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      in Environment

      MIT-derived algorithm helps forecast the frequency of extreme weather

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      To assess a community’s risk of extreme weather, policymakers rely first on global climate models that can be run decades, and even centuries, forward in time, but only at a coarse resolution. These models might be used to gauge, for instance, future climate conditions for the northeastern U.S., but not specifically for Boston.

      To estimate Boston’s future risk of extreme weather such as flooding, policymakers can combine a coarse model’s large-scale predictions with a finer-resolution model, tuned to estimate how often Boston is likely to experience damaging floods as the climate warms. But this risk analysis is only as accurate as the predictions from that first, coarser climate model.

      “If you get those wrong for large-scale environments, then you miss everything in terms of what extreme events will look like at smaller scales, such as over individual cities,” says Themistoklis Sapsis, the William I. Koch Professor and director of the Center for Ocean Engineering in MIT’s Department of Mechanical Engineering.

      Sapsis and his colleagues have now developed a method to “correct” the predictions from coarse climate models. By combining machine learning with dynamical systems theory, the team’s approach “nudges” a climate model’s simulations into more realistic patterns over large scales. When paired with smaller-scale models to predict specific weather events such as tropical cyclones or floods, the team’s approach produced more accurate predictions for how often specific locations will experience those events over the next few decades, compared to predictions made without the correction scheme.

      Play video

      This animation shows the evolution of storms around the northern hemisphere, as a result of a high-resolution storm model, combined with the MIT team’s corrected global climate model. The simulation improves the modeling of extreme values for wind, temperature, and humidity, which typically have significant errors in coarse scale models. Credit: Courtesy of Ruby Leung and Shixuan Zhang, PNNL

      Sapsis says the new correction scheme is general in form and can be applied to any global climate model. Once corrected, the models can help to determine where and how often extreme weather will strike as global temperatures rise over the coming years. 

      “Climate change will have an effect on every aspect of human life, and every type of life on the planet, from biodiversity to food security to the economy,” Sapsis says. “If we have capabilities to know accurately how extreme weather will change, especially over specific locations, it can make a lot of difference in terms of preparation and doing the right engineering to come up with solutions. This is the method that can open the way to do that.”

      The team’s results appear today in the Journal of Advances in Modeling Earth Systems. The study’s MIT co-authors include postdoc Benedikt Barthel Sorensen and Alexis-Tzianni Charalampopoulos SM ’19, PhD ’23, with Shixuan Zhang, Bryce Harrop, and Ruby Leung of the Pacific Northwest National Laboratory in Washington state.

      Over the hood

      Today’s large-scale climate models simulate weather features such as the average temperature, humidity, and precipitation around the world, on a grid-by-grid basis. Running simulations of these models takes enormous computing power, and in order to simulate how weather features will interact and evolve over periods of decades or longer, models average out features every 100 kilometers or so.

      “It’s a very heavy computation requiring supercomputers,” Sapsis notes. “But these models still do not resolve very important processes like clouds or storms, which occur over smaller scales of a kilometer or less.”

      To improve the resolution of these coarse climate models, scientists typically have gone under the hood to try and fix a model’s underlying dynamical equations, which describe how phenomena in the atmosphere and oceans should physically interact.

      “People have tried to dissect into climate model codes that have been developed over the last 20 to 30 years, which is a nightmare, because you can lose a lot of stability in your simulation,” Sapsis explains. “What we’re doing is a completely different approach, in that we’re not trying to correct the equations but instead correct the model’s output.”

      The team’s new approach takes a model’s output, or simulation, and overlays an algorithm that nudges the simulation toward something that more closely represents real-world conditions. The algorithm is based on a machine-learning scheme that takes in data, such as past information for temperature and humidity around the world, and learns associations within the data that represent fundamental dynamics among weather features. The algorithm then uses these learned associations to correct a model’s predictions.

      “What we’re doing is trying to correct dynamics, as in how an extreme weather feature, such as the windspeeds during a Hurricane Sandy event, will look like in the coarse model, versus in reality,” Sapsis says. “The method learns dynamics, and dynamics are universal. Having the correct dynamics eventually leads to correct statistics, for example, frequency of rare extreme events.”

      Climate correction

      As a first test of their new approach, the team used the machine-learning scheme to correct simulations produced by the Energy Exascale Earth System Model (E3SM), a climate model run by the U.S. Department of Energy, that simulates climate patterns around the world at a resolution of 110 kilometers. The researchers used eight years of past data for temperature, humidity, and wind speed to train their new algorithm, which learned dynamical associations between the measured weather features and the E3SM model. They then ran the climate model forward in time for about 36 years and applied the trained algorithm to the model’s simulations. They found that the corrected version produced climate patterns that more closely matched real-world observations from the last 36 years, not used for training.

      “We’re not talking about huge differences in absolute terms,” Sapsis says. “An extreme event in the uncorrected simulation might be 105 degrees Fahrenheit, versus 115 degrees with our corrections. But for humans experiencing this, that is a big difference.”

      When the team then paired the corrected coarse model with a specific, finer-resolution model of tropical cyclones, they found the approach accurately reproduced the frequency of extreme storms in specific locations around the world.

