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    Surprisingly diverse innovations led to dramatically cheaper solar panels

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    The cost of solar panels has dropped by more than 99 percent since the 1970s, enabling widespread adoption of photovoltaic systems that convert sunlight into electricity.A new MIT study drills down on specific innovations that enabled such dramatic cost reductions, revealing that technical advances across a web of diverse research efforts and industries played a pivotal role.The findings could help renewable energy companies make more effective R&D investment decisions and aid policymakers in identifying areas to prioritize to spur growth in manufacturing and deployment.The researchers’ modeling approach shows that key innovations often originated outside the solar sector, including advances in semiconductor fabrication, metallurgy, glass manufacturing, oil and gas drilling, construction processes, and even legal domains.“Our results show just how intricate the process of cost improvement is, and how much scientific and engineering advances, often at a very basic level, are at the heart of these cost reductions. A lot of knowledge was drawn from different domains and industries, and this network of knowledge is what makes these technologies improve,” says study senior author Jessika Trancik, a professor in MIT’s Institute for Data, Systems, and Society.Trancik is joined on the paper by co-lead authors Goksin Kavlak, a former IDSS graduate student and postdoc who is now a senior energy associate at the Brattle Group; Magdalena Klemun, a former IDSS graduate student and postdoc who is now an assistant professor at Johns Hopkins University; former MIT postdoc Ajinkya Kamat; as well as Brittany Smith and Robert Margolis of the National Renewable Energy Laboratory. The research appears today in PLOS ONE.Identifying innovationsThis work builds on mathematical models that the researchers previously developed that tease out the effects of engineering technologies on the cost of photovoltaic (PV) modules and systems.In this study, the researchers aimed to dig even deeper into the scientific advances that drove those cost declines.They combined their quantitative cost model with a detailed, qualitative analysis of innovations that affected the costs of PV system materials, manufacturing steps, and deployment processes.“Our quantitative cost model guided the qualitative analysis, allowing us to look closely at innovations in areas that are hard to measure due to a lack of quantitative data,” Kavlak says.Building on earlier work identifying key cost drivers — such as the number of solar cells per module, wiring efficiency, and silicon wafer area — the researchers conducted a structured scan of the literature for innovations likely to affect these drivers. Next, they grouped these innovations to identify patterns, revealing clusters that reduced costs by improving materials or prefabricating components to streamline manufacturing and installation. Finally, the team tracked industry origins and timing for each innovation, and consulted domain experts to zero in on the most significant innovations.All told, they identified 81 unique innovations that affected PV system costs since 1970, from improvements in antireflective coated glass to the implementation of fully online permitting interfaces.“With innovations, you can always go to a deeper level, down to things like raw materials processing techniques, so it was challenging to know when to stop. Having that quantitative model to ground our qualitative analysis really helped,” Trancik says.They chose to separate PV module costs from so-called balance-of-system (BOS) costs, which cover things like mounting systems, inverters, and wiring.PV modules, which are wired together to form solar panels, are mass-produced and can be exported, while many BOS components are designed, built, and sold at the local level.“By examining innovations both at the BOS level and within the modules, we identify the different types of innovations that have emerged in these two parts of PV technology,” Kavlak says.BOS costs depend more on soft technologies, nonphysical elements such as permitting procedures, which have contributed significantly less to PV’s past cost improvement compared to hardware innovations.“Often, it comes down to delays. Time is money, and if you have delays on construction sites and unpredictable processes, that affects these balance-of-system costs,” Trancik says.Innovations such as automated permitting software, which flags code-compliant systems for fast-track approval, show promise. Though not yet quantified in this study, the team’s framework could support future analysis of their economic impact and similar innovations that streamline deployment processes.Interconnected industriesThe researchers found that innovations from the semiconductor, electronics, metallurgy, and petroleum industries played a major role in reducing both PV and BOS costs, but BOS costs were also impacted by innovations in software engineering and electric utilities.Noninnovation factors, like efficiency gains from bulk purchasing and the accumulation of knowledge in the solar power industry, also reduced some cost variables.In addition, while most PV panel innovations originated in research organizations or industry, many BOS innovations were developed by city governments, U.S. states, or professional associations.“I knew there was a lot going on with this technology, but the diversity of all these fields and how closely linked they are, and the fact that we can clearly see that network through this analysis, was interesting,” Trancik says.“PV was very well-positioned to absorb innovations from other industries — thanks to the right timing, physical compatibility, and supportive policies to adapt innovations for PV applications,” Klemun adds.The analysis also reveals the role greater computing power could play in reducing BOS costs through advances like automated engineering review systems and remote site assessment software.“In terms of knowledge spillovers, what we’ve seen so far in PV may really just be the beginning,” Klemun says, pointing to the expanding role of robotics and AI-driven digital tools in driving future cost reductions and quality improvements.In addition to their qualitative analysis, the researchers demonstrated how this methodology could be used to estimate the quantitative impact of a particular innovation if one has the numerical data to plug into the cost equation.For instance, using information about material prices and manufacturing procedures, they estimate that wire sawing, a technique which was introduced in the 1980s, led to an overall PV system cost decrease of $5 per watt by reducing silicon losses and increasing throughput during fabrication.“Through this retrospective analysis, you learn something valuable for future strategy because you can see what worked and what didn’t work, and the models can also be applied prospectively. It is also useful to know what adjacent sectors may help support improvement in a particular technology,” Trancik says.Moving forward, the researchers plan to apply this methodology to a wide range of technologies, including other renewable energy systems. They also want to further study soft technology to identify innovations or processes that could accelerate cost reductions.“Although the process of technological innovation may seem like a black box, we’ve shown that you can study it just like any other phenomena,” Trancik says.This research is funded, in part, by the U.S. Department of Energy Solar Energies Technology Office. More

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      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 Energy

