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      in Energy

      New tools are available to help reduce the energy that AI models devour

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      When searching for flights on Google, you may have noticed that each flight’s carbon-emission estimate is now presented next to its cost. It’s a way to inform customers about their environmental impact, and to let them factor this information into their decision-making.

      A similar kind of transparency doesn’t yet exist for the computing industry, despite its carbon emissions exceeding those of the entire airline industry. Escalating this energy demand are artificial intelligence models. Huge, popular models like ChatGPT signal a trend of large-scale artificial intelligence, boosting forecasts that predict data centers will draw up to 21 percent of the world’s electricity supply by 2030.

      The MIT Lincoln Laboratory Supercomputing Center (LLSC) is developing techniques to help data centers reel in energy use. Their techniques range from simple but effective changes, like power-capping hardware, to adopting novel tools that can stop AI training early on. Crucially, they have found that these techniques have a minimal impact on model performance.

      In the wider picture, their work is mobilizing green-computing research and promoting a culture of transparency. “Energy-aware computing is not really a research area, because everyone’s been holding on to their data,” says Vijay Gadepally, senior staff in the LLSC who leads energy-aware research efforts. “Somebody has to start, and we’re hoping others will follow.”

      Curbing power and cooling down

      Like many data centers, the LLSC has seen a significant uptick in the number of AI jobs running on its hardware. Noticing an increase in energy usage, computer scientists at the LLSC were curious about ways to run jobs more efficiently. Green computing is a principle of the center, which is powered entirely by carbon-free energy.

      Training an AI model — the process by which it learns patterns from huge datasets — requires using graphics processing units (GPUs), which are power-hungry hardware. As one example, the GPUs that trained GPT-3 (the precursor to ChatGPT) are estimated to have consumed 1,300 megawatt-hours of electricity, roughly equal to that used by 1,450 average U.S. households per month.

      While most people seek out GPUs because of their computational power, manufacturers offer ways to limit the amount of power a GPU is allowed to draw. “We studied the effects of capping power and found that we could reduce energy consumption by about 12 percent to 15 percent, depending on the model,” Siddharth Samsi, a researcher within the LLSC, says.

      The trade-off for capping power is increasing task time — GPUs will take about 3 percent longer to complete a task, an increase Gadepally says is “barely noticeable” considering that models are often trained over days or even months. In one of their experiments in which they trained the popular BERT language model, limiting GPU power to 150 watts saw a two-hour increase in training time (from 80 to 82 hours) but saved the equivalent of a U.S. household’s week of energy.

      The team then built software that plugs this power-capping capability into the widely used scheduler system, Slurm. The software lets data center owners set limits across their system or on a job-by-job basis.

      “We can deploy this intervention today, and we’ve done so across all our systems,” Gadepally says.

      Side benefits have arisen, too. Since putting power constraints in place, the GPUs on LLSC supercomputers have been running about 30 degrees Fahrenheit cooler and at a more consistent temperature, reducing stress on the cooling system. Running the hardware cooler can potentially also increase reliability and service lifetime. They can now consider delaying the purchase of new hardware — reducing the center’s “embodied carbon,” or the emissions created through the manufacturing of equipment — until the efficiencies gained by using new hardware offset this aspect of the carbon footprint. They’re also finding ways to cut down on cooling needs by strategically scheduling jobs to run at night and during the winter months.

      “Data centers can use these easy-to-implement approaches today to increase efficiencies, without requiring modifications to code or infrastructure,” Gadepally says.

      Taking this holistic look at a data center’s operations to find opportunities to cut down can be time-intensive. To make this process easier for others, the team — in collaboration with Professor Devesh Tiwari and Baolin Li at Northeastern University — recently developed and published a comprehensive framework for analyzing the carbon footprint of high-performance computing systems. System practitioners can use this analysis framework to gain a better understanding of how sustainable their current system is and consider changes for next-generation systems.  

      Adjusting how models are trained and used

      On top of making adjustments to data center operations, the team is devising ways to make AI-model development more efficient.

      When training models, AI developers often focus on improving accuracy, and they build upon previous models as a starting point. To achieve the desired output, they have to figure out what parameters to use, and getting it right can take testing thousands of configurations. This process, called hyperparameter optimization, is one area LLSC researchers have found ripe for cutting down energy waste. 

      “We’ve developed a model that basically looks at the rate at which a given configuration is learning,” Gadepally says. Given that rate, their model predicts the likely performance. Underperforming models are stopped early. “We can give you a very accurate estimate early on that the best model will be in this top 10 of 100 models running,” he says.

      In their studies, this early stopping led to dramatic savings: an 80 percent reduction in the energy used for model training. They’ve applied this technique to models developed for computer vision, natural language processing, and material design applications.

      “In my opinion, this technique has the biggest potential for advancing the way AI models are trained,” Gadepally says.

      Training is just one part of an AI model’s emissions. The largest contributor to emissions over time is model inference, or the process of running the model live, like when a user chats with ChatGPT. To respond quickly, these models use redundant hardware, running all the time, waiting for a user to ask a question.

      One way to improve inference efficiency is to use the most appropriate hardware. Also with Northeastern University, the team created an optimizer that matches a model with the most carbon-efficient mix of hardware, such as high-power GPUs for the computationally intense parts of inference and low-power central processing units (CPUs) for the less-demanding aspects. This work recently won the best paper award at the International ACM Symposium on High-Performance Parallel and Distributed Computing.

      Using this optimizer can decrease energy use by 10-20 percent while still meeting the same “quality-of-service target” (how quickly the model can respond).

      This tool is especially helpful for cloud customers, who lease systems from data centers and must select hardware from among thousands of options. “Most customers overestimate what they need; they choose over-capable hardware just because they don’t know any better,” Gadepally says.

      Growing green-computing awareness

      The energy saved by implementing these interventions also reduces the associated costs of developing AI, often by a one-to-one ratio. In fact, cost is usually used as a proxy for energy consumption. Given these savings, why aren’t more data centers investing in green techniques?

      “I think it’s a bit of an incentive-misalignment problem,” Samsi says. “There’s been such a race to build bigger and better models that almost every secondary consideration has been put aside.”

      They point out that while some data centers buy renewable-energy credits, these renewables aren’t enough to cover the growing energy demands. The majority of electricity powering data centers comes from fossil fuels, and water used for cooling is contributing to stressed watersheds. 

      Hesitancy may also exist because systematic studies on energy-saving techniques haven’t been conducted. That’s why the team has been pushing their research in peer-reviewed venues in addition to open-source repositories. Some big industry players, like Google DeepMind, have applied machine learning to increase data center efficiency but have not made their work available for others to deploy or replicate. 

      Top AI conferences are now pushing for ethics statements that consider how AI could be misused. The team sees the climate aspect as an AI ethics topic that has not yet been given much attention, but this also appears to be slowly changing. Some researchers are now disclosing the carbon footprint of training the latest models, and industry is showing a shift in energy transparency too, as in this recent report from Meta AI.