      “We now have a coarse model that can get you the right frequency of events, for the present climate. It’s much more improved,” Sapsis says. “Once we correct the dynamics, this is a relevant correction, even when you have a different average global temperature, and it can be used for understanding how forest fires, flooding events, and heat waves will look in a future climate. Our ongoing work is focusing on analyzing future climate scenarios.”

      “The results are particularly impressive as the method shows promising results on E3SM, a state-of-the-art climate model,” says Pedram Hassanzadeh, an associate professor who leads the Climate Extremes Theory and Data group at the University of Chicago and was not involved with the study. “It would be interesting to see what climate change projections this framework yields once future greenhouse-gas emission scenarios are incorporated.”

      This work was supported, in part, by the U.S. Defense Advanced Research Projects Agency. More

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      in Environment

      Gosha Geogdzhayev and Sadhana Lolla named 2024 Gates Cambridge Scholars

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      This article was updated on April 23 to reflect the promotion of Gosha Geogdzhayev from alternate to winner of the Gates Cambridge Scholarship.

      MIT seniors Gosha Geogdzhayev and Sadhana Lolla have won the prestigious Gates Cambridge Scholarship, which offers students an opportunity to pursue graduate study in the field of their choice at Cambridge University in the U.K.

      Established in 2000, Gates Cambridge offers full-cost post-graduate scholarships to outstanding applicants from countries outside of the U.K. The mission of Gates Cambridge is to build a global network of future leaders committed to improving the lives of others.

      Gosha Geogdzhayev

      Originally from New York City, Geogdzhayev is a senior majoring in physics with minors in mathematics and computer science. At Cambridge, Geogdzhayev intends to pursue an MPhil in quantitative climate and environmental science. He is interested in applying these subjects to climate science and intends to spend his career developing novel statistical methods for climate prediction.

      At MIT, Geogdzhayev researches climate emulators with Professor Raffaele Ferrari’s group in the Department of Earth, Atmospheric and Planetary Sciences and is part of the “Bringing Computation to the Climate Challenge” Grand Challenges project. He is currently working on an operator-based emulator for the projection of climate extremes. Previously, Geogdzhayev studied the statistics of changing chaotic systems, work that has recently been published as a first-author paper.

      As a recipient of the National Oceanic and Atmospheric Agency (NOAA) Hollings Scholarship, Geogdzhayev has worked on bias correction methods for climate data at the NOAA Geophysical Fluid Dynamics Laboratory. He is the recipient of several other awards in the field of earth and atmospheric sciences, notably the American Meteorological Society Ward and Eileen Seguin Scholarship.

      Outside of research, Geogdzhayev enjoys writing poetry and is actively involved with his living community, Burton 1, for which he has previously served as floor chair.

      Sadhana Lolla

      Lolla, a senior from Clarksburg, Maryland, is majoring in computer science and minoring in mathematics and literature. At Cambridge, she will pursue an MPhil in technology policy.

      In the future, Lolla aims to lead conversations on deploying and developing technology for marginalized communities, such as the rural Indian village that her family calls home, while also conducting research in embodied intelligence.

      At MIT, Lolla conducts research on safe and trustworthy robotics and deep learning at the Distributed Robotics Laboratory with Professor Daniela Rus. Her research has spanned debiasing strategies for autonomous vehicles and accelerating robotic design processes. At Microsoft Research and Themis AI, she works on creating uncertainty-aware frameworks for deep learning, which has impacts across computational biology, language modeling, and robotics. She has presented her work at the Neural Information Processing Systems (NeurIPS) conference and the International Conference on Machine Learning (ICML). 

      Outside of research, Lolla leads initiatives to make computer science education more accessible globally. She is an instructor for class 6.s191 (MIT Introduction to Deep Learning), one of the largest AI courses in the world, which reaches millions of students annually. She serves as the curriculum lead for Momentum AI, the only U.S. program that teaches AI to underserved students for free, and she has taught hundreds of students in Northern Scotland as part of the MIT Global Teaching Labs program.

      Lolla was also the director for xFair, MIT’s largest student-run career fair, and is an executive board member for Next Sing, where she works to make a cappella more accessible for students across musical backgrounds. In her free time, she enjoys singing, solving crossword puzzles, and baking. More

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      in Energy

      Generative AI for smart grid modeling

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      MIT’s Laboratory for Information and Decision Systems (LIDS) has been awarded $1,365,000 in funding from the Appalachian Regional Commission (ARC) to support its involvement with an innovative project, “Forming the Smart Grid Deployment Consortium (SGDC) and Expanding the HILLTOP+ Platform.”

      The grant was made available through ARC’s Appalachian Regional Initiative for Stronger Economies, which fosters regional economic transformation through multi-state collaboration.

      Led by Kalyan Veeramachaneni, research scientist and principal investigator at LIDS’ Data to AI Group, the project will focus on creating AI-driven generative models for customer load data. Veeramachaneni and colleagues will work alongside a team of universities and organizations led by Tennessee Tech University, including collaborators across Ohio, Pennsylvania, West Virginia, and Tennessee, to develop and deploy smart grid modeling services through the SGDC project.