      Confronting the AI/energy conundrum

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      The explosive growth of AI-powered computing centers is creating an unprecedented surge in electricity demand that threatens to overwhelm power grids and derail climate goals. At the same time, artificial intelligence technologies could revolutionize energy systems, accelerating the transition to clean power.“We’re at a cusp of potentially gigantic change throughout the economy,” said William H. Green, director of the MIT Energy Initiative (MITEI) and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, at MITEI’s Spring Symposium, “AI and energy: Peril and promise,” held on May 13. The event brought together experts from industry, academia, and government to explore solutions to what Green described as both “local problems with electric supply and meeting our clean energy targets” while seeking to “reap the benefits of AI without some of the harms.” The challenge of data center energy demand and potential benefits of AI to the energy transition is a research priority for MITEI.AI’s startling energy demandsFrom the start, the symposium highlighted sobering statistics about AI’s appetite for electricity. After decades of flat electricity demand in the United States, computing centers now consume approximately 4 percent of the nation’s electricity. Although there is great uncertainty, some projections suggest this demand could rise to 12-15 percent by 2030, largely driven by artificial intelligence applications.Vijay Gadepally, senior scientist at MIT’s Lincoln Laboratory, emphasized the scale of AI’s consumption. “The power required for sustaining some of these large models is doubling almost every three months,” he noted. “A single ChatGPT conversation uses as much electricity as charging your phone, and generating an image consumes about a bottle of water for cooling.”Facilities requiring 50 to 100 megawatts of power are emerging rapidly across the United States and globally, driven both by casual and institutional research needs relying on large language programs such as ChatGPT and Gemini. Gadepally cited congressional testimony by Sam Altman, CEO of OpenAI, highlighting how fundamental this relationship has become: “The cost of intelligence, the cost of AI, will converge to the cost of energy.”“The energy demands of AI are a significant challenge, but we also have an opportunity to harness these vast computational capabilities to contribute to climate change solutions,” said Evelyn Wang, MIT vice president for energy and climate and the former director at the Advanced Research Projects Agency-Energy (ARPA-E) at the U.S. Department of Energy.Wang also noted that innovations developed for AI and data centers — such as efficiency, cooling technologies, and clean-power solutions — could have broad applications beyond computing facilities themselves.Strategies for clean energy solutionsThe symposium explored multiple pathways to address the AI-energy challenge. Some panelists presented models suggesting that while artificial intelligence may increase emissions in the short term, its optimization capabilities could enable substantial emissions reductions after 2030 through more efficient power systems and accelerated clean technology development.Research shows regional variations in the cost of powering computing centers with clean electricity, according to Emre Gençer, co-founder and CEO of Sesame Sustainability and former MITEI principal research scientist. Gençer’s analysis revealed that the central United States offers considerably lower costs due to complementary solar and wind resources. However, achieving zero-emission power would require massive battery deployments — five to 10 times more than moderate carbon scenarios — driving costs two to three times higher.“If we want to do zero emissions with reliable power, we need technologies other than renewables and batteries, which will be too expensive,” Gençer said. He pointed to “long-duration storage technologies, small modular reactors, geothermal, or hybrid approaches” as necessary complements.Because of data center energy demand, there is renewed interest in nuclear power, noted Kathryn Biegel, manager of R&D and corporate strategy at Constellation Energy, adding that her company is restarting the reactor at the former Three Mile Island site, now called the “Crane Clean Energy Center,” to meet this demand. “The data center space has become a major, major priority for Constellation,” she said, emphasizing how their needs for both reliability and carbon-free electricity are reshaping the power industry.Can AI accelerate the energy transition?Artificial intelligence could dramatically improve power systems, according to Priya Donti, assistant professor and the Silverman Family Career Development Professor in MIT’s Department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision Systems. She showcased how AI can accelerate power grid optimization by embedding physics-based constraints into neural networks, potentially solving complex power flow problems at “10 times, or even greater, speed compared to your traditional models.”AI is already reducing carbon emissions, according to examples shared by Antonia Gawel, global director of sustainability and partnerships at Google. Google Maps’ fuel-efficient routing feature has “helped to prevent more than 2.9 million metric tons of GHG [greenhouse gas] emissions reductions since launch, which is the equivalent of taking 650,000 fuel-based cars off the road for a year,” she said. Another Google research project uses artificial intelligence to help pilots avoid creating contrails, which represent about 1 percent of global warming impact.AI’s potential to speed materials discovery for power applications was highlighted by Rafael Gómez-Bombarelli, the Paul M. Cook Career Development Associate Professor in the MIT Department of Materials Science and Engineering. “AI-supervised models can be trained to go from structure to property,” he noted, enabling the development of materials crucial for both computing and efficiency.Securing growth with sustainabilityThroughout the symposium, participants grappled with balancing rapid AI deployment against environmental impacts. While AI training receives most attention, Dustin Demetriou, senior technical staff member in sustainability and data center innovation at IBM, quoted a World Economic Forum article that suggested that “80 percent of the environmental footprint is estimated to be due to inferencing.” Demetriou emphasized the need for efficiency across all artificial intelligence applications.Jevons’ paradox, where “efficiency gains tend to increase overall resource consumption rather than decrease it” is another factor to consider, cautioned Emma Strubell, the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. Strubell advocated for viewing computing center electricity as a limited resource requiring thoughtful allocation across different applications.Several presenters discussed novel approaches for integrating renewable sources with existing grid infrastructure, including potential hybrid solutions that combine clean installations with existing natural gas plants that have valuable grid connections already in place. These approaches could provide substantial clean capacity across the United States at reasonable costs while minimizing reliability impacts.Navigating the AI-energy paradoxThe symposium highlighted MIT’s central role in developing solutions to the AI-electricity challenge.Green spoke of a new MITEI program on computing centers, power, and computation that will operate alongside the comprehensive spread of MIT Climate Project research. “We’re going to try to tackle a very complicated problem all the way from the power sources through the actual algorithms that deliver value to the customers — in a way that’s going to be acceptable to all the stakeholders and really meet all the needs,” Green said.Participants in the symposium were polled about priorities for MIT’s research by Randall Field, MITEI director of research. The real-time results ranked “data center and grid integration issues” as the top priority, followed by “AI for accelerated discovery of advanced materials for energy.”In addition, attendees revealed that most view AI’s potential regarding power as a “promise,” rather than a “peril,” although a considerable portion remain uncertain about the ultimate impact. When asked about priorities in power supply for computing facilities, half of the respondents selected carbon intensity as their top concern, with reliability and cost following. More

