, targeted attack.Attack modeIn their new work, Annaswamy, Nair, and their colleagues developed a framework for incorporating grid-edge devices, and in particular, internet-of-things (IoT) devices, in a way that would support the larger grid in the event of an attack or disruption. IoT devices are physical objects that contain sensors and software that connect to the internet.For their new framework, named EUREICA (Efficient, Ultra-REsilient, IoT-Coordinated Assets), the researchers start with the assumption that one day, most grid-edge devices will also be IoT devices, enabling rooftop panels, EV chargers, and smart thermostats to wirelessly connect to a larger network of similarly independent and distributed devices. The team envisions that for a given region, such as a community of 1,000 homes, there exists a certain number of IoT devices that could potentially be enlisted in the region’s local network, or microgrid. Such a network would be managed by an operator, who would be able to communicate with operators of other nearby microgrids.If the main power grid is compromised or attacked, operators would run the researchers’ decision-making algorithm to determine trustworthy devices within the network that can pitch in to help mitigate the attack.The team tested the algorithm on a number of scenarios, such as a cyber attack in which all smart thermostats made by a certain manufacturer are hacked to raise their setpoints simultaneously to a degree that dramatically alters a region’s energy load and destabilizes the grid. The researchers also considered attacks and weather events that would shut off the transmission of energy at various levels and nodes throughout a power grid.“In our attacks we consider between 5 and 40 percent of the power being lost. We assume some nodes are attacked, and some are still available and have some IoT resources, whether a battery with energy available or an EV or HVAC device that’s controllable,” Nair explains. “So, our algorithm decides which of those houses can step in to either provide extra power generation to inject into the grid or reduce their demand to meet the shortfall.”In every scenario that they tested, the team found that the algorithm was able to successfully restabilize the grid and mitigate the attack or power failure. They acknowledge that to put in place such a network of grid-edge devices will require buy-in from customers, policymakers, and local officials, as well as innovations such as advanced power inverters that enable EVs to inject power back into the grid.“This is just the first of many steps that have to happen in quick succession for this idea of local electricity markets to be implemented and expanded upon,” Annaswamy says. “But we believe it’s a good start.”This work was supported, in part, by the U.S. Department of Energy and the MIT Energy Initiative. More

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      in Energy

      Puzzling out climate change

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      Shreyaa Raghavan’s journey into solving some of the world’s toughest challenges started with a simple love for puzzles. By high school, her knack for problem-solving naturally drew her to computer science. Through her participation in an entrepreneurship and leadership program, she built apps and twice made it to the semifinals of the program’s global competition.Her early successes made a computer science career seem like an obvious choice, but Raghavan says a significant competing interest left her torn.“Computer science sparks that puzzle-, problem-solving part of my brain,” says Raghavan ’24, an Accenture Fellow and a PhD candidate in MIT’s Institute for Data, Systems, and Society. “But while I always felt like building mobile apps was a fun little hobby, it didn’t feel like I was directly solving societal challenges.”Her perspective shifted when, as an MIT undergraduate, Raghavan participated in an Undergraduate Research Opportunity in the Photovoltaic Research Laboratory, now known as the Accelerated Materials Laboratory for Sustainability. There, she discovered how computational techniques like machine learning could optimize materials for solar panels — a direct application of her skills toward mitigating climate change.“This lab had a very diverse group of people, some from a computer science background, some from a chemistry background, some who were hardcore engineers. All of them were communicating effectively and working toward one unified goal — building better renewable energy systems,” Raghavan says. “It opened my eyes to the fact that I could use very technical tools that I enjoy building and find fulfillment in that by helping solve major climate challenges.”With her sights set on applying machine learning and optimization to energy and climate, Raghavan joined Cathy Wu’s lab when she started her PhD in 2023. The lab focuses on building more sustainable transportation systems, a field that resonated with Raghavan due to its universal impact and its outsized role in climate change — transportation accounts for roughly 30 percent of greenhouse gas emissions.“If we were to throw all of the intelligent systems we are exploring into the transportation networks, by how much could we reduce emissions?” she asks, summarizing a core question of her research.Wu, an associate professor in the Department of Civil and Environmental Engineering, stresses the value of Raghavan’s work.“Transportation is a critical element of both the economy and climate change, so potential changes to transportation must be carefully studied,” Wu says. “Shreyaa’s research into smart congestion management is important because it takes a data-driven approach to add rigor to the broader research supporting sustainability.”Raghavan’s contributions have been recognized with the Accenture Fellowship, a cornerstone of the MIT-Accenture Convergence Initiative for Industry and Technology. As an Accenture Fellow, she is exploring the potential impact of technologies for avoiding stop-and-go traffic and its emissions, using systems such as networked autonomous vehicles and digital speed limits that vary according to traffic conditions — solutions that could advance decarbonization in the transportation section at relatively low cost and in the near term.Raghavan says she appreciates the Accenture Fellowship not only for the support it provides, but also because it demonstrates industry involvement in sustainable transportation solutions.“It’s important for the field of transportation, and also energy and climate as a whole, to synergize with all of the different stakeholders,” she says. “I think it’s important for industry to be involved in this issue of incorporating smarter transportation systems to decarbonize transportation.”Raghavan has also received a fellowship supporting her research from the U.S. Department of Transportation.“I think it’s really exciting that there’s interest from the policy side with the Department of Transportation and from the industry side with Accenture,” she says.Raghavan believes that addressing climate change requires collaboration across disciplines. “I think with climate change, no one industry or field is going to solve it on its own. It’s really got to be each field stepping up and trying to make a difference,” she says. “I don’t think there’s any silver-bullet solution to this problem. It’s going to take many different solutions from different people, different angles, different disciplines.”With that in mind, Raghavan has been very active in the MIT Energy and Climate Club since joining about three years ago, which, she says, “was a really cool way to meet lots of people who were working toward the same goal, the same climate goals, the same passions, but from completely different angles.”This year, Raghavan is on the community and education team, which works to build the community at MIT that is working on climate and energy issues. As part of that work, Raghavan is launching a mentorship program for undergraduates, pairing them with graduate students who help the undergrads develop ideas about how they can work on climate using their unique expertise.“I didn’t foresee myself using my computer science skills in energy and climate,” Raghavan says, “so I really want to give other students a clear pathway, or a clear sense of how they can get involved.”Raghavan has embraced her area of study even in terms of where she likes to think.“I love working on trains, on buses, on airplanes,” she says. “It’s really fun to be in transit and working on transportation problems.”Anticipating a trip to New York to visit a cousin, she holds no dread for the long train trip.“I know I’m going to do some of my best work during those hours,” she says. “Four hours there. Four hours back.” More