      These generative models have far-reaching applications, including grid modeling and training algorithms for energy tech startups. When the models are trained on existing data, they create additional, realistic data that can augment limited datasets or stand in for sensitive ones. Stakeholders can then use these models to understand and plan for specific what-if scenarios far beyond what could be achieved with existing data alone. For example, generated data can predict the potential load on the grid if an additional 1,000 households were to adopt solar technologies, how that load might change throughout the day, and similar contingencies vital to future planning.

      The generative AI models developed by Veeramachaneni and his team will provide inputs to modeling services based on the HILLTOP+ microgrid simulation platform, originally prototyped by MIT Lincoln Laboratory. HILLTOP+ will be used to model and test new smart grid technologies in a virtual “safe space,” providing rural electric utilities with increased confidence in deploying smart grid technologies, including utility-scale battery storage. Energy tech startups will also benefit from HILLTOP+ grid modeling services, enabling them to develop and virtually test their smart grid hardware and software products for scalability and interoperability.

      The project aims to assist rural electric utilities and energy tech startups in mitigating the risks associated with deploying these new technologies. “This project is a powerful example of how generative AI can transform a sector — in this case, the energy sector,” says Veeramachaneni. “In order to be useful, generative AI technologies and their development have to be closely integrated with domain expertise. I am thrilled to be collaborating with experts in grid modeling, and working alongside them to integrate the latest and greatest from my research group and push the boundaries of these technologies.”

      “This project is testament to the power of collaboration and innovation, and we look forward to working with our collaborators to drive positive change in the energy sector,” says Satish Mahajan, principal investigator for the project at Tennessee Tech and a professor of electrical and computer engineering. Tennessee Tech’s Center for Rural Innovation director, Michael Aikens, adds, “Together, we are taking significant steps towards a more sustainable and resilient future for the Appalachian region.” More

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      in Environment

      Q&A: A blueprint for sustainable innovation

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      Atacama Biomaterials is a startup combining architecture, machine learning, and chemical engineering to create eco-friendly materials with multiple applications. Passionate about sustainable innovation, its co-founder Paloma Gonzalez-Rojas SM ’15, PhD ’21 highlights here how MIT has supported the project through several of its entrepreneurship initiatives, and reflects on the role of design in building a holistic vision for an expanding business.

      Q: What role do you see your startup playing in the sustainable materials space?

      A: Atacama Biomaterials is a venture dedicated to advancing sustainable materials through state-of-the-art technology. With my co-founder Jose Tomas Dominguez, we have been working on developing our technology since 2019. We initially started the company in 2020 under another name and received Sandbox funds the next year. In 2021, we went through The Engine’s accelerator, Blueprint, and changed our name to Atacama Biomaterials in 2022 during the MITdesignX program. 

      This technology we have developed allows us to create our own data and material library using artificial intelligence and machine learning, and serves as a platform applicable to various industries horizontally — biofuels, biological drugs, and even mining. Vertically, we produce inexpensive, regionally sourced, and environmentally friendly bio-based polymers and packaging — that is, naturally compostable plastics as a flagship product, along with AI products.

      Q: What motivated you to venture into biomaterials and found Atacama?

      A: I’m from Chile, a country with a beautiful, rich geography and nature where we can see all the problems stemming from industry, waste management, and pollution. We named our company Atacama Biomaterials because the Atacama Desert in Chile — one of the places where you can best see the stars in the world — is becoming a plastic dump, as many other places on Earth. I care deeply about sustainability, and I have an emotional attachment to stop these problems. Considering that manufacturing accounts for 29 percent of global carbon emissions, it is clear that sustainability has a role in how we define technology and entrepreneurship, as well as a socio-economic dimension.

      When I first came to MIT, it was to develop software in the Department of Architecture’s Design and Computation Group, with MIT professors Svafa Gronfeldt as co-advisor and Regina Barzilay as committee member. During my PhD, I studied machine-learning methods simulating pedestrian motion to understand how people move in space. In my work, I would use lots of plastics for 3D printing and I couldn’t stop thinking about sustainability and climate change, so I reached out to material science and mechanical engineering professors to look into biopolymers and degradable bio-based materials. This is how I met my co-founder, as we were both working with MIT Professor Neil Gershenfeld. Together, we were part of one of the first teams in the world to 3D print wood fibers, which is difficult — it’s slow and expensive — and quickly pivoted to sustainable packaging. 

      I then won a fellowship from MCSC [the MIT Climate and Sustainability Consortium], which gave me freedom to explore further, and I eventually got a postdoc in MIT chemical engineering, guided by MIT Professor Gregory Rutledge, a polymer physicist. This was unexpected in my career path. Winning Nucleate Eco Track 2022 and the MITdesignX Innovation Award in 2022 profiled Atacama Biomaterials as one of the rising startups in Boston’s biotechnology and climate-tech scene.

      Q: What is your process to develop new biomaterials?

      A: My PhD research, coupled with my background in material development and molecular dynamics, sparked the realization that principles I studied simulating pedestrian motion could also apply to molecular engineering. This connection may seem unconventional, but for me, it was a natural progression. Early in my career, I developed an intuition for materials, understanding their mechanics and physics.