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      in Security

      How J-WAFS Solutions grants bring research to market

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      For the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), 2025 marks a decade of translating groundbreaking research into tangible solutions for global challenges. Few examples illustrate that mission better than NONA Technologies. With support from a J-WAFS Solutions grant, MIT electrical engineering and biological engineering Professor Jongyoon Han and his team developed a portable desalination device that transforms seawater into clean drinking water without filters or high-pressure pumps. The device stands apart from traditional systems because conventional desalination technologies, like reverse osmosis, are energy-intensive, prone to fouling, and typically deployed at large, centralized plants. In contrast, the device developed in Han’s lab employs ion concentration polarization technology to remove salts and particles from seawater, producing potable water that exceeds World Health Organization standards. It is compact, solar-powered, and operable at the push of a button — making it an ideal solution for off-grid and disaster-stricken areas.This research laid the foundation for spinning out NONA Technologies along with co-founders Junghyo Yoon PhD ’21 from Han’s lab and Bruce Crawford MBA ’22, to commercialize the technology and address pressing water-scarcity issues worldwide. “This is really the culmination of a 10-year journey that I and my group have been on,” said Han in an earlier MIT News article. “We worked for years on the physics behind individual desalination processes, but pushing all those advances into a box, building a system, and demonstrating it in the ocean … that was a really meaningful and rewarding experience for me.” You can watch this video showcasing the device in action.Moving breakthrough research out of the lab and into the world is a well-known challenge. While traditional “seed” grants typically support early-stage research at Technology Readiness Level (TRL) 1-2, few funding sources exist to help academic teams navigate to the next phase of technology development. The J-WAFS Solutions Program is strategically designed to address this critical gap by supporting technologies in the high-risk, early-commercialization phase that is often neglected by traditional research, corporate, and venture funding. By supporting technologies at TRLs 3-5, the program increases the likelihood that promising innovations will survive beyond the university setting, advancing sufficiently to attract follow-on funding.Equally important, the program gives academic researchers the time, resources, and flexibility to de-risk their technology, explore customer need and potential real-world applications, and determine whether and how they want to pursue commercialization. For faculty-led teams like Han’s, the J-WAFS Solutions Program provided the critical financial runway and entrepreneurial guidance needed to refine the technology, test assumptions about market fit, and lay the foundation for a startup team. While still in the MIT innovation ecosystem, Nona secured over $200,000 in non-dilutive funding through competitions and accelerators, including the prestigious MIT delta v Educational Accelerator. These early wins laid the groundwork for further investment and technical advancement.Since spinning out of MIT, NONA has made major strides in both technology development and business viability. What started as a device capable of producing just over half-a-liter of clean drinking water per hour has evolved into a system that now delivers 10 times that capacity, at 5 liters per hour. The company successfully raised a $3.5 million seed round to advance its portable desalination device, and entered into a collaboration with the U.S. Army Natick Soldier Systems Center, where it co-developed early prototypes and began generating revenue while validating the technology. Most recently, NONA was awarded two SBIR Phase I grants totaling $575,000, one from the National Science Foundation and another from the National Institute of Environmental Health Sciences.Now operating out of Greentown Labs in Somerville, Massachusetts, NONA has grown to a dedicated team of five and is preparing to launch its nona5 product later this year, with a wait list of over 1,000 customers. It is also kicking off its first industrial pilot, marking a key step toward commercial scale-up. “Starting a business as a postdoc was challenging, especially with limited funding and industry knowledge,” says Yoon, who currently serves as CTO of NONA. “J-WAFS gave me the financial freedom to pursue my venture, and the mentorship pushed me to hit key milestones. Thanks to J-WAFS, I successfully transitioned from an academic researcher to an entrepreneur in the water industry.”NONA is one of several J-WAFS-funded technologies that have moved from the lab to market, part of a growing portfolio of water and food solutions advancing through MIT’s innovation pipeline. As J-WAFS marks a decade of catalyzing innovation in water and food, NONA exemplifies what is possible when mission-driven research is paired with targeted early-stage support and mentorship.To learn more or get involved in supporting startups through the J-WAFS Solutions Program, please contact jwafs@mit.edu. More