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      in Environment

      Streamlining data collection for improved salmon population management

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      Sara Beery came to MIT as an assistant professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) eager to focus on ecological challenges. She has fashioned her research career around the opportunity to apply her expertise in computer vision, machine learning, and data science to tackle real-world issues in conservation and sustainability. Beery was drawn to the Institute’s commitment to “computing for the planet,” and set out to bring her methods to global-scale environmental and biodiversity monitoring.In the Pacific Northwest, salmon have a disproportionate impact on the health of their ecosystems, and their complex reproductive needs have attracted Beery’s attention. Each year, millions of salmon embark on a migration to spawn. Their journey begins in freshwater stream beds where the eggs hatch. Young salmon fry (newly hatched salmon) make their way to the ocean, where they spend several years maturing to adulthood. As adults, the salmon return to the streams where they were born in order to spawn, ensuring the continuation of their species by depositing their eggs in the gravel of the stream beds. Both male and female salmon die shortly after supplying the river habitat with the next generation of salmon. Throughout their migration, salmon support a wide range of organisms in the ecosystems they pass through. For example, salmon bring nutrients like carbon and nitrogen from the ocean upriver, enhancing their availability to those ecosystems. In addition, salmon are key to many predator-prey relationships: They serve as a food source for various predators, such as bears, wolves, and birds, while helping to control other populations, like insects, through predation. After they die from spawning, the decomposing salmon carcasses also replenish valuable nutrients to the surrounding ecosystem. The migration of salmon not only sustains their own species but plays a critical role in the overall health of the rivers and oceans they inhabit. At the same time, salmon populations play an important role both economically and culturally in the region. Commercial and recreational salmon fisheries contribute significantly to the local economy. And for many Indigenous peoples in the Pacific northwest, salmon hold notable cultural value, as they have been central to their diets, traditions, and ceremonies. Monitoring salmon migrationIncreased human activity, including overfishing and hydropower development, together with habitat loss and climate change, have had a significant impact on salmon populations in the region. As a result, effective monitoring and management of salmon fisheries is important to ensure balance among competing ecological, cultural, and human interests. Accurately counting salmon during their seasonal migration to their natal river to spawn is essential in order to track threatened populations, assess the success of recovery strategies, guide fishing season regulations, and support the management of both commercial and recreational fisheries. Precise population data help decision-makers employ the best strategies to safeguard the health of the ecosystem while accommodating human needs. Monitoring salmon migration is a labor-intensive and inefficient undertaking.Beery is currently leading a research project that aims to streamline salmon monitoring using cutting-edge computer vision methods. This project fits within Beery’s broader research interest, which focuses on the interdisciplinary space between artificial intelligence, the natural world, and sustainability. Its relevance to fisheries management made it a good fit for funding from MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Beery’s 2023 J-WAFS seed grant was the first research funding she was awarded since joining the MIT faculty.  Historically, monitoring efforts relied on humans to manually count salmon from riverbanks using eyesight. In the past few decades, underwater sonar systems have been implemented to aid in counting the salmon. These sonar systems are essentially underwater video cameras, but they differ in that they use acoustics instead of light sensors to capture the presence of a fish. Use of this method requires people to set up a tent alongside the river to count salmon based on the output of a sonar camera that is hooked up to a laptop. While this system is an improvement to the original method of monitoring salmon by eyesight, it still relies significantly on human effort and is an arduous and time-consuming process. Automating salmon monitoring is necessary for better management of salmon fisheries. “We need these technological tools,” says Beery. “We can’t keep up with the demand of monitoring and understanding and studying these really complex ecosystems that we work in without some form of automation.”In order to automate counting of migrating salmon populations in the Pacific Northwest, the project team, including Justin Kay, a PhD student in EECS, has been collecting data in the form of videos from sonar cameras at different rivers. The team annotates a subset of the data to train the computer vision system to autonomously detect and count the fish as they migrate. Kay describes the process of how the model counts each migrating fish: “The computer vision algorithm is designed to locate a fish in the frame, draw a box around it, and then track it over time. If a fish is detected on one side of the screen and leaves on the other side of the screen, then we count it as moving upstream.” On rivers where the team has created training data for the system, it has produced strong results, with only 3 to 5 percent counting