      Using my experience and skills, and leveraging machine learning as a technology jump, I applied a similar conceptual framework to simulate the trajectories of molecules and find potential applications in biomaterials. Making that parallel and shift was amazing. It allowed me to optimize a state-of-the-art molecular dynamic software to run twice as fast as more traditional technologies through my algorithm presented at the International Conference of Machine Learning this year. This is very important, because this kind of simulation usually takes a week, so narrowing it down to two days has major implications for scientists and industry, in material science, chemical engineering, computer science and related fields. Such work greatly influenced the foundation of Atacama Biomaterials, where we developed our own AI to deploy our materials. In an effort to mitigate the environmental impact of manufacturing, Atacama is targeting a 16.7 percent reduction in carbon dioxide emissions associated with the manufacturing process of its polymers, through the use of renewable energy. 

      Another thing is that I was trained as an architect in Chile, and my degree had a design component. I think design allows me to understand problems at a very high level, and how things interconnect. It contributed to developing a holistic vision for Atacama, because it allowed me to jump from one technology or discipline to another and understand broader applications on a conceptual level. Our design approach also meant that sustainability came to the center of our work from the very beginning, not just a plus or an added cost.

      Q: What was the role of MITdesignX in Atacama’s development?

      A: I have known Svafa Grönfeldt, MITdesignX’s faculty director, for almost six years. She was the co-advisor of my PhD, and we had a mentor-mentee relationship. I admire the fact that she created a space for people interested in business and entrepreneurship to grow within the Department of Architecture. She and Executive Director Gilad Rosenzweig gave us fantastic advice, and we received significant support from mentors. For example, Daniel Tsai helped us with intellectual property, including a crucial patent for Atacama. And we’re still in touch with the rest of the cohort. I really like this “design your company” approach, which I find quite unique, because it gives us the opportunity to reflect on who we want to be as designers, technologists, and entrepreneurs. Studying user insights also allowed us to understand the broad applicability of our research, and align our vision with market demands, ultimately shaping Atacama into a company with a holistic perspective on sustainable material development.

      Q: How does Atacama approach scaling, and what are the immediate next steps for the company?

      A: When I think about accomplishing our vision, I feel really inspired by my 3-year-old daughter. I want her to experience a world with trees and wildlife when she’s 100 years old, and I hope Atacama will contribute to such a future.

      Going back to the designer’s perspective, we designed the whole process holistically, from feedstock to material development, incorporating AI and advanced manufacturing. Having proved that there is a demand for the materials we are developing, and having tested our products, manufacturing process, and technology in critical environments, we are now ready to scale. Our level of technology-readiness is comparable to the one used by NASA (level 4).

      We have proof of concept: a biodegradable and recyclable packaging material which is cost- and energy-efficient as a clean energy enabler in large-scale manufacturing. We have received pre-seed funding, and are sustainably scaling by taking advantage of available resources around the world, like repurposing machinery from the paper industry. As presented in the MIT Industrial Liaison and STEX Program’s recent Sustainability Conference, unlike our competitors, we have cost-parity with current packaging materials, as well as low-energy processes. And we also proved the demand for our products, which was an important milestone. Our next steps involve strategically expanding our manufacturing capabilities and research facilities and we are currently evaluating building a factory in Chile and establishing an R&D lab plus a manufacturing plant in the U.S. More

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      in Environment

      MIT in the media: 2023 in review

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      It was an eventful trip around the sun for MIT this year, from President Sally Kornbluth’s inauguration and Mark Rober’s Commencement address to Professor Moungi Bawendi winning the Nobel Prize in Chemistry. In 2023 MIT researchers made key advances, detecting a dying star swallowing a planet, exploring the frontiers of artificial intelligence, creating clean energy solutions, inventing tools aimed at earlier detection and diagnosis of cancer, and even exploring the science of spreading kindness. Below are highlights of some of the uplifting people, breakthroughs, and ideas from MIT that made headlines in 2023.

      The gift: Kindness goes viral with Steve HartmanSteve Hartman visited Professor Anette “Peko” Hosoi to explore the science behind whether a single act of kindness can change the world.Full story via CBS News

      Trio wins Nobel Prize in chemistry for work on quantum dots, used in electronics and medical imaging“The motivation really is the basic science. A basic understanding, the curiosity of ‘how does the world work?’” said Professor Moungi Bawendi of the inspiration for his research on quantum dots, for which he was co-awarded the 2023 Nobel Prize in Chemistry.Full story via the Associated Press

      How MIT’s all-women leadership team plans to change science for the betterPresident Sally Kornbluth, Provost Cynthia Barnhart, and Chancellor Melissa Nobles emphasized the importance of representation for women and underrepresented groups in STEM.Full story via Radio Boston

      MIT via community college? Transfer students find a new path to a degreeUndergraduate Subin Kim shared his experience transferring from community college to MIT through the Transfer Scholars Network, which is aimed at helping community college students find a path to four-year universities.Full story via the Christian Science Monitor