      They also acknowledge that transparency is difficult without tools that can show AI developers their consumption. Reporting is on the LLSC roadmap for this year. They want to be able to show every LLSC user, for every job, how much energy they consume and how this amount compares to others, similar to home energy reports.

      Part of this effort requires working more closely with hardware manufacturers to make getting these data off hardware easier and more accurate. If manufacturers can standardize the way the data are read out, then energy-saving and reporting tools can be applied across different hardware platforms. A collaboration is underway between the LLSC researchers and Intel to work on this very problem.

      Even for AI developers who are aware of the intense energy needs of AI, they can’t do much on their own to curb this energy use. The LLSC team wants to help other data centers apply these interventions and provide users with energy-aware options. Their first partnership is with the U.S. Air Force, a sponsor of this research, which operates thousands of data centers. Applying these techniques can make a significant dent in their energy consumption and cost.

      “We’re putting control into the hands of AI developers who want to lessen their footprint,” Gadepally says. “Do I really need to gratuitously train unpromising models? Am I willing to run my GPUs slower to save energy? To our knowledge, no other supercomputing center is letting you consider these options. Using our tools, today, you get to decide.”

      Visit this webpage to see the group’s publications related to energy-aware computing and findings described in this article. More

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      in Environment

      AI pilot programs look to reduce energy use and emissions on MIT campus

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      Smart thermostats have changed the way many people heat and cool their homes by using machine learning to respond to occupancy patterns and preferences, resulting in a lower energy draw. This technology — which can collect and synthesize data — generally focuses on single-dwelling use, but what if this type of artificial intelligence could dynamically manage the heating and cooling of an entire campus? That’s the idea behind a cross-departmental effort working to reduce campus energy use through AI building controls that respond in real-time to internal and external factors. 

      Understanding the challenge

      Heating and cooling can be an energy challenge for campuses like MIT, where existing building management systems (BMS) can’t respond quickly to internal factors like occupancy fluctuations or external factors such as forecast weather or the carbon intensity of the grid. This results in using more energy than needed to heat and cool spaces, often to sub-optimal levels. By engaging AI, researchers have begun to establish a framework to understand and predict optimal temperature set points (the temperature at which a thermostat has been set to maintain) at the individual room level and take into consideration a host of factors, allowing the existing systems to heat and cool more efficiently, all without manual intervention. 

      “It’s not that different from what folks are doing in houses,” explains Les Norford, a professor of architecture at MIT, whose work in energy studies, controls, and ventilation connected him with the effort. “Except we have to think about things like how long a classroom may be used in a day, weather predictions, time needed to heat and cool a room, the effect of the heat from the sun coming in the window, and how the classroom next door might impact all of this.” These factors are at the crux of the research and pilots that Norford and a team are focused on. That team includes Jeremy Gregory, executive director of the MIT Climate and Sustainability Consortium; Audun Botterud, principal research scientist for the Laboratory for Information and Decision Systems; Steve Lanou, project manager in the MIT Office of Sustainability (MITOS); Fran Selvaggio, Department of Facilities Senior Building Management Systems engineer; and Daisy Green and You Lin, both postdocs.

      The group is organized around the call to action to “explore possibilities to employ artificial intelligence to reduce on-campus energy consumption” outlined in Fast Forward: MIT’s Climate Action Plan for the Decade, but efforts extend back to 2019. “As we work to decarbonize our campus, we’re exploring all avenues,” says Vice President for Campus Services and Stewardship Joe Higgins, who originally pitched the idea to students at the 2019 MIT Energy Hack. “To me, it was a great opportunity to utilize MIT expertise and see how we can apply it to our campus and share what we learn with the building industry.” Research into the concept kicked off at the event and continued with undergraduate and graduate student researchers running differential equations and managing pilots to test the bounds of the idea. Soon, Gregory, who is also a MITOS faculty fellow, joined the project and helped identify other individuals to join the team. “My role as a faculty fellow is to find opportunities to connect the research community at MIT with challenges MIT itself is facing — so this was a perfect fit for that,” Gregory says. 

      Early pilots of the project focused on testing thermostat set points in NW23, home to the Department of Facilities and Office of Campus Planning, but Norford quickly realized that classrooms provide many more variables to test, and the pilot was expanded to Building 66, a mixed-use building that is home to classrooms, offices, and lab spaces. “We shifted our attention to study classrooms in part because of their complexity, but also the sheer scale — there are hundreds of them on campus, so [they offer] more opportunities to gather data and determine parameters of what we are testing,” says Norford. 

      Developing the technology

      The work to develop smarter building controls starts with a physics-based model using differential equations to understand how objects can heat up or cool down, store heat, and how the heat may flow across a building façade. External data like weather, carbon intensity of the power grid, and classroom schedules are also inputs, with the AI responding to these conditions to deliver an optimal thermostat set point each hour — one that provides the best trade-off between the two objectives of thermal comfort of occupants and energy use. That set point then tells the existing BMS how much to heat up or cool down a space. Real-life testing follows, surveying building occupants about their comfort. Botterud, whose research focuses on the interactions between engineering, economics, and policy in electricity markets, works to ensure that the AI algorithms can then translate this learning into energy and carbon emission savings. 

      Currently the pilots are focused on six classrooms within Building 66, with the intent to move onto lab spaces before expanding to the entire building. “The goal here is energy savings, but that’s not something we can fully assess until we complete a whole building,” explains Norford. “We have to work classroom by classroom to gather the data, but are looking at a much bigger picture.” The research team used its data-driven simulations to estimate significant energy savings while maintaining thermal comfort in the six classrooms over two days, but further work is needed to implement the controls and measure savings across an entire year. 

      With significant savings estimated across individual classrooms, the energy savings derived from an entire building could be substantial, and AI can help meet that goal, explains Botterud: “This whole concept of scalability is really at the heart of what we are doing. We’re spending a lot of time in Building 66 to figure out how it works and hoping that these algorithms can be scaled up with much less effort to other rooms and buildings so solutions we are developing can make a big impact at MIT,” he says.

      Part of that big impact involves operational staff, like Selvaggio, who are essential in connecting the research to current operations and putting them into practice across campus. “Much of the BMS team’s work is done in the pilot stage for a project like this,” he says. “We were able to get these AI systems up and running with our existing BMS within a matter of weeks, allowing the pilots to get off the ground quickly.” Selvaggio says in preparation for the completion of the pilots, the BMS team has identified an additional 50 buildings on campus where the technology can easily be installed in the future to start energy savings. The BMS team also collaborates with the building automation company, Schneider Electric, that has implemented the new control algorithms in Building 66 classrooms and is ready to expand to new pilot locations. 

      Expanding impact

      The successful completion of these programs will also open the possibility for even greater energy savings — bringing MIT closer to its decarbonization goals. “Beyond just energy savings, we can eventually turn our campus buildings into a virtual energy network, where thousands of thermostats are aggregated and coordinated to function as a unified virtual entity,” explains Higgins. These types of energy networks can accelerate power sector decarbonization by decreasing the need for carbon-intensive power plants at peak times and allowing for more efficient power grid energy use.