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      in Energy

      The MIT-Portugal Program enters Phase 4

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      Since its founding 19 years ago as a pioneering collaboration with Portuguese universities, research institutions and corporations, the MIT-Portugal Program (MPP) has achieved a slew of successes — from enabling 47 entrepreneurial spinoffs and funding over 220 joint projects between MIT and Portuguese researchers to training a generation of exceptional researchers on both sides of the Atlantic.In March, with nearly two decades of collaboration under their belts, MIT and the Portuguese Science and Technology Foundation (FCT) signed an agreement that officially launches the program’s next chapter. Running through 2030, MPP’s Phase 4 will support continued exploration of innovative ideas and solutions in fields ranging from artificial intelligence and nanotechnology to climate change — both on the MIT campus and with partners throughout Portugal.  “One of the advantages of having a program that has gone on so long is that we are pretty well familiar with each other at this point. Over the years, we’ve learned each other’s systems, strengths and weaknesses and we’ve been able to create a synergy that would not have existed if we worked together for a short period of time,” says Douglas Hart, MIT mechanical engineering professor and MPP co-director.Hart and John Hansman, the T. Wilson Professor of Aeronautics and Astronautics at MIT and MPP co-director, are eager to take the program’s existing research projects further, while adding new areas of focus identified by MIT and FCT. Known as the Fundação para a Ciência e Tecnologia in Portugal, FCT is the national public agency supporting research in science, technology and innovation under Portugal’s Ministry of Education, Science and Innovation.“Over the past two decades, the partnership with MIT has built a foundation of trust that has fostered collaboration among researchers and the development of projects with significant scientific impact and contributions to the Portuguese economy,” Fernando Alexandre, Portugal’s minister for education, science, and innovation, says. “In this new phase of the partnership, running from 2025 to 2030, we expect even greater ambition and impact — raising Portuguese science and its capacity to transform the economy and improve our society to even higher levels, while helping to address the challenges we face in areas such as climate change and the oceans, digitalization, and space.”“International collaborations like the MIT-Portugal Program are absolutely vital to MIT’s mission of research, education and service. I’m thrilled to see the program move into its next phase,” says MIT President Sally Kornbluth. “MPP offers our faculty and students opportunities to work in unique research environments where they not only make new findings and learn new methods but also contribute to solving urgent local and global problems. MPP’s work in the realm of ocean science and climate is a prime example of how international partnerships like this can help solve important human problems.”Sharing MIT’s commitment to academic independence and excellence, Kornbluth adds, “the institutions and researchers we partner with through MPP enhance MIT’s ability to achieve its mission, enabling us to pursue the exacting standards of intellectual and creative distinction that make MIT a cradle of innovation and world leader in scientific discovery.”The epitome of an effective international collaboration, MPP has stayed true to its mission and continued to deliver results here in the U.S. and in Portugal for nearly two decades — prevailing amid myriad shifts in the political, social, and economic landscape. The multifaceted program encompasses an annual research conference and educational summits such as an Innovation Workshop at MIT each June and a Marine Robotics Summer School in the Azores in July, as well as student and faculty exchanges that facilitate collaborative research. During the third phase of the program alone, 59 MIT students and 53 faculty and researchers visited Portugal, and MIT hosted 131 students and 49 faculty and researchers from Portuguese universities and other institutions.In each roughly five-year phase, MPP researchers focus on a handful of core research areas. For Phase 3, MPP advanced cutting-edge research in four strategic areas: climate science and climate change; Earth systems: oceans to near space; digital transformation in manufacturing; and sustainable cities. Within these broad areas, MIT and FCT researchers worked together on numerous small-scale projects and several large “flagship” ones, including development of Portugal’s CubeSat satellite, a collaboration between MPP and several Portuguese universities and companies that marked the country’s second satellite launch and the first in 30 years.While work in the Phase 3 fields will continue during Phase 4, researchers will also turn their attention to four more areas: chips/nanotechnology, energy (a previous focus in Phase 2), artificial intelligence, and space.“We are opening up the aperture for additional collaboration areas,” Hansman says.In addition to focusing on distinct subject areas, each phase has emphasized the various parts of MPP’s mission to differing degrees. While Phase 3 accentuated collaborative research more than educational exchanges and entrepreneurship, those two aspects will be given more weight under the Phase 4 agreement, Hart said.“We have approval in Phase 4 to bring a number of Portuguese students over, and our principal investigators will benefit from close collaborations with Portuguese researchers,” he says.The longevity of MPP and the recent launch of Phase 4 are evidence of the program’s value. The program has played a role in the educational, technological and economic progress Portugal has achieved over the past two decades, as well.  “The Portugal of today is remarkably stronger than the Portugal of 20 years ago, and many of the places where they are stronger have been impacted by the program,” says Hansman, pointing to sustainable cities and “green” energy, in particular. “We can’t take direct credit, but we’ve been part of Portugal’s journey forward.”Since MPP began, Hart adds, “Portugal has become much more entrepreneurial. Many, many, many more start-up companies are coming out of Portuguese universities than there used to be.”  A recent analysis of MPP and FCT’s other U.S. collaborations highlighted a number of positive outcomes. The report noted that collaborations with MIT and other US universities have enhanced Portuguese research capacities and promoted organizational upgrades in the national R&D ecosystem, while providing Portuguese universities and companies with opportunities to engage in complex projects that would have been difficult to undertake on their own.Regarding MIT in particular, the report found that MPP’s long-term collaboration has spawned the establishment of sustained doctoral programs and pointed to a marked shift within Portugal’s educational ecosystem toward globally aligned standards. MPP, it reported, has facilitated the education of 198 Portuguese PhDs.Portugal’s universities, students and companies are not alone in benefitting from the research, networks, and economic activity MPP has spawned. MPP also delivers unique value to MIT, as well as to the broader US science and research community. Among the program’s consistent themes over the years, for example, is “joint interest in the Atlantic,” Hansman says.This summer, Faial Island in the Azores will host MPP’s fifth annual Marine Robotics Summer School, a two-week course open to 12 Portuguese Master’s and first year PhD students and 12 MIT upper-level undergraduates and graduate students. The course, which includes lectures by MIT and Portuguese faculty and other researchers, workshops, labs and hands-on experiences, “is always my favorite,” said Hart.“I get to work with some of the best researchers in the world there, and some of the top students coming out of Woods Hole Oceanographic Institution, MIT, and Portugal,” he says, adding that some of his previous Marine Robotics Summer School students have come to study at MIT and then gone on to become professors in ocean science.“So, it’s been exciting to see the growth of students coming out of that program, certainly a positive impact,” Hart says.MPP provides one-of-a-kind opportunities for ocean research due to the unique marine facilities available in Portugal, including not only open ocean off the Azores but also Lisbon’s deep-water port and a Portuguese Naval facility just south of Lisbon that is available for collaborative research by international scientists. Like MIT, Portuguese universities are also strongly invested in climate change research — a field of study keenly related to ocean systems.“The international collaboration has allowed us to test and further develop our research prototypes in different aquaculture environments both in the US and in Portugal, while building on the unique expertise of our Portuguese faculty collaborator Dr. Ricardo Calado from the University of Aveiro and our industry collaborators,” says Stefanie Mueller, the TIBCO Career Development Associate Professor in MIT’s departments of Electrical Engineering and Computer Science and Mechanical Engineering and leader of the Human-Computer Interaction Group at the MIT Computer Science and Artificial Intelligence Lab.Mueller points to the work of MIT mechanical engineering PhD student Charlene Xia, a Marine Robotics Summer School participant, whose research is aimed at developing an economical system to monitor the microbiome of seaweed farms and halt the spread of harmful bacteria associated with ocean warming. In addition to participating in the summer school as a student, Xia returned to the Azores for two subsequent years as a teaching assistant.“The MIT-Portugal Program has been a key enabler of our research on monitoring the aquatic microbiome for potential disease outbreaks,” Mueller says.As MPP enters its next phase, Hart and Hansman are optimistic about the program’s continuing success on both sides of the Atlantic and envision broadening its impact going forward.“I think, at this point, the research is going really well, and we’ve got a lot of connections. I think one of our goals is to expand not the science of the program necessarily, but the groups involved,” Hart says, noting that MPP could have a bigger presence in technical fields such as AI and micro-nano manufacturing, as well as in social sciences and humanities.“We’d like to involve many more people and new people here at MIT, as well as in Portugal,” he says, “so that we can reach a larger slice of the population.”  More