      MIT president Sally Kornbluth doesn’t think we can hit the pause button on AIPresident Kornbluth discussed the future of AI, ethics in science, and climate change with columnist Shirley Leung on her new “Say More” podcast. “I view [the climate crisis] as an existential issue to the extent that if we don’t take action there, all of the many, many other things that we’re working on, not that they’ll be irrelevant, but they’ll pale in comparison,” Kornbluth said.Full story via The Boston Globe 

      It’s the end of a world as we know itAstronomers from MIT, Harvard University, Caltech and elsewhere spotted a dying star swallowing a large planet. Postdoc Kishalay De explained that: “Finding an event like this really puts all of the theories that have been out there to the most stringent tests possible. It really opens up this entire new field of research.”Full story via The New York Times

      Frontiers of AI

      Hey, Alexa, what should students learn about AI?The Day of AI is a program developed by the MIT RAISE initiative aimed at introducing and teaching K-12 students about AI. “We want students to be informed, responsible users and informed, responsible designers of these technologies,” said Professor Cynthia Breazeal, dean of digital learning at MIT.Full story via The New York Times

      AI tipping pointFour faculty members from across MIT — Professors Song Han, Simon Johnson, Yoon Kim and Rosalind Picard — described the opportunities and risks posed by the rapid advancements in the field of AI.Full story via Curiosity Stream 

      A look into the future of AI at MIT’s robotics laboratoryProfessor Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory, discussed the future of artificial intelligence, robotics, and machine learning, emphasizing the importance of balancing the development of new technologies with the need to ensure they are deployed in a way that benefits humanity.Full story via Mashable

      Health care providers say artificial intelligence could transform medicineProfessor Regina Barzilay spoke about her work developing new AI systems that could be used to help diagnose breast and lung cancer before the cancers are detectable to the human eye.Full story via Chronicle

      Is AI coming for your job? Tech experts weigh in: “They don’t replace human labor”Professor David Autor discussed how the rise of artificial intelligence could change the quality of jobs available.Full story via CBS News

      Big tech is bad. Big AI will be worse.Institute Professor Daron Acemoglu and Professor Simon Johnson made the case that “rather than machine intelligence, what we need is ‘machine usefulness,’ which emphasizes the ability of computers to augment human capabilities.”Full story via The New York Times

      Engineering excitement

      MIT’s 3D-printed hearts could pump new life into customized treatments MIT engineers developed a technique for 3D printing a soft, flexible, custom-designed replica of a patient’s heart.Full story via WBUR

      Mystery of why Roman buildings have survived so long has been unraveled, scientists sayScientists from MIT and other institutions discovered that ancient Romans used lime clasts when manufacturing concrete, giving the material self-healing properties.Full story via CNN

      The most interesting startup in America is in Massachusetts. You’ve probably never heard of it.VulcanForms, an MIT startup, is at the “leading edge of a push to transform 3D printing from a niche technology — best known for new-product prototyping and art-class experimentation — into an industrial force.”Full story via The Boston Globe

      Catalyzing climate innovations

      Can Boston’s energy innovators save the world?Boston Magazine reporter Rowan Jacobsen spotlighted how MIT faculty, students, and alumni are leading the charge in clean energy startups. “When it comes to game-changing breakthroughs in energy, three letters keep surfacing again and again: MIT,” writes Jacobsen.Full story via Boston Magazine

      MIT research could be game changer in combating water shortagesMIT researchers discovered that a common hydrogel used in cosmetic creams, industrial coatings, and pharmaceutical capsules can absorb moisture from the atmosphere even as the temperature rises. “For a planet that’s getting hotter, this could be a game-changing discovery.”Full story via NBC Boston

      Energy-storing concrete could form foundations for solar-powered homesMIT engineers uncovered a new way of creating an energy supercapacitor by combining cement, carbon black, and water that could one day be used to power homes or electric vehicles.Full story via New Scientist

      MIT researchers tackle key question of EV adoption: When to charge?MIT scientists found that delayed charging and strategic placement of EV charging stations could help reduce additional energy demands caused by more widespread EV adoption.Full story via Fast Company

      Building better buildingsProfessor John Fernández examined how to reduce the climate footprints of homes and office buildings, recommending creating airtight structures, switching to cleaner heating sources, using more environmentally friendly building materials, and retrofitting existing homes and offices.Full story via The New York Times

      They’re building an “ice penetrator” on a hillside in WestfordResearchers from MIT’s Haystack Observatory built an “ice penetrator,” a device designed to monitor the changing conditions of sea ice.Full story via The Boston Globe

      Healing health solutions

      How Boston is beating cancerMIT researchers are developing drug-delivery nanoparticles aimed at targeting cancer cells without disturbing healthy cells. Essentially, the nanoparticles are “engineered for selectivity,” explained Professor Paula Hammond, head of MIT’s Department of Chemical Engineering.Full story via Boston Magazine

      A new antibiotic, discovered with artificial intelligence, may defeat a dangerous superbugUsing a machine-learning algorithm, researchers from MIT discovered a type of antibiotic that’s effective against a particular strain of drug-resistant bacteria.Full story via CNN