      As pilots continue, they fulfill another call to action in Fast Forward — for campus to be a “test bed for change.” Says Gregory: “This project is a great example of using our campus as a test bed — it brings in cutting-edge research to apply to decarbonizing our own campus. It’s a great project for its specific focus, but also for serving as a model for how to utilize the campus as a living lab.” More

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      in Energy

      Jackson Jewett wants to design buildings that use less concrete

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      After three years leading biking tours through U.S. National Parks, Jackson Jewett decided it was time for a change.

      “It was a lot of fun, but I realized I missed buildings,” says Jewett. “I really wanted to be a part of that industry, learn more about it, and reconnect with my roots in the built environment.”

      Jewett grew up in California in what he describes as a “very creative household.”

      “I remember making very elaborate Halloween costumes with my parents, making fun dioramas for school projects, and building forts in the backyard, that kind of thing,” Jewett explains.

      Both of his parents have backgrounds in design; his mother studied art in college and his father is a practicing architect. From a young age, Jewett was interested in following in his father’s footsteps. But when he arrived at the University of California at Berkeley in the midst of the 2009 housing crash, it didn’t seem like the right time. Jewett graduated with a degree in cognitive science and a minor in history of architecture. And even as he led tours through Yellowstone, the Grand Canyon, and other parks, buildings were in the back of his mind.

      It wasn’t just the built environment that Jewett was missing. He also longed for the rigor and structure of an academic environment.

      Jewett arrived at MIT in 2017, initially only planning on completing the master’s program in civil and environmental engineering. It was then that he first met Josephine Carstensen, a newly hired lecturer in the department. Jewett was interested in Carstensen’s work on “topology optimization,” which uses algorithms to design structures that can achieve their performance requirements while using only a limited amount of material. He was particularly interested in applying this approach to concrete design, and he collaborated with Carstensen to help demonstrate its viability.

      After earning his master’s, Jewett spent a year and a half as a structural engineer in New York City. But when Carstensen was hired as a professor, she reached out to Jewett about joining her lab as a PhD student. He was ready for another change.

      Now in the third year of his PhD program, Jewett’s dissertation work builds upon his master’s thesis to further refine algorithms that can design building-scale concrete structures that use less material, which would help lower carbon emissions from the construction industry. It is estimated that the concrete industry alone is responsible for 8 percent of global carbon emissions, so any efforts to reduce that number could help in the fight against climate change.

      Implementing new ideas

      Topology optimization is a small field, with the bulk of the prior work being computational without any experimental verification. The work Jewett completed for his master’s thesis was just the start of a long learning process.

      “I do feel like I’m just getting to the part where I can start implementing my own ideas without as much support as I’ve needed in the past,” says Jewett. “In the last couple of months, I’ve been working on a reinforced concrete optimization algorithm that I hope will be the cornerstone of my thesis.”

      The process of fine-tuning a generative algorithm is slow going, particularly when tackling a multifaceted problem.

      “It can take days or usually weeks to take a step toward making it work as an entire integrated system,” says Jewett. “The days when that breakthrough happens and I can see the algorithm converging on a solution that makes sense — those are really exciting moments.”

      By harnessing computational power, Jewett is searching for materially efficient components that can be used to make up structures such as bridges or buildings. These are other constraints to consider as well, particularly ensuring that the cost of manufacturing isn’t too high. Having worked in the industry before starting the PhD program, Jewett has an eye toward doing work that can be feasibly implemented.

      Inspiring others

      When Jewett first visited MIT campus, he was drawn in by the collaborative environment of the institute and the students’ drive to learn. Now, he’s a part of that process as a teaching assistant and a supervisor in the Undergraduate Research Opportunities Program.  

      Working as a teaching assistant isn’t a requirement for Jewett’s program, but it’s been one of his favorite parts of his time at MIT.

      “The MIT undergrads are so gifted and just constantly impress me,” says Jewett. “Being able to teach, especially in the context of what MIT values is a lot of fun. And I learn, too. My coding practices have gotten so much better since working with undergrads here.”

      Jewett’s experiences have inspired him to pursue a career in academia after the completion of his program, which he expects to complete in the spring of 2025. But he’s making sure to take care of himself along the way. He still finds time to plan cycling trips with his friends and has gotten into running ever since moving to Boston. So far, he’s completed two marathons.

      “It’s so inspiring to be in a place where so many good ideas are just bouncing back and forth all over campus,” says Jewett. “And on most days, I remember that and it inspires me. But it’s also the case that academics is hard, PhD programs are hard, and MIT — there’s pressure being here, and sometimes that pressure can feel like it’s working against you.”

      Jewett is grateful for the mental health resources that MIT provides students. While he says it can be imperfect, it’s been a crucial part of his journey.

      “My PhD thesis will be done in 2025, but the work won’t be done. The time horizon of when these things need to be implemented is relatively short if we want to make an impact before global temperatures have already risen too high. My PhD research will be developing a framework for how that could be done with concrete construction, but I’d like to keep thinking about other materials and construction methods even after this project is finished.” More

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      in Environment

      Technologies for water conservation and treatment move closer to commercialization

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      The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) provides Solutions Grants to help MIT researchers launch startup companies or products to commercialize breakthrough technologies in water and food systems. The Solutions Grant Program began in 2015 and is supported by Community Jameel. In addition to one-year, renewable grants of up to $150,000, the program also matches grantees with industry mentors and facilitates introductions to potential investors. Since its inception, the J-WAFS Solutions Program has awarded over $3 million in funding to the MIT community. Numerous startups and products, including a portable desalination device and a company commercializing a novel food safety sensor, have spun out of this support.

      The 2023 J-WAFS Solutions Grantees are Professor C. Cem Tasan of the Department of Materials Science and Engineering and Professor Andrew Whittle of the Department of Civil and Environmental Engineering. Tasan’s project involves reducing water use in steel manufacturing and Whittle’s project tackles harmful algal blooms in water. Project work commences this September.

      “This year’s Solutions Grants are being award to professors Tasan and Whittle to help commercialize technologies they have been developing at MIT,” says J-WAFS executive director Renee J. Robins. “With J-WAFS’ support, we hope to see the teams move their technologies from the lab to the market, so they can have a beneficial impact on water use and water quality challenges,” Robins adds.

      Reducing water consumption by solid-state steelmaking

      Water is a major requirement for steel production. The steel industry ranks fourth in industrial freshwater consumption worldwide, since large amounts of water are needed mainly for cooling purposes in the process. Unfortunately, a strong correlation has also been shown to exist between freshwater use in steelmaking and water contamination. As the global demand for steel increases and freshwater availability decreases due to climate change, improved methods for more sustainable steel production are needed.