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      in Energy

      Chip-based system for terahertz waves could enable more efficient, sensitive electronics

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      The use of terahertz waves, which have shorter wavelengths and higher frequencies than radio waves, could enable faster data transmission, more precise medical imaging, and higher-resolution radar.But effectively generating terahertz waves using a semiconductor chip, which is essential for incorporation into electronic devices, is notoriously difficult.Many current techniques can’t generate waves with enough radiating power for useful applications unless they utilize bulky and expensive silicon lenses. Higher radiating power allows terahertz signals to travel farther. Such lenses, which are often larger than the chip itself, make it hard to integrate the terahertz source into an electronic device.To overcome these limitations, MIT researchers developed a terahertz amplifier-multiplier system that achieves higher radiating power than existing devices without the need for silicon lenses.By affixing a thin, patterned sheet of material to the back of the chip and utilizing higher-power Intel transistors, the researchers produced a more efficient, yet scalable, chip-based terahertz wave generator.This compact chip could be used to make terahertz arrays for applications like improved security scanners for detecting hidden objects or environmental monitors for pinpointing airborne pollutants.“To take full advantage of a terahertz wave source, we need it to be scalable. A terahertz array might have hundreds of chips, and there is no place to put silicon lenses because the chips are combined with such high density. We need a different package, and here we’ve demonstrated a promising approach that can be used for scalable, low-cost terahertz arrays,” says Jinchen Wang, a graduate student in the Department of Electrical Engineering and Computer Science (EECS) and lead author of a paper on the terahertz radiator.He is joined on the paper by EECS graduate students Daniel Sheen and Xibi Chen; Steven F. Nagel, managing director of the T.J. Rodgers RLE Laboratory; and senior author Ruonan Han, an associate professor in EECS, who leads the Terahertz Integrated Electronics Group. The research will be presented at the IEEE International Solid-States Circuits Conference.Making wavesTerahertz waves sit on the electromagnetic spectrum between radio waves and infrared light. Their higher frequencies enable them to carry more information per second than radio waves, while they can safely penetrate a wider range of materials than infrared light.One way to generate terahertz waves is with a CMOS chip-based amplifier-multiplier chain that increases the frequency of radio waves until they reach the terahertz range. To achieve the best performance, waves go through the silicon chip and are eventually emitted out the back into the open air.But a property known as the dielectric constant gets in the way of a smooth transmission.The dielectric constant influences how electromagnetic waves interact with a material. It affects the amount of radiation that is absorbed, reflected, or transmitted. Because the dielectric constant of silicon is much higher than that of air, most terahertz waves are reflected at the silicon-air boundary rather than being cleanly transmitted out the back.Since most signal strength is lost at this boundary, current approaches often use silicon lenses to boost the power of the remaining signal. The MIT researchers approached this problem differently.They drew on an electromechanical theory known as matching. With matching, they seek to equal out the dielectric constants of silicon and air, which will minimize the amount of signal that is reflected at the boundary.They accomplish this by sticking a thin sheet of material which has a dielectric constant between silicon and air to the back of the chip. With this matching sheet in place, most waves will be transmitted out the back rather than being reflected.A scalable approachThey chose a low-cost, commercially available substrate material with a dielectric constant very close to what they needed for matching. To improve performance, they used a laser cutter to punch tiny holes into the sheet until its dielectric constant was exactly right.“Since the dielectric constant of air is 1, if you just cut some subwavelength holes in the sheet, it is equivalent to injecting some air, which lowers the overall dielectric constant of the matching sheet,” Wang explains.In addition, they designed their chip with special transistors developed by Intel that have a higher maximum frequency and breakdown voltage than traditional CMOS transistors.“These two things taken together, the more powerful transistors and the dielectric sheet, plus a few other small innovations, enabled us to outperform several other devices,” he says.Their chip generated terahertz signals with a peak radiation power of 11.1 decibel-milliwatts, the best among state-of-the-art techniques. Moreover, since the low-cost chip can be fabricated at scale, it could be integrated into real-world electronic devices more readily.One of the biggest challenges of developing a scalable chip was determining how to manage the power and temperature when generating terahertz waves.“Because the frequency and the power are so high, many of the standard ways to design a CMOS chip are not applicable here,” Wang says.The researchers also needed to devise a technique for installing the matching sheet that could be scaled up in a manufacturing facility.Moving forward, they want to demonstrate this scalability by fabricating a phased array of CMOS terahertz sources, enabling them to steer and focus a powerful terahertz beam with a low-cost, compact device.This research is supported, in part, by NASA’s Jet Propulsion Laboratory and Strategic University Research Partnerships Program, as well as the MIT Center for Integrated Circuits and Systems. The chip was fabricated through the Intel University Shuttle Program. More