      To detect breast cancer sooner, an MIT professor designs an ultrasound braMIT researchers designed a wearable ultrasound device that attaches to a bra and could be used to detect early-stage breast tumors.Full story via STAT

      The quest for a switch to turn on hungerAn ingestible pill developed by MIT scientists can raise levels of hormones to help increase appetite and decrease nausea in patients with gastroparesis.Full story via Wired

      Here’s how to use dreams for creative inspirationMIT scientists found that the earlier stages of sleep are key to sparking creativity and that people can be guided to dream about specific topics, further boosting creativity.Full story via Scientific American

      Astounding art

      An AI opera from 1987 reboots for a new generationProfessor Tod Machover discussed the restaging of his opera “VALIS” at MIT, which featured an artificial intelligence-assisted musical instrument developed by Nina Masuelli ’23.Full story via The Boston Globe

      Surfacing the stories hidden in migration dataAssociate Professor Sarah Williams discussed the Civic Data Design Lab’s “Motivational Tapestry,” a large woven art piece that uses data from the United Nations World Food Program to visually represent the individual motivations of 1,624 Central Americans who have migrated to the U.S.Full story via Metropolis

      Augmented reality-infused production of Wagner’s “Parsifal” opens Bayreuth FestivalProfessor Jay Scheib’s augmented reality-infused production of Richard Wagner’s “Parsifal” brought “fantastical images” to audience members.Full story via the Associated Press

      Understanding our universe

      New image reveals violent events near a supermassive black holeScientists captured a new image of M87*, the black hole at the center of the Messier 87 galaxy, showing the “launching point of a colossal jet of high-energy particles shooting outward into space.”Full story via Reuters

      Gravitational waves: A new universeMIT researchers Lisa Barsotti, Deep Chatterjee, and Victoria Xu explored how advances in gravitational wave detection are enabling a better understanding of the universe.Full story via Curiosity Stream 

      Nergis Mavalvala helped detect the first gravitational wave. Her work doesn’t stop thereProfessor Nergis Mavalvala, dean of the School of Science, discussed her work searching for gravitational waves, the importance of skepticism in scientific research, and why she enjoys working with young people.Full story via Wired

      Hitting the books

      “The Transcendent Brain” review: Beyond ones and zeroesIn his book “The Transcendent Brain: Spirituality in the Age of Science,” Alan Lightman, a professor of the practice of humanities, displayed his gift for “distilling complex ideas and emotions to their bright essence.”Full story via The Wall Street Journal

      What happens when CEOs treat workers better? Companies (and workers) win.Professor of the practice Zeynep Ton published a book, “The Case for Good Jobs,” and is “on a mission to change how company leaders think, and how they treat their employees.”Full story via The Boston Globe

      How to wage war on conspiracy theoriesProfessor Adam Berinsky’s book, “Political Rumors: Why We Accept Misinformation and How to Fight it,” examined “attitudes toward both politics and health, both of which are undermined by distrust and misinformation in ways that cause harm to both individuals and society.”Full story via Politico

      What it takes for Mexican coders to cross the cultural border with Silicon ValleyAssistant Professor Héctor Beltrán discussed his new book, “Code Work: Hacking across the U.S./México Techno-Borderlands,” which explores the culture of hackathons and entrepreneurship in Mexico.Full story via Marketplace

      Cultivating community

      The Indigenous rocketeerNicole McGaa, a fourth-year student at MIT, discussed her work leading MIT’s all-Indigenous rocket team at the 2023 First Nations Launch National Rocket Competition.Full story via Nature

      “You totally got this,” YouTube star and former NASA engineer Mark Rober tells MIT graduatesDuring his Commencement address at MIT, Mark Rober urged graduates to embrace their accomplishments and boldly face any challenges they encounter.Full story via The Boston Globe

      MIT Juggling Club going strong after half centuryAfter almost 50 years, the MIT Juggling Club, which was founded in 1975 and then merged with a unicycle club, is the oldest drop-in juggling club in continuous operation and still welcomes any aspiring jugglers to come toss a ball (or three) into the air.Full story via Cambridge Day

      Volpe Transportation Center opens as part of $750 million deal between MIT and fedsThe John A. Volpe National Transportation Systems Center in Kendall Square was the first building to open in MIT’s redevelopment of the 14-acre Volpe site that will ultimately include “research labs, retail, affordable housing, and open space, with the goal of not only encouraging innovation, but also enhancing the surrounding community.”Full story via The Boston Globe

      Sparking conversation

      The future of AI innovation and the role of academics in shaping itProfessor Daniela Rus emphasized the central role universities play in fostering innovation and the importance of ensuring universities have the computing resources necessary to help tackle major global challenges.Full story via The Boston Globe

      Moving the needle on supply chain sustainabilityProfessor Yossi Sheffi examined several strategies companies could use to help improve supply chain sustainability, including redesigning last-mile deliveries, influencing consumer choices and incentivizing returnable containers.Full story via The Hill

      Expelled from the mountain top?Sylvester James Gates Jr. ’73, PhD ’77 made the case that “diverse learning environments expose students to a broader range of perspectives, enhance education, and inculcate creativity and innovative habits of mind.”Full story via Science