      A strategy to reduce the water footprint of steelmaking is to explore steel recycling processes that avoid liquid metal processing. With this motivation, Cem Tasan, the Thomas B. King Associate Professor of Metallurgy in the Department of Materials Science and Engineering, and postdoc Onur Guvenc PhD created a new process called Scrap Metal Consolidation (SMC). SMC is based on a well-established metal forming process known as roll bonding. Conventionally, roll bonding requires intensive prior surface treatment of the raw material, specific atmospheric conditions, and high deformation levels. Tasan and Guvenc’s research revealed that SMC can overcome these restrictions by enabling the solid-state bonding of scrap into a sheet metal form, even when the surface quality, atmospheric conditions, and deformation levels are suboptimal. Through lab-scale proof-of-principle investigations, they have already identified SMC process conditions and validated the mechanical formability of resulting steel sheets, focusing on mild steel, the most common sheet metal scrap.

      The J-WAFS Solutions Grant will help the team to build customer product prototypes, design the processing unit, and develop a scale-up strategy and business model. By simultaneously decreasing water usage, energy demand, contamination risk, and carbon dioxide burden, SMC has the potential to decrease the energy need for steel recycling by up to 86 percent, as well as reduce the linked carbon dioxide emissions and safeguard the freshwater resources that would otherwise be directed to industrial consumption. 

      Detecting harmful algal blooms in water before it’s too late

      Harmful algal blooms (HABs) are a growing problem in both freshwater and saltwater environments worldwide, causing an estimated $13 billion in annual damage to drinking water, water for recreational use, commercial fishing areas, and desalination activities. HABs pose a threat to both human health and aquaculture, thereby threatening the food supply. Toxins in HABs are produced by some cyanobacteria, or blue-green algae, whose communities change in composition in response to eutrophication from agricultural runoff, sewer overflows, or other events. Mitigation of risks from HABs are most effective when there is advance warning of these changes in algal communities. 

      Most in situ measurements of algae are based on fluorescence spectroscopy that is conducted with LED-induced fluorescence (LEDIF) devices, or probes that induce fluorescence of specific algal pigments using LED light sources. While LEDIFs provide reasonable estimates of concentrations of individual pigments, they lack resolution to discriminate algal classes within complex mixtures found in natural water bodies. In prior research, Andrew Whittle, the Edmund K. Turner Professor of Civil and Environmental Engineering, worked with colleagues to design REMORA, a low-cost, field-deployable prototype spectrofluorometer for measuring induced fluorescence. This research was part of a collaboration between MIT and the AMS Institute. Whittle and the team successfully trained a machine learning model to discriminate and quantify cell concentrations for mixtures of different algal groups in water samples through an extensive laboratory calibration program using various algae cultures. The group demonstrated these capabilities in a series of field measurements at locations in Boston and Amsterdam. 

      Whittle will work with Fábio Duarte of the Department of Urban Studies and Planning, the Senseable City Lab, and MIT’s Center for Real Estate to refine the design of REMORA. They will develop software for autonomous operation of the sensor that can be deployed remotely on mobile vessels or platforms to enable high-resolution spatiotemporal monitoring for harmful algae. Sensor commercialization will hopefully be able to exploit the unique capabilities of REMORA for long-term monitoring applications by water utilities, environmental regulatory agencies, and water-intensive industries.  More

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      in Energy

      Fast-tracking fusion energy’s arrival with AI and accessibility

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      As the impacts of climate change continue to grow, so does interest in fusion’s potential as a clean energy source. While fusion reactions have been studied in laboratories since the 1930s, there are still many critical questions scientists must answer to make fusion power a reality, and time is of the essence. As part of their strategy to accelerate fusion energy’s arrival and reach carbon neutrality by 2050, the U.S. Department of Energy (DoE) has announced new funding for a project led by researchers at MIT’s Plasma Science and Fusion Center (PSFC) and four collaborating institutions.

      Cristina Rea, a research scientist and group leader at the PSFC, will serve as the primary investigator for the newly funded three-year collaboration to pilot the integration of fusion data into a system that can be read by AI-powered tools. The PSFC, together with scientists from the College of William and Mary, the University of Wisconsin at Madison, Auburn University, and the nonprofit HDF Group, plan to create a holistic fusion data platform, the elements of which could offer unprecedented access for researchers, especially underrepresented students. The project aims to encourage diverse participation in fusion and data science, both in academia and the workforce, through outreach programs led by the group’s co-investigators, of whom four out of five are women. 

      The DoE’s award, part of a $29 million funding package for seven projects across 19 institutions, will support the group’s efforts to distribute data produced by fusion devices like the PSFC’s Alcator C-Mod, a donut-shaped “tokamak” that utilized powerful magnets to control and confine fusion reactions. Alcator C-Mod operated from 1991 to 2016 and its data are still being studied, thanks in part to the PSFC’s commitment to the free exchange of knowledge.

      Currently, there are nearly 50 public experimental magnetic confinement-type fusion devices; however, both historical and current data from these devices can be difficult to access. Some fusion databases require signing user agreements, and not all data are catalogued and organized the same way. Moreover, it can be difficult to leverage machine learning, a class of AI tools, for data analysis and to enable scientific discovery without time-consuming data reorganization. The result is fewer scientists working on fusion, greater barriers to discovery, and a bottleneck in harnessing AI to accelerate progress.

      The project’s proposed data platform addresses technical barriers by being FAIR — Findable, Interoperable, Accessible, Reusable — and by adhering to UNESCO’s Open Science (OS) recommendations to improve the transparency and inclusivity of science; all of the researchers’ deliverables will adhere to FAIR and OS principles, as required by the DoE. The platform’s databases will be built using MDSplusML, an upgraded version of the MDSplus open-source software developed by PSFC researchers in the 1980s to catalogue the results of Alcator C-Mod’s experiments. Today, nearly 40 fusion research institutes use MDSplus to store and provide external access to their fusion data. The release of MDSplusML aims to continue that legacy of open collaboration.

      The researchers intend to address barriers to participation for women and disadvantaged groups not only by improving general access to fusion data, but also through a subsidized summer school that will focus on topics at the intersection of fusion and machine learning, which will be held at William and Mary for the next three years.

      Of the importance of their research, Rea says, “This project is about responding to the fusion community’s needs and setting ourselves up for success. Scientific advancements in fusion are enabled via multidisciplinary collaboration and cross-pollination, so accessibility is absolutely essential. I think we all understand now that diverse communities have more diverse ideas, and they allow faster problem-solving.”

      The collaboration’s work also aligns with vital areas of research identified in the International Atomic Energy Agency’s “AI for Fusion” Coordinated Research Project (CRP). Rea was selected as the technical coordinator for the IAEA’s CRP emphasizing community engagement and knowledge access to accelerate fusion research and development. In a letter of support written for the group’s proposed project, the IAEA stated that, “the work [the researchers] will carry out […] will be beneficial not only to our CRP but also to the international fusion community in large.”