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      in Environment

      J-WAFS: Supporting food and water research across MIT

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      MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) has transformed the landscape of water and food research at MIT, driving faculty engagement and catalyzing new research and innovation in these critical areas. With philanthropic, corporate, and government support, J-WAFS’ strategic approach spans the entire research life cycle, from support for early-stage research to commercialization grants for more advanced projects.Over the past decade, J-WAFS has invested approximately $25 million in direct research funding to support MIT faculty pursuing transformative research with the potential for significant impact. “Since awarding our first cohort of seed grants in 2015, it’s remarkable to look back and see that over 10 percent of the MIT faculty have benefited from J-WAFS funding,” observes J-WAFS Executive Director Renee J. Robins ’83. “Many of these professors hadn’t worked on water or food challenges before their first J-WAFS grant.” By fostering interdisciplinary collaborations and supporting high-risk, high-reward projects, J-WAFS has amplified the capacity of MIT faculty to pursue groundbreaking research that addresses some of the world’s most pressing challenges facing our water and food systems.Drawing MIT faculty to water and food researchJ-WAFS open calls for proposals enable faculty to explore bold ideas and develop impactful approaches to tackling critical water and food system challenges. Professor Patrick Doyle’s work in water purification exemplifies this impact. “Without J-WAFS, I would have never ventured into the field of water purification,” Doyle reflects. While previously focused on pharmaceutical manufacturing and drug delivery, exposure to J-WAFS-funded peers led him to apply his expertise in soft materials to water purification. “Both the funding and the J-WAFS community led me to be deeply engaged in understanding some of the key challenges in water purification and water security,” he explains.Similarly, Professor Otto Cordero of the Department of Civil and Environmental Engineering (CEE) leveraged J-WAFS funding to pivot his research into aquaculture. Cordero explains that his first J-WAFS seed grant “has been extremely influential for my lab because it allowed me to take a step in a new direction, with no preliminary data in hand.” Cordero’s expertise is in microbial communities. He was previous unfamiliar with aquaculture, but he saw the relevance of microbial communities the health of farmed aquatic organisms.Supporting early-career facultyNew assistant professors at MIT have particularly benefited from J-WAFS funding and support. J-WAFS has played a transformative role in shaping the careers and research trajectories of many new faculty members by encouraging them to explore novel research areas, and in many instances providing their first MIT research grant.Professor Ariel Furst reflects on how pivotal J-WAFS’ investment has been in advancing her research. “This was one of the first grants I received after starting at MIT, and it has truly shaped the development of my group’s research program,” Furst explains. With J-WAFS’ backing, her lab has achieved breakthroughs in chemical detection and remediation technologies for water. “The support of J-WAFS has enabled us to develop the platform funded through this work beyond the initial applications to the general detection of environmental contaminants and degradation of those contaminants,” she elaborates. Karthish Manthiram, now a professor of chemical engineering and chemistry at Caltech, explains how J-WAFS’ early investment enabled him and other young faculty to pursue ambitious ideas. “J-WAFS took a big risk on us,” Manthiram reflects. His research on breaking the nitrogen triple bond to make ammonia for fertilizer was initially met with skepticism. However, J-WAFS’ seed funding allowed his lab to lay the groundwork for breakthroughs that later attracted significant National Science Foundation (NSF) support. “That early funding from J-WAFS has been pivotal to our long-term success,” he notes. These stories underscore the broad impact of J-WAFS’ support for early-career faculty, and its commitment to empowering them to address critical global challenges and innovate boldly.Fueling follow-on funding J-WAFS seed grants enable faculty to explore nascent research areas, but external funding for continued work is usually necessary to achieve the full potential of these novel ideas. “It’s often hard to get funding for early stage or out-of-the-box ideas,” notes J-WAFS Director Professor John H. Lienhard V. “My hope, when I founded J-WAFS in 2014, was that seed grants would allow PIs [principal investigators] to prove out novel ideas so that they would be attractive for follow-on funding. And after 10 years, J-WAFS-funded research projects have brought more than $21 million in subsequent awards to MIT.”Professor Retsef Levi led a seed study on how agricultural supply chains affect food safety, with a team of faculty spanning the MIT schools Engineering and Science as well as the MIT Sloan School of Management. The team parlayed their seed grant research into a multi-million-dollar follow-on initiative. Levi reflects, “The J-WAFS seed funding allowed us to establish the initial credibility of our team, which was key to our success in obtaining large funding from several other agencies.”Dave Des Marais was an assistant professor in the Department of CEE when he received his first J-WAFS seed grant. The funding supported his research on how plant growth and physiology are controlled by genes and interact with the environment. The seed grant helped launch his lab’s work addressing enhancing climate change resilience in agricultural systems. The work led to his Faculty Early Career Development (CAREER) Award from the NSF, a prestigious honor for junior faculty members. Now an associate professor, Des Marais’ ongoing project to further investigate the mechanisms and consequences of genomic and environmental interactions is supported by the five-year, $1,490,000 NSF grant. “J-WAFS providing essential funding to get my new research underway,” comments Des Marais.Stimulating interdisciplinary collaborationDes Marais’ seed grant was also key to developing new collaborations. He explains, “the J-WAFS grant supported me to develop a collaboration with Professor Caroline Uhler in EECS/IDSS [the Department of Electrical Engineering and Computer Science/Institute for Data, Systems, and Society] that really shaped how I think about framing and testing hypotheses. One of the best things about J-WAFS is facilitating unexpected connections among MIT faculty with diverse yet complementary skill sets.”Professors A. John Hart of the Department of Mechanical Engineering and Benedetto Marelli of CEE also launched a new interdisciplinary collaboration with J-WAFS funding. They partnered to join expertise in biomaterials, microfabrication, and manufacturing, to create printed silk-based colorimetric sensors that detect food spoilage. “The J-WAFS Seed Grant provided a unique opportunity for multidisciplinary collaboration,” Hart notes.Professors Stephen Graves in the MIT Sloan School of Management and Bishwapriya Sanyal in the Department of Urban Studies and Planning (DUSP) partnered to pursue new research on agricultural supply chains. With field work in