      Marketing magic of “Barbie” movie has lessons for women’s sportsMIT Sloan Lecturer Shira Springer explored how the success of the “Barbie” movie could be applied to women’s sports.Full story via Sports Business Journal

      We’re already paying for universal health care. Why don’t we have it?Professor Amy Finkelstein asserted that the solution to health insurance reform in the U.S. is “universal coverage that is automatic, free and basic.”Full story via The New York Times 

      The internet could be so good. Really.Professor Deb Roy described how “new kinds of social networks can be designed for constructive communication — for listening, dialogue, deliberation, and mediation — and they can actually work.”Full story via The Atlantic

      Fostering educational excellence

      MIT students give legendary linear algebra professor standing ovation in last lectureAfter 63 years of teaching and over 10 million views of his online lectures, Professor Gilbert Strang received a standing ovation after his last lecture on linear algebra. “I am so grateful to everyone who likes linear algebra and sees its importance. So many universities (and even high schools) now appreciate how beautiful it is and how valuable it is,” said Strang.Full story via USA Today

      “Brave Behind Bars”: Reshaping the lives of inmates through coding classesGraduate students Martin Nisser and Marisa Gaetz co-founded Brave Behind Bars, a program designed to provide incarcerated individuals with coding and digital literacy skills to better prepare them for life after prison.Full story via MSNBC

      Melrose TikTok user “Ms. Nuclear Energy” teaching about nuclear power through social mediaGraduate student Kaylee Cunningham discussed her work using social media to help educate and inform the public about nuclear energy.Full story via CBS Boston  More

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      in Energy

      AI meets climate: MIT Energy and Climate Hack 2023

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      The MIT Energy and Climate Hack brought together participants from myriad fields and disciplines to develop rapid, innovative solutions to one of the most complex challenges facing society today: the global energy and climate crisis. Hundreds of students from MIT and colleges across the globe convened on MIT’s campus and virtually for this year’s event, which was held Nov. 10-12.

      Established in 2013, the MIT Energy and Climate Hack has been the launchpad for innovative and sustainable solutions for a decade; an annual reminder that exciting new ideas are always just around the corner.

      According to Claire Lorenzo, an MIT student organizer and communications director for this year’s Energy and Climate Hack, “There were a lot of people from a lot of places who showed up; both virtually and in person. It was encouraging to see how driven everyone was. How passionate they were about finding great solutions. You could see these ideas starting to form immediately.”

      On the first day, representatives from companies across numerous industries presented participants with their most pressing energy and climate-related challenges. Once the gathering broke into teams, participants had two days to “hack the challenge” they were assigned and present their solution to company representatives, fellow hackers, and judges.  

      The focus areas at this year’s event were energy markets, transportation, and farms and forests. Participating corporate sponsors included Google, Crusoe, Ironwood, Foothill Ventures, Koidra, Mitra Chem, Avangrid, Schneider Electric, First Solar, and Climate Ledger. 

      This year’s event also marked the first time that artificial intelligence emerged as a viable tool for developing creative climate solutions. Lorenzo observed, “I’m studying computer science, so exploring how AI could be harnessed to have a positive impact on the climate was particularly exciting for me. It can be applicable to virtually any domain. Like transportation, [with emissions] for example. In agriculture, too.”

      Energy and Climate Hack organizers identified the implementation of four core AI applications for special consideration: the acceleration of discovery (shortening the development process while simultaneously producing less waste), optimizing real-world solutions (utilizing automation to increase efficiency), prediction (using AI to improve prediction algorithms), and processing unstructured data (using AI to analyze and scale large amounts of data efficiently).

      “If there was a shared sentiment among the participants, it would probably be the idea that there isn’t a singular solution to climate change,” says Lorenzo, “and that requires cooperation from various industries, leveraging knowledge and experience from numerous fields, to make a lasting impact.”

      After the initial round of presentations concluded, one team from each challenge advanced from the preliminary presentation judging session to the final presentation round, where they pitched their solutions to a crowded room of attendees. Once the semi-finalists had pitched their solutions, the judges deliberated over the entries and selected team Fenergy, which worked in the energy markets sector, as the winners. The team, consisting of Alessandro Fumi, Amal Nammouchi, Amaury De Bock, Cyrine Chaabani, and Robbie Lee V, said, “Our solution, Unbiased Cathode, enables researchers to assess the supply chain implications of battery materials before development begins, hence reducing the lab-to-production timeline.”

      “They created a LLM [large language model]-powered tool that allows innovative new battery technologies to be iterated and developed much more efficiently,” Lorenzo added.

      When asked what she will remember most about her first experience at the MIT Energy and Climate Hack, Lorenzo replied, “Having hope for the future. Hope from seeing the passion that so many people have to find a solution. Hope from seeing all of these individuals come so far to tackle this challenge and make a difference. If we continue to develop and implement solutions like these on a global level, I am hopeful.”

      Students interested in learning more about the MIT Energy and Climate Hackathon, or participating in next year’s Hack, can find more information on the event website. More

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      in Energy

      Celebrating Kendall Square’s past and shaping its future

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      Kendall Square’s community took a deep dive into the history and future of the region at the Kendall Square Association’s 15th annual meeting on Oct. 19.