      PSFC Director and Hitachi America Professor of Engineering Dennis Whyte adds, “I am thrilled to see PSFC and our collaborators be at the forefront of applying new AI tools while simultaneously encouraging and enabling extraction of critical data from our experiments.”

      “Having the opportunity to lead such an important project is extremely meaningful, and I feel a responsibility to show that women are leaders in STEM,” says Rea. “We have an incredible team, strongly motivated to improve our fusion ecosystem and to contribute to making fusion energy a reality.” More

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      in Environment

      The curse of variety in transportation systems

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      Cathy Wu has always delighted in systems that run smoothly. In high school, she designed a project to optimize the best route for getting to class on time. Her research interests and career track are evidence of a propensity for organizing and optimizing, coupled with a strong sense of responsibility to contribute to society instilled by her parents at a young age.

      As an undergraduate at MIT, Wu explored domains like agriculture, energy, and education, eventually homing in on transportation. “Transportation touches each of our lives,” she says. “Every day, we experience the inefficiencies and safety issues as well as the environmental harms associated with our transportation systems. I believe we can and should do better.”

      But doing so is complicated. Consider the long-standing issue of traffic systems control. Wu explains that it is not one problem, but more accurately a family of control problems impacted by variables like time of day, weather, and vehicle type — not to mention the types of sensing and communication technologies used to measure roadway information. Every differentiating factor introduces an exponentially larger set of control problems. There are thousands of control-problem variations and hundreds, if not thousands, of studies and papers dedicated to each problem. Wu refers to the sheer number of variations as the curse of variety — and it is hindering innovation.

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      “To prove that a new control strategy can be safely deployed on our streets can take years. As time lags, we lose opportunities to improve safety and equity while mitigating environmental impacts. Accelerating this process has huge potential,” says Wu.  

      Which is why she and her group in the MIT Laboratory for Information and Decision Systems are devising machine learning-based methods to solve not just a single control problem or a single optimization problem, but families of control and optimization problems at scale. “In our case, we’re examining emerging transportation problems that people have spent decades trying to solve with classical approaches. It seems to me that we need a different approach.”

      Optimizing intersections

      Currently, Wu’s largest research endeavor is called Project Greenwave. There are many sectors that directly contribute to climate change, but transportation is responsible for the largest share of greenhouse gas emissions — 29 percent, of which 81 percent is due to land transportation. And while much of the conversation around mitigating environmental impacts related to mobility is focused on electric vehicles (EVs), electrification has its drawbacks. EV fleet turnover is time-consuming (“on the order of decades,” says Wu), and limited global access to the technology presents a significant barrier to widespread adoption.

      Wu’s research, on the other hand, addresses traffic control problems by leveraging deep reinforcement learning. Specifically, she is looking at traffic intersections — and for good reason. In the United States alone, there are more than 300,000 signalized intersections where vehicles must stop or slow down before re-accelerating. And every re-acceleration burns fossil fuels and contributes to greenhouse gas emissions.

      Highlighting the magnitude of the issue, Wu says, “We have done preliminary analysis indicating that up to 15 percent of land transportation CO2 is wasted through energy spent idling and re-accelerating at intersections.”

      To date, she and her group have modeled 30,000 different intersections across 10 major metropolitan areas in the United States. That is 30,000 different configurations, roadway topologies (e.g., grade of road or elevation), different weather conditions, and variations in travel demand and fuel mix. Each intersection and its corresponding scenarios represents a unique multi-agent control problem.

      Wu and her team are devising techniques that can solve not just one, but a whole family of problems comprised of tens of thousands of scenarios. Put simply, the idea is to coordinate the timing of vehicles so they arrive at intersections when traffic lights are green, thereby eliminating the start, stop, re-accelerate conundrum. Along the way, they are building an ecosystem of tools, datasets, and methods to enable roadway interventions and impact assessments of strategies to significantly reduce carbon-intense urban driving.

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      Their collaborator on the project is the Utah Department of Transportation, which Wu says has played an essential role, in part by sharing data and practical knowledge that she and her group otherwise would not have been able to access publicly.

      “I appreciate industry and public sector collaborations,” says Wu. “When it comes to important societal problems, one really needs grounding with practitioners. One needs to be able to hear the perspectives in the field. My interactions with practitioners expand my horizons and help ground my research. You never know when you’ll hear the perspective that is the key to the solution, or perhaps the key to understanding the problem.”

      Finding the best routes

      In a similar vein, she and her research group are tackling large coordination problems. For example, vehicle routing. “Every day, delivery trucks route more than a hundred thousand packages for the city of Boston alone,” says Wu. Accomplishing the task requires, among other things, figuring out which trucks to use, which packages to deliver, and the order in which to deliver them as efficiently as possible. If and when the trucks are electrified, they will need to be charged, adding another wrinkle to the process and further complicating route optimization.

      The vehicle routing problem, and therefore the scope of Wu’s work, extends beyond truck routing for package delivery. Ride-hailing cars may need to pick up objects as well as drop them off; and what if delivery is done by bicycle or drone? In partnership with Amazon, for example, Wu and her team addressed routing and path planning for hundreds of robots (up to 800) in their warehouses.

      Every variation requires custom heuristics that are expensive and time-consuming to develop. Again, this is really a family of problems — each one complicated, time-consuming, and currently unsolved by classical techniques — and they are all variations of a central routing problem. The curse of variety meets operations and logistics.

      By combining classical approaches with modern deep-learning methods, Wu is looking for a way to automatically identify heuristics that can effectively solve all of these vehicle routing problems. So far, her approach has proved successful.

      “We’ve contributed hybrid learning approaches that take existing solution methods for small problems and incorporate them into our learning framework to scale and accelerate that existing solver for large problems. And we’re able to do this in a way that can automatically identify heuristics for specialized variations of the vehicle routing problem.” The next step, says Wu, is applying a similar approach to multi-agent robotics problems in automated warehouses.

      Wu and her group are making big strides, in part due to their dedication to use-inspired basic research. Rather than applying known methods or science to a problem, they develop new methods, new science, to address problems. The methods she and her team employ are necessitated by societal problems with practical implications. The inspiration for the approach? None other than Louis Pasteur, who described his research style in a now-famous article titled “Pasteur’s Quadrant.” Anthrax was decimating the sheep population, and Pasteur wanted to better understand why and what could be done about it. The tools of the time could not solve the problem, so he invented a new field, microbiology, not out of curiosity but out of necessity. More

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      in Environment

      Preparing Colombia’s cities for life amid changing forests

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      It was an uncharacteristically sunny morning as Marcela Angel MCP ’18, flanked by a drone pilot from the Boston engineering firm AirWorks and a data collection team from the Colombian regional environmental agency Corpoamazonia, climbed a hill in the Andes Mountains of southwest Colombia. The area’s usual mountain cloud cover — one of the major challenges to working with satellite imagery or flying UAVs (unpiloted aerial vehicles, or drones) in the Pacific highlands of the Amazon — would roll through in the hours to come. But for now, her team had chosen a good day to hike out for their first flight. Angel is used to long travel for her research. Raised in Bogotá, she maintained strong ties to Colombia throughout her master’s program in the MIT Department of Urban Studies and Planning (DUSP). Her graduate thesis, examining Bogotá’s management of its public green space, took her regularly back to her hometown, exploring how the city could offer residents more equal access to the clean air, flood protection and day-to-day health and social benefits provided by parks and trees. But the hill she was hiking this morning, outside the remote city of Mocoa, had taken an especially long time to climb: five years building relationships with the community of Mocoa and the Colombian government, recruiting project partners, and navigating the bureaucracy of bringing UAVs into the country. Now, her team finally unwrapped their first, knee-high drone from its tarp and set it carefully in the grass. Under the gathering gray clouds, the buzz of its rotors joined the hum of insects in the trees, and the machine at last took to the skies.