Senegal, their J-WAFS-supported project brought together international development specialists and operations management experts to study how small firms and government agencies influence access to and uptake of irrigation technology by poorer farmers. “We used J-WAFS to spur a collaboration that would have been improbable without this grant,” they explain. Being part of the J-WAFS community also introduced them to researchers in Professor Amos Winter’s lab in the Department of Mechanical Engineering working on irrigation technologies for low-resource settings. DUSP doctoral candidate Mark Brennan notes, “We got to share our understanding of how irrigation markets and irrigation supply chains work in developing economies, and then we got to contrast that with their understanding of how irrigation system models work.”Timothy Swager, professor of chemistry, and Rohit Karnik, professor of mechanical engineering and J-WAFS associate director, collaborated on a sponsored research project supported by Xylem, Inc. through the J-WAFS Research Affiliate program. The cross-disciplinary research, which targeted the development of ultra-sensitive sensors for toxic PFAS chemicals, was conceived following a series of workshops hosted by J-WAFS. Swager and Karnik were two of the participants, and their involvement led to the collaborative proposal that Xylem funded. “J-WAFS funding allowed us to combine Swager lab’s expertise in sensing with my lab’s expertise in microfluidics to develop a cartridge for field-portable detection of PFAS,” says Karnik. “J-WAFS has enriched my research program in so many ways,” adds Swager, who is now working to commercialize the technology.Driving global collaboration and impactJ-WAFS has also helped MIT faculty establish and advance international collaboration and impactful global research. By funding and supporting projects that connect MIT researchers with international partners, J-WAFS has not only advanced technological solutions, but also strengthened cross-cultural understanding and engagement.Professor Matthew Shoulders leads the inaugural J-WAFS Grand Challenge project. In response to the first J-WAFS call for “Grand Challenge” proposals, Shoulders assembled an interdisciplinary team based at MIT to enhance and provide climate resilience to agriculture by improving the most inefficient aspect of photosynthesis, the notoriously-inefficient carbon dioxide-fixing plant enzyme RuBisCO. J-WAFS funded this high-risk/high-reward project following a competitive process that engaged external reviewers through a several rounds of iterative proposal development. The technical feedback to the team led them to researchers with complementary expertise from the Australian National University. “Our collaborative team of biochemists and synthetic biologists, computational biologists, and chemists is deeply integrated with plant biologists and field trial experts, yielding a robust feedback loop for enzyme engineering,” Shoulders says. “Together, this team will be able to make a concerted effort using the most modern, state-of-the-art techniques to engineer crop RuBisCO with an eye to helping make meaningful gains in securing a stable crop supply, hopefully with accompanying improvements in both food and water security.”Professor Leon Glicksman and Research Engineer Eric Verploegen’s team designed a low-cost cooling chamber to preserve fruits and vegetables harvested by smallholder farmers with no access to cold chain storage. J-WAFS’ guidance motivated the team to prioritize practical considerations informed by local collaborators, ensuring market competitiveness. “As our new idea for a forced-air evaporative cooling chamber was taking shape, we continually checked that our solution was evolving in a direction that would be competitive in terms of cost, performance, and usability to existing commercial alternatives,” explains Verploegen. Following the team’s initial seed grant, the team secured a J-WAFS Solutions commercialization grant, which Verploegen say “further motivated us to establish partnerships with local organizations capable of commercializing the technology earlier in the project than we might have done otherwise.” The team has since shared an open-source design as part of its commercialization strategy to maximize accessibility and impact.Bringing corporate sponsored research opportunities to MIT facultyJ-WAFS also plays a role in driving private partnerships, enabling collaborations that bridge industry and academia. Through its Research Affiliate Program, for example, J-WAFS provides opportunities for faculty to collaborate with industry on sponsored research, helping to convert scientific discoveries into licensable intellectual property (IP) that companies can turn into commercial products and services.J-WAFS introduced professor of mechanical engineering Alex Slocum to a challenge presented by its research affiliate company, Xylem: how to design a more energy-efficient pump for fluctuating flows. With centrifugal pumps consuming an estimated 6 percent of U.S. electricity annually, Slocum and his then-graduate student Hilary Johnson SM ’18, PhD ’22 developed an innovative variable volute mechanism that reduces energy usage. “Xylem envisions this as the first in a new category of adaptive pump geometry,” comments Johnson. The research produced a pump prototype and related IP that Xylem is working on commercializing. Johnson notes that these outcomes “would not have been possible without J-WAFS support and facilitation of the Xylem industry partnership.” Slocum adds, “J-WAFS enabled Hilary to begin her work on pumps, and Xylem sponsored the research to bring her to this point … where she has an opportunity to do far more than the original project called for.”Swager speaks highly of the impact of corporate research sponsorship through J-WAFS on his research and technology translation efforts. His PFAS project with Karnik described above was also supported by Xylem. “Xylem was an excellent sponsor of our research. Their engagement and feedback were instrumental in advancing our PFAS detection technology, now on the path to commercialization,” Swager says.Looking forwardWhat J-WAFS has accomplished is more than a collection of research projects; a decade of impact demonstrates how J-WAFS’ approach has been transformative for many MIT faculty members. As Professor Mathias Kolle puts it, his engagement with J-WAFS “had a significant influence on how we think about our research and its broader impacts.” He adds that it “opened my eyes to the challenges in the field of water and food systems and the many different creative ideas that are explored by MIT.” This thriving ecosystem of innovation, collaboration, and academic growth around water and food research has not only helped faculty build interdisciplinary and international partnerships, but has also led to the commercialization of transformative technologies with real-world applications. C. Cem Taşan, the POSCO Associate Professor of Metallurgy who is leading a J-WAFS Solutions commercialization team that is about to launch a startup company, sums it up by noting, “Without J-WAFS, we wouldn’t be here at all.”  As J-WAFS looks to the future, its continued commitment — supported by the generosity of its donors and partners — builds on a decade of success enabling MIT faculty to advance water and food research that addresses some of the world’s most pressing challenges. 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