      It’s no secret that Kendall Square, located in Cambridge, Massachusetts, moves fast. The event, titled “Looking Back, Looking Ahead,” gave community members a chance to pause and reflect on how far the region has come and to discuss efforts to shape where it’s going next.

      “The impact of the last 15 years of working together with a purposeful commitment to make the world a better place was on display this evening,” KSA Executive Director Beth O’Neill Maloney told the audience toward the end of the evening. “It also shows how Kendall Square can continue contributing to the world.”

      The gathering took place at the Microsoft NERD Center on Memorial Drive, on a floor that also featured music from the Kendall Square Orchestra and, judging by the piles of empty trays at the end of the night, an exceedingly popular selection of food from Kendall Square restaurants. Attendees came from across Cambridge’s prolific innovation ecosystem — not just entrepreneurs and life science workers but also high school and college students, restaurant and retail shop owners, workers at local cleantech and robotics companies, and leaders of nonprofits.

      KSA itself is a nonprofit made up of over 150 organizations across Kendall Square, from major companies to universities like MIT to research organizations like the Broad Institute of MIT and Harvard and the independent shops and restaurants that give Kendall Square its distinct character.

      The night’s programming included talks about recent funding achievements in the region, a panel discussion on the implications of artificial intelligence, and a highly entertaining, whirlwind history lesson led by Daniel Berger-Jones of Cambridge Historical Tours.

      “Our vision for the state is to be the best, and Kendall really represents that,” said Yvonne Hao, Massachusetts secretary of economic development. “When I went to DC to talk to folks about why Massachusetts should win some of these grants, they said, ‘You already have Kendall, that’s what we’re trying to get the whole country to be like!’”

      Hao started her talk by noting her personal connection to Kendall Square. She moved to Cambridge with her family in 2010 and has watched the neighborhood transform, with her kids frequenting the old and new restaurants and shops around town.

      The crux of Hao’s talk was to remind attendees they had more to celebrate than KSA’s anniversary. Massachusetts was recently named the recipient of two major federal grants that will fuel the state’s innovation work. One of those grants, from the Advanced Research Projects Agency for Health (ARPA-H), designated the state an “Investor Catalyst Hub” to accelerate innovation around health care. The other, which came through the federal CHIPS and Science Act, will allow the state to establish the Northeast Microelectronics Coalition Hub to advance microelectronics jobs, workforce training opportunities, and investment in the region’s advanced manufacturing.

      Hao recalled making the pitch for the grants, which could collectively amount to hundreds of millions of dollars in funding over time.

      “The pitch happened in Kendall Square because Kendall highlights everything magical about Massachusetts — we have our universities, MIT, we have our research institutions, nonprofits, small businesses, and great community members,” Hao said. “We were hoping for good weather because we wanted to walk with government officials, because when you walk around Kendall, you see the art, you see the coffee shops, you see the people bumping into each other and talking, and you see why it’s so important that this one square mile of geography become the hub they were looking for.”

      Hao is also part of work to put together the state’s newest economic development plan. She said the group’s tier one priorities are transportation and housing, but listed a number of other areas where she hopes Massachusetts can improve.

      “We can be an amazing, strong economy that’s mission-driven and innovation-driven with all kinds of jobs for all kinds of people, and at the same time an awesome community that loves each other and has great food and small businesses and looks out for each other, that looks diverse just like this room,” Hao said. “That’s the story we want to tell.”

      After the historical tour and the debut of a video explaining the origins of the KSA, attendees fast-forwarded into the future with a panel discussion on the impact and implications of generative AI.

      “I think the paradigm shift we’re seeing with generative AI is going to be as transformative as the internet, perhaps even more so because the pace of adoption is much faster now,” said Microsoft’s Soundar Srinivasan.

      The panel also featured Jennat Jounaidi, a student at Cambridge Rindge and Latin School and member of Innovators for Purpose, a nonprofit that seeks to empower young people from historically marginalized groups to become innovators.

      “I’m interested to see how generative AI shapes my upbringing as well as the lives of future generations, and I think it’s a pivotal moment to decide how we can best develop and incorporate AI into all of our lives,” Jounaidi said.

      Panelists noted that today’s concerns around AI are important, such as its potential to perpetuate inequality and amplify misinformation. But they also discussed the technology’s potential to drive advances in areas like sustainability and health care.

      “I came to Kendall Square to do my PhD in AI at MIT back when the internet was called the ARPA-Net… so a while ago,” said Jeremy Wertheimer SM ’89, PhD ’96. “One of the dreams I had back then was to create a program to read all biology papers. We’re not quite there yet, but I think we’re on the cusp, and it’s very exciting.

      Above all else, the panelists characterized AI as an opportunity. Despite all that’s been accomplished in Kendall Square to date, the prevailing feeling at the event was excitement for the future.

      “Generative AI is giving us chance to stop working in siloes,” Jounaidi said. “Many people in this room go back to their companies and think about corporate responsibility, and I want to expand that to creating shared value in companies by seeking out the community and the people here. I think that’s important, and I’m excited to see what comes next.” More

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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