      From Colombia to Cambridge

      “I actually grew up on the last street before the eastern mountains reserve,” Angel says of her childhood in Bogotá. “I’ve always been at that border between city and nature.” This idea, that urban areas are married to the ecosystems around them, would inform Angel’s whole education and career. Before coming to MIT, she studied architecture at Bogotá’s Los Andes University; for her graduation project she proposed a plan to resettle an informal neighborhood on Bogotá’s outskirts to minimize environmental risks to its residents. Among her projects at MIT was an initiative to spatially analyze Bogotá’s tree canopy, providing data for the city to plan a tree-planting program as a strategy to give vulnerable populations in the city more access to nature. And she was naturally intrigued when Colombia’s former minister of environment and sustainable development came to MIT in 2017 to give a guest presentation to the DUSP master’s program. The minister, Luis Gilberto Murillo (now the Colombian ambassador to the United States), introduced the students to the challenges triggered by a recent disaster in the city of Mocoa, on the border between the lowland Amazon and the Andes Mountains. Unprecedented rainstorms had destabilized the surrounding forests, and that April a devastating flood and landslide had killed hundreds of people and destroyed entire neighborhoods. And as climate change contributed to growing rainfall in the region, the risks of more landslide events were rising. Murillo provided useful insights into how city planning decisions had contributed to the crisis. But he also asked for MIT’s support addressing future landslide risks in the area. Angel and Juan Camilo Osorio, a PhD candidate at DUSP, decided to take up the challenge, and in January 2018 and 2019, a research delegation from MIT traveled to Colombia for a newly-created graduate course. Returning once again to Bogotá, Angel interviewed government agencies and nonprofits to understand the state of landslide monitoring and public policy. In Mocoa, further interviews and a series of workshops helped clarify what locals needed most and what MIT could provide: better information on where and when landslides might strike, and a process to increase risk awareness and involve traditionally marginalized groups in decision-making processes around that risk. Over the coming year, a core team formed to put the insights from this trip into action, including Angel, Osorio, postdoc Norhan Bayomi of the MIT Environmental Solutions Initiative (ESI) and MIT Professor John Fernández, director of the ESI and one of Angel’s mentors at DUSP. After a second visit to Mocoa that brought into the fold Indigenous groups, environmental agencies, and the national army, a plan was formed: MIT would partner with Corpoamazonia and build a network of community researchers to deploy and test drone technology and machine learning models to monitor the mountain forests for both landslide risks and signs of forest health, while implementing a participatory planning process with residents. “What our projects aim to do is give the communities new tools to continue protecting and restoring the forest,” says Angel, “and support new and inclusive development models, even in the face of new challenges.”

      Lifelines for the climate

      The goal of tropical forest conservation is an urgent one. As forests are cut down, their trees and soils release carbon they have stored over millennia, adding huge amounts of heat-trapping carbon dioxide to the atmosphere. Deforestation, mainly in the tropics, is now estimated to contribute more to climate change than any country besides the United States and China — and once lost, tropical forests are exceptionally hard to restore. “Tropical forests should be a natural way to slow and reverse climate change,” says Angel. “And they can be. But today, we are reaching critical tipping points where it is just the opposite.” This became the motivating force for Angel’s career after her graduation. In 2019, Fernández invited her to join the ESI and lead a new Natural Climate Solutions Program, with the Mocoa project as its first centerpiece. She quickly mobilized the partners to raise funding for the project from the Global Environmental Facility and the CAF Development Bank of Latin America and the Caribbean, and recruited additional partners including MIT Lincoln Laboratories, AirWorks, and the Pratt Institute, where Osorio had become an assistant professor. She hired machine learning specialists from MIT to begin design on UAVs’ data processing, and helped assemble a local research network in Mocoa to increase risk awareness, promote community participation, and better understand what information city officials and community groups needed for city planning and conservation. “This is the amazing thing about MIT,” she says. “When you study a problem here, you’re not just playing in a sandbox. Everyone I’ve worked with is motivated by the complexity of the technical challenge and the opportunity for meaningful engagement in Mocoa, and hopefully in many more places besides.” At the same time, Angel created opportunities for the next generation of MIT graduate students to follow in her footsteps. With Fernández and Bayomi, she created a new course, 4.S23 (Biodiversity and Cities), in which students traveled to Colombia to develop urban planning strategies for the cities of Quidbó and Leticia, located in carbon-rich and biodiverse areas. The course has been taught twice, with Professor Gabriella Carolini joining the teaching team for spring 2023, and has already led to a student report to city officials in Quidbó recommending ways to enhance biodiversity and adapt to climate change as the city grows, a multi-stakeholder partnership to train local youth and implement a citizen-led biodiversity survey, and a seed grant from the MIT Climate and Sustainability Consortium to begin providing both cities detailed data on their tree cover derived from satellite images. “These regions face serious threats, especially on a warming planet, but many of the solutions for climate change, biodiversity conservation, and environmental equity in the region go hand-in-hand,” Angel says. “When you design a city to use fewer resources, to contribute less to climate change, it also causes less pressure on the environment around it. When you design a city for equity and quality of life, you’re giving attention to its green spaces and what they can provide for people and as habitat for other species. When you protect and restore forests, you’re protecting local bioeconomies.”