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      in Energy

      Explained: Generative AI’s environmental impact

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      In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI “gold rush” remain difficult to pin down, let alone mitigate.The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed.Beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems. The increasing number of generative AI applications has also spurred demand for high-performance computing hardware, adding indirect environmental impacts from its manufacture and transport.“When we think about the environmental impact of generative AI, it is not just the electricity you consume when you plug the computer in. There are much broader consequences that go out to a system level and persist based on actions that we take,” says Elsa A. Olivetti, professor in the Department of Materials Science and Engineering and the lead of the Decarbonization Mission of MIT’s new Climate Project.Olivetti is senior author of a 2024 paper, “The Climate and Sustainability Implications of Generative AI,” co-authored by MIT colleagues in response to an Institute-wide call for papers that explore the transformative potential of generative AI, in both positive and negative directions for society.Demanding data centersThe electricity demands of data centers are one major factor contributing to the environmental impacts of generative AI, since data centers are used to train and run the deep learning models behind popular tools like ChatGPT and DALL-E.A data center is a temperature-controlled building that houses computing infrastructure, such as servers, data storage drives, and network equipment. For instance, Amazon has more than 100 data centers worldwide, each of which has about 50,000 servers that the company uses to support cloud computing services.While data centers have been around since the 1940s (the first was built at the University of Pennsylvania in 1945 to support the first general-purpose digital computer, the ENIAC), the rise of generative AI has dramatically increased the pace of data center construction.“What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload,” says Noman Bashir, lead author of the impact paper, who is a Computing and Climate Impact Fellow at MIT Climate and Sustainability Consortium (MCSC) and a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL).Scientists have estimated that the power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, partly driven by the demands of generative AI. Globally, the electricity consumption of data centers rose to 460 terawatts in 2022. This would have made data centers the 11th largest electricity consumer in the world, between the nations of Saudi Arabia (371 terawatts) and France (463 terawatts), according to the Organization for Economic Co-operation and Development.By 2026, the electricity consumption of data centers is expected to approach 1,050 terawatts (which would bump data centers up to fifth place on the global list, between Japan and Russia).While not all data center computation involves generative AI, the technology has been a major driver of increasing energy demands.“The demand for new data centers cannot be met in a sustainable way. The pace at which companies are building new data centers means the bulk of the electricity to power them must come from fossil fuel-based power plants,” says Bashir.The power needed to train and deploy a model like OpenAI’s GPT-3 is difficult to ascertain. In a 2021 research paper, scientists from Google and the University of California at Berkeley estimated the training process alone consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide.While all machine-learning models must be trained, one issue unique to generative AI is the rapid fluctuations in energy use that occur over different phases of the training process, Bashir explains.Power grid operators must have a way to absorb those fluctuations to protect the grid, and they usually employ diesel-based generators for that task.Increasing impacts from inferenceOnce a generative AI model is trained, the energy demands don’t disappear.Each time a model is used, perhaps by an individual asking ChatGPT to summarize an email, the computing hardware that performs those operations consumes energy. Researchers have estimated that a ChatGPT query consumes about five times more electricity than a simple web search.“But an everyday user doesn’t think too much about that,” says Bashir. “The ease-of-use of generative AI interfaces and the lack of information about the environmental impacts of my actions means that, as a user, I don’t have much incentive to cut back on my use of generative AI.”With traditional AI, the energy usage is split fairly evenly between data processing, model training, and inference, which is the process of using a trained model to make predictions on new data. However, Bashir expects the electricity demands of generative AI inference to eventually dominate since these models are becoming ubiquitous in so many applications, and the electricity needed for inference will increase as future versions of the models become larger and more complex.Plus, generative AI models have an especially short shelf-life, driven by rising demand for new AI applications. Companies release new models every few weeks, so the energy used to train prior versions goes to waste, Bashir adds. New models often consume more energy for training, since they usually have more parameters than their predecessors.While electricity demands of data centers may be getting the most attention in research literature, the amount of water consumed by these facilities has environmental impacts, as well.Chilled water is used to cool a data center by absorbing heat from computing equipment. It has been estimated that, for each kilowatt hour of energy a data center consumes, it would need two liters of water for cooling, says Bashir.“Just because this is called ‘cloud computing’ doesn’t mean the hardware lives in the cloud. Data centers are present in our physical world, and because of their water usage they have direct and indirect implications for biodiversity,” he says.The computing hardware inside data centers brings its own, less direct environmental impacts.While it is difficult to estimate how much power is needed to manufacture a GPU, a type of powerful processor that can handle intensive generative AI workloads, it would be more than what is needed to produce a simpler CPU because the fabrication process is more complex. A GPU’s carbon footprint is compounded by the emissions related to material and product transport.There are also environmental implications of obtaining the raw materials used to fabricate GPUs, which can involve dirty mining procedures and the use of toxic chemicals for processing.Market research firm TechInsights estimates that the three major producers (NVIDIA, AMD, and Intel) shipped 3.85 million GPUs to data centers in 2023, up from about 2.67 million in 2022. That number is expected to have increased by an even greater percentage in 2024.The industry is on an unsustainable path, but there are ways to encourage responsible development of generative AI that supports environmental objectives, Bashir says.He, Olivetti, and their MIT colleagues argue that this will require a comprehensive consideration of all the environmental and societal costs of generative AI, as well as a detailed assessment of the value in its perceived benefits.“We need a more contextual way of systematically and comprehensively understanding the implications of new developments in this space. Due to the speed at which there have been improvements, we haven’t had a chance to catch up with our abilities to measure and understand the tradeoffs,” Olivetti says. More