error. This is well below the target that the team and partnering stakeholders set of no more than a 10 percent counting error. Testing and deployment: Balancing human effort and use of automationThe researchers’ technology is being deployed to monitor the migration of salmon on the newly restored Klamath River. Four dams on the river were recently demolished, making it the largest dam removal project in U.S. history. The dams came down after a more than 20-year-long campaign to remove them, which was led by Klamath tribes, in collaboration with scientists, environmental organizations, and commercial fishermen. After the removal of the dams, 240 miles of the river now flow freely and nearly 800 square miles of habitat are accessible to salmon. Beery notes the almost immediate regeneration of salmon populations in the Klamath River: “I think it was within eight days of the dam coming down, they started seeing salmon actually migrate upriver beyond the dam.” In a collaboration with California Trout, the team is currently processing new data to adapt and create a customized model that can then be deployed to help count the newly migrating salmon.One challenge with the system revolves around training the model to accurately count the fish in unfamiliar environments with variations such as riverbed features, water clarity, and lighting conditions. These factors can significantly alter how the fish appear on the output of a sonar camera and confuse the computer model. When deployed in new rivers where no data have been collected before, like the Klamath, the performance of the system degrades and the margin of error increases substantially to 15-20 percent. The researchers constructed an automatic adaptation algorithm within the system to overcome this challenge and create a scalable system that can be deployed to any site without human intervention. This self-initializing technology works to automatically calibrate to the new conditions and environment to accurately count the migrating fish. In testing, the automatic adaptation algorithm was able to reduce the counting error down to the 10 to 15 percent range. The improvement in counting error with the self-initializing function means that the technology is closer to being deployable to new locations without much additional human effort. Enabling real-time management with the “Fishbox”Another challenge faced by the research team was the development of an efficient data infrastructure. In order to run the computer vision system, the video produced by sonar cameras must be delivered via the cloud or by manually mailing hard drives from a river site to the lab. These methods have notable drawbacks: a cloud-based approach is limited due to lack of internet connectivity in remote river site locations, and shipping the data introduces problems of delay. Instead of relying on these methods, the team has implemented a power-efficient computer, coined the “Fishbox,” that can be used in the field to perform the processing. The Fishbox consists of a small, lightweight computer with optimized software that fishery managers can plug into their existing laptops and sonar cameras. The system is then capable of running salmon counting models directly at the sonar sites without the need for internet connectivity. This allows managers to make hour-by-hour decisions, supporting more responsive, real-time management of salmon populations.Community developmentThe team is also working to bring a community together around monitoring for salmon fisheries management in the Pacific Northwest. “It’s just pretty exciting to have stakeholders who are enthusiastic about getting access to [our technology] as we get it to work and having a tighter integration and collaboration with them,” says Beery. “I think particularly when you’re working on food and water systems, you need direct collaboration to help facilitate impact, because you’re ensuring that what you develop is actually serving the needs of the people and organizations that you are helping to support.”This past June, Beery’s lab organized a workshop in Seattle that convened nongovernmental organizations, tribes, and state and federal departments of fish and wildlife to discuss the use of automated sonar systems to monitor and manage salmon populations. Kay notes that the workshop was an “awesome opportunity to have everybody sharing different ways that they’re using sonar and thinking about how the automated methods that we’re building could fit into that workflow.” The discussion continues now via a shared Slack channel created by the team, with over 50 participants. Convening this group is a significant achievement, as many of these organizations would not otherwise have had an opportunity to come together and collaborate. Looking forwardAs the team continues to tune the computer vision system, refine their technology, and engage with diverse stakeholders — from Indigenous communities to fishery managers — the project is poised to make significant improvements to the efficiency and accuracy of salmon monitoring and management in the region. And as Beery advances the work of her MIT group, the J-WAFS seed grant is helping to keep challenges such as fisheries management in her sights.  “The fact that the J-WAFS seed grant existed here at MIT enabled us to continue to work on this project when we moved here,” comments Beery, adding “it also expanded the scope of the project and allowed us to maintain active collaboration on what I think is a really important and impactful project.” As J-WAFS marks its 10th anniversary this year, the program aims to continue supporting and encouraging MIT faculty to pursue innovative projects that aim to advance knowledge and create practical solutions with real-world impacts on global water and food system challenges.  More