      Bringing the data home

      Meanwhile, in Mocoa, Angel’s original vision is taking flight. With the team’s test flights behind them, they can now begin creating digital models of the surrounding area. Regular drone flights and soil samples will fill in changing information about trees, water, and local geology, allowing the project’s machine learning specialists to identify warning signs for future landslides and extreme weather events. More importantly, there is now an established network of local community researchers and leaders ready to make use of this information. With feedback from their Mocoan partners, Angel’s team has built a prototype of the online platform they will use to share their UAV data; they’re now letting Mocoa residents take it for a test drive and suggest how it can be made more user-friendly. Her visit this January also paved the way for new projects that will tie the Environmental Solutions Initiative more tightly to Mocoa. With her project partners, Angel is exploring developing a course to teach local students how to use UAVs like the ones her team is flying. She is also considering expanded efforts to collect the kind of informal knowledge of Mocoa, on the local ecology and culture, that people everywhere use in making their city planning and emergency response decisions, but that is rarely codified and included in scientific risk analyses. It’s a great deal of work to offer this one community the tools to adapt successfully to climate change. But even with all the robotics and machine learning models in the world, this close, slow-unfolding engagement, grounded in trust and community inclusion, is what it takes to truly prepare people to confront profound changes in their city and environment. “Protecting natural carbon sinks is a global socio-environmental challenge, and one where it is not enough for MIT to just contribute to the knowledge base or develop a new technology,” says Angel. “But we can help mobilize decision-makers and nontraditional actors, and design more inclusive and technology-enhanced processes, to make this easier for the people who have lifelong stakes in these ecosystems. That is the vision.” More

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      in Environment

      J-WAFS announces 2023 seed grant recipients

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      Today, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) announced its ninth round of seed grants to support innovative research projects at MIT. The grants are designed to fund research efforts that tackle challenges related to water and food for human use, with the ultimate goal of creating meaningful impact as the world population continues to grow and the planet undergoes significant climate and environmental changes.Ten new projects led by 15 researchers from seven different departments will be supported this year. The projects address a range of challenges by employing advanced materials, technology innovations, and new approaches to resource management. The new projects aim to remove harmful chemicals from water sources, develop monitoring and other systems to help manage various aquaculture industries, optimize water purification materials, and more.“The seed grant program is J-WAFS’ flagship grant initiative,” says J-WAFS executive director Renee J. Robins. “The funding is intended to spur groundbreaking MIT research addressing complex issues that are challenging our water and food systems. The 10 projects selected this year show great promise, and we look forward to the progress and accomplishments these talented researchers will make,” she adds.The 2023 J-WAFS seed grant researchers and their projects are:Sara Beery, an assistant professor in the Department of Electrical Engineering and Computer Science (EECS), is building the first completely automated system to estimate the size of salmon populations in the Pacific Northwest (PNW).Salmon are a keystone species in the PNW, feeding human populations for the last 7,500 years at least. However, overfishing, habitat loss, and climate change threaten extinction of salmon populations across the region. Accurate salmon counts during their seasonal migration to their natal river to spawn are essential for fisheries’ regulation and management but are limited by human capacity. Fish population monitoring is a widespread challenge in the United States and worldwide. Beery and her team are working to build a system that will provide a detailed picture of the state of salmon populations in unprecedented, spatial, and temporal resolution by combining sonar sensors and computer vision and machine learning (CVML) techniques. The sonar will capture individual fish as they swim upstream and CVML will train accurate algorithms to interpret the sonar video for detecting, tracking, and counting fish automatically while adapting to changing river conditions and fish densities.Another aquaculture project is being led by Michael Triantafyllou, the Henry L. and Grace Doherty Professor in Ocean Science and Engineering in the Department of Mechanical Engineering, and Robert Vincent, the assistant director at MIT’s Sea Grant Program. They are working with Otto Cordero, an associate professor in the Department of Civil and Environmental Engineering, to control harmful bacteria blooms in aquaculture algae feed production.

      Aquaculture in the United States represents a $1.5 billion industry annually and helps support 1.7 million jobs, yet many American hatcheries are not able to keep up with demand. One barrier to aquaculture production is the high degree of variability in survival rates, most likely caused by a poorly controlled microbiome that leads to bacterial infections and sub-optimal feed efficiency. Triantafyllou, Vincent, and Cordero plan to monitor the microbiome composition of a shellfish hatchery in order to identify possible causing agents of mortality, as well as beneficial microbes. They hope to pair microbe data with detail phenotypic information about the animal population to generate rapid diagnostic tests and explore the potential for microbiome therapies to protect larvae and prevent future outbreaks. The researchers plan to transfer their findings and technology to the local and regional aquaculture community to ensure healthy aquaculture production that will support the expansion of the U.S. aquaculture industry.

      David Des Marais is the Cecil and Ida Green Career Development Professor in the Department of Civil and Environmental Engineering. His 2023 J-WAFS project seeks to understand plant growth responses to elevated carbon dioxide (CO2) in the atmosphere, in the hopes of identifying breeding strategies that maximize crop yield under future CO2 scenarios.Today’s crop plants experience higher atmospheric CO2 than 20 or 30 years ago. Crops such as wheat, oat, barley, and rice typically increase their growth rate and biomass when grown at experimentally elevated atmospheric CO2. This is known as the so-called “CO2 fertilization effect.” However, not all plant species respond to rising atmospheric CO2 with increased growth, and for the ones that do, increased growth doesn’t necessarily correspond to increased crop yield. Using specially built plant growth chambers that can control the concentration of CO2, Des Marais will explore how CO2 availability impacts the development of tillers (branches) in the grass species Brachypodium. He will study how gene expression controls tiller development, and whether this is affected by the growing environment. The tillering response refers to how many branches a plant produces, which sets a limit on how much grain it can yield. Therefore, optimizing the tillering response to elevated CO2 could greatly increase yield. Des Marais will also look at the complete genome sequence of Brachypodium, wheat, oat, and barley to help identify genes relevant for branch growth.Darcy McRose, an assistant professor in the Department of Civil and Environmental Engineering, is researching whether a combination of plant metabolites and soil bacteria can be used to make mineral-associated phosphorus more bioavailable.The nutrient phosphorus is essential for agricultural plant growth, but when added as a fertilizer, phosphorus sticks to the surface of soil minerals, decreasing bioavailability, limiting plant growth, and accumulating residual phosphorus. Heavily fertilized agricultural soils often harbor large reservoirs of this type of mineral-associated “legacy” phosphorus. Redox transformations are one chemical process that can liberate mineral-associated phosphorus. However, this needs to be carefully controlled, as overly mobile phosphorus can lead to runoff and pollution of natural waters. Ideally, phosphorus would be made bioavailable when plants need it and immobile when they don’t. Many plants make small metabolites called coumarins that might be able to solubilize mineral-adsorbed phosphorus and be activated and inactivated under different conditions. McRose will use laboratory experiments to determine whether a combination of plant metabolites and soil bacteria can be used as a highly efficient and tunable system for phosphorus solubilization. She also aims to develop an imaging platform to investigate exchanges of phosphorus between plants and soil microbes.Many of the 2023 seed grants will support innovative technologies to monitor, quantify, and remediate various kinds of pollutants found in water. Two of the new projects address the problem of per- and polyfluoroalkyl substances (PFAS), human-made chemicals that have recently emerged as a global health threat. Known as “forever chemicals,” PFAS are used in many manufacturing processes. These chemicals are known to cause significant health issues including cancer, and they have become pervasive in soil, dust, air, groundwater, and drinking water. Unfortunately, the physical and chemical properties of PFAS render them difficult to detect and remove.Aristide Gumyusenge, the Merton C. Assistant Professor of Materials Science and Engineering, is using metal-organic frameworks for low-cost sensing and capture of PFAS. Most metal-organic frameworks (MOFs) are synthesized as particles, which complicates their high accuracy sensing performance due to defects such as intergranular boundaries. Thin, film-based electronic devices could enable the use of MOFs for many applications, especially chemical sensing. Gumyusenge’s project aims to design test kits based on two-dimensional conductive MOF films for detecting PFAS in drinking water. In early demonstrations, Gumyusenge and his team showed that these MOF films can sense PFAS at low concentrations. They will continue to iterate using a computation-guided approach to tune sensitivity and selectivity of the kits with the goal of deploying them in real-world scenarios.Carlos Portela, the Brit (1961) and Alex (1949) d’Arbeloff Career Development Professor in the Department of Mechanical Engineering, and Ariel Furst, the Cook Career Development Professor in the Department of Chemical Engineering, are building novel architected materials to act as filters for the removal of PFAS from water. Portela and Furst will design and fabricate nanoscale materials that use activated carbon and porous polymers to create a physical adsorption system. They will engineer the materials to have tunable porosities and morphologies that can maximize interactions between contaminated water and functionalized surfaces, while providing a mechanically robust system.Rohit Karnik is a Tata Professor and interim co-department head of the Department of Mechanical Engineering. He is working on another technology, his based on microbead sensors, to rapidly measure and monitor trace contaminants in water.Water pollution from both biological and chemical contaminants contributes to an estimated 1.36 million deaths annually. Chemical contaminants include pesticides and herbicides, heavy metals like lead, and compounds used in manufacturing. These emerging contaminants can be found throughout the environment, including in water supplies. The Environmental Protection Agency (EPA) in the United States sets recommended water quality standards, but states are responsible for developing their own monitoring criteria and systems, which must be approved by the EPA every three years. However, the availability of data on regulated chemicals and on candidate pollutants is limited by current testing methods that are either insensitive or expensive and laboratory-based, requiring trained scientists and technicians. Karnik’s project proposes a simple, self-contained, portable system for monitoring trace and emerging pollutants in water, making it suitable for field studies. The concept is based on multiplexed microbead-based sensors that use thermal or gravitational actuation to generate a signal. His proposed sandwich assay, a testing format that is appealing for environmental sensing, will enable both single-use and continuous monitoring. The hope is that the bead-based assays will increase the ease and reach of detecting and quantifying trace contaminants in water for both personal and industrial scale applications.Alexander Radosevich, a professor in the Department of Chemistry, and Timothy Swager, the John D. MacArthur Professor of Chemistry, are teaming up to create rapid, cost-effective, and reliable techniques for on-site arsenic detection in water.Arsenic contamination of groundwater is a problem that affects as many as 500 million people worldwide. Arsenic poisoning can lead to a range of severe health problems from cancer to cardiovascular and neurological impacts. Both the EPA and the World Health Organization have established that 10 parts per billion is a practical threshold for arsenic in drinking water, but measuring arsenic in water at such low levels is challenging, especially in resource-limited environments where access to sensitive laboratory equipment may not be readily accessible. Radosevich and Swager plan to develop reaction-based chemical sensors that bind and extract electrons from aqueous arsenic. In this way, they will exploit the inherent reactivity of aqueous arsenic to selectively detect and quantify it. This work will establish the chemical basis for a new method of detecting trace arsenic in drinking water.Rajeev Ram is a professor in the Department of Electrical Engineering and Computer Science. His J-WAFS research will advance a robust technology for monitoring nitrogen-containing pollutants, which threaten over 15,000 bodies of water in the United States alone.Nitrogen in the form of nitrate, nitrite, ammonia, and urea can run off from agricultural fertilizer and lead to harmful algal blooms that jeopardize human health. Unfortunately, monitoring these contaminants in the environment is challenging, as sensors are difficult to maintain and expensive to deploy. Ram and his students will work to establish limits of detection for nitrate, nitrite, ammonia, and urea in environmental, industrial, and agricultural samples using swept-source Raman spectroscopy. Swept-source Raman spectroscopy is a method of detecting the presence of a chemical by using a tunable, single mode laser that illuminates a sample. This method does not require costly, high-power lasers or a spectrometer. Ram will then develop and demonstrate a portable system that is capable of achieving chemical specificity in complex, natural environments. Data generated by such a system should help regulate polluters and guide remediation.Kripa Varanasi, a professor in the Department of Mechanical Engineering, and Angela Belcher, the James Mason Crafts Professor and head of the Department of Biological Engineering, will join forces to develop an affordable water disinfection technology that selectively identifies, adsorbs, and kills “superbugs” in domestic and industrial wastewater.Recent research predicts that antibiotic-resistance bacteria (superbugs) will result in $100 trillion in health care expenses and 10 million deaths annually by 2050. The prevalence of superbugs in our water systems has increased due to corroded pipes, contamination, and climate change. Current drinking water disinfection technologies are designed to kill all types of bacteria before human consumption. However, for certain domestic and industrial applications there is a need to protect the good bacteria required for ecological processes that contribute to soil and plant health. Varanasi and Belcher will combine material, biological, process, and system engineering principles to design a sponge-based water disinfection technology that can identify and destroy harmful bacteria while leaving the good bacteria unharmed. By modifying the sponge surface with specialized nanomaterials, their approach will be able to kill superbugs faster and more efficiently. The sponge filters can be deployed under very low pressure, making them an affordable technology, especially in resource-constrained communities.In addition to the 10 seed grant projects, J-WAFS will also fund a research initiative led by Greg Sixt. Sixt is the research manager for climate and food systems at J-WAFS, and the director of the J-WAFS-led Food and Climate Systems Transformation (FACT) Alliance. His project focuses on the Lake Victoria Basin (LVB) of East Africa. The second-largest freshwater lake in the world, Lake Victoria straddles three countries (Uganda, Tanzania, and Kenya) and has a catchment area that encompasses two more (Rwanda and Burundi). Sixt will collaborate with Michael Hauser of the University of Natural Resources and Life Sciences, Vienna, and Paul Kariuki, of the Lake Victoria Basin Commission.The group will study how to adapt food systems to climate change in the Lake Victoria Basin. The basin is facing a range of climate threats that could significantly impact livelihoods and food systems in the expansive region. For example, extreme weather events like droughts and floods are negatively affecting agricultural production and freshwater resources. Across the LVB, current approaches to land and water management are unsustainable and threaten future food and water security. The Lake Victoria Basin Commission (LVBC), a specialized institution of the East African Community, wants to play a more vital role in coordinating transboundary land and water management to support transitions toward more resilient, sustainable, and equitable food systems. The primary goal of this research will be to support the LVBC’s transboundary land and water management efforts, specifically as they relate to sustainability and climate change adaptation in food systems. The research team will work with key stakeholders in Kenya, Uganda, and Tanzania to identify specific capacity needs to facilitate land and water management transitions. The two-year project will produce actionable recommendations to the LVBC. More