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      in Energy

      Integrating humans with AI in structural design

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      Modern fabrication tools such as 3D printers can make structural materials in shapes that would have been difficult or impossible using conventional tools. Meanwhile, new generative design systems can take great advantage of this flexibility to create innovative designs for parts of a new building, car, or virtually any other device.

      But such “black box” automated systems often fall short of producing designs that are fully optimized for their purpose, such as providing the greatest strength in proportion to weight or minimizing the amount of material needed to support a given load. Fully manual design, on the other hand, is time-consuming and labor-intensive.

      Now, researchers at MIT have found a way to achieve some of the best of both of these approaches. They used an automated design system but stopped the process periodically to allow human engineers to evaluate the work in progress and make tweaks or adjustments before letting the computer resume its design process. Introducing a few of these iterations produced results that performed better than those designed by the automated system alone, and the process was completed more quickly compared to the fully manual approach.

      The results are reported this week in the journal Structural and Multidisciplinary Optimization, in a paper by MIT doctoral student Dat Ha and assistant professor of civil and environmental engineering Josephine Carstensen.

      The basic approach can be applied to a broad range of scales and applications, Carstensen explains, for the design of everything from biomedical devices to nanoscale materials to structural support members of a skyscraper. Already, automated design systems have found many applications. “If we can make things in a better way, if we can make whatever we want, why not make it better?” she asks.

      “It’s a way to take advantage of how we can make things in much more complex ways than we could in the past,” says Ha, adding that automated design systems have already begun to be widely used over the last decade in automotive and aerospace industries, where reducing weight while maintaining structural strength is a key need.

      “You can take a lot of weight out of components, and in these two industries, everything is driven by weight,” he says. In some cases, such as internal components that aren’t visible, appearance is irrelevant, but for other structures aesthetics may be important as well. The new system makes it possible to optimize designs for visual as well as mechanical properties, and in such decisions the human touch is essential.

      As a demonstration of their process in action, the researchers designed a number of structural load-bearing beams, such as might be used in a building or a bridge. In their iterations, they saw that the design has an area that could fail prematurely, so they selected that feature and required the program to address it. The computer system then revised the design accordingly, removing the highlighted strut and strengthening some other struts to compensate, and leading to an improved final design.

      The process, which they call Human-Informed Topology Optimization, begins by setting out the needed specifications — for example, a beam needs to be this length, supported on two points at its ends, and must support this much of a load. “As we’re seeing the structure evolve on the computer screen in response to initial specification,” Carstensen says, “we interrupt the design and ask the user to judge it. The user can select, say, ‘I’m not a fan of this region, I’d like you to beef up or beef down this feature size requirement.’ And then the algorithm takes into account the user input.”

      While the result is not as ideal as what might be produced by a fully rigorous yet significantly slower design algorithm that considers the underlying physics, she says it can be much better than a result generated by a rapid automated design system alone. “You don’t get something that’s quite as good, but that was not necessarily the goal. What we can show is that instead of using several hours to get something, we can use 10 minutes and get something much better than where we started off.”

      The system can be used to optimize a design based on any desired properties, not just strength and weight. For example, it can be used to minimize fracture or buckling, or to reduce stresses in the material by softening corners.

      Carstensen says, “We’re not looking to replace the seven-hour solution. If you have all the time and all the resources in the world, obviously you can run these and it’s going to give you the best solution.” But for many situations, such as designing replacement parts for equipment in a war zone or a disaster-relief area with limited computational power available, “then this kind of solution that catered directly to your needs would prevail.”

      Similarly, for smaller companies manufacturing equipment in essentially “mom and pop” businesses, such a simplified system might be just the ticket. The new system they developed is not only simple and efficient to run on smaller computers, but it also requires far less training to produce useful results, Carstensen says. A basic two-dimensional version of the software, suitable for designing basic beams and structural parts, is freely available now online, she says, as the team continues to develop a full 3D version.

      “The potential applications of Prof Carstensen’s research and tools are quite extraordinary,” says Christian Málaga-Chuquitaype, a professor of civil and environmental engineering at Imperial College London, who was not associated with this work. “With this work, her group is paving the way toward a truly synergistic human-machine design interaction.”

      “By integrating engineering ‘intuition’ (or engineering ‘judgement’) into a rigorous yet computationally efficient topology optimization process, the human engineer is offered the possibility of guiding the creation of optimal structural configurations in a way that was not available to us before,” he adds. “Her findings have the potential to change the way engineers tackle ‘day-to-day’ design tasks.” More

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      in Environment

      MIT Solve announces 2023 global challenges and Indigenous Communities Fellowship

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      MIT Solve, an MIT initiative with a mission to drive innovation to solve world challenges, announced today the 2023 Global Challenges and the Indigenous Communities Fellowship. 

      Solve invites anyone from anywhere in the world to submit a solution to this year’s challenges by 12 p.m. EST on May 9. The 40 innovators — including eight new Indigenous Communities Fellows — will form the 2023 Solver Class, and pitch their solutions during Solve Challenge Finals on Sept. 17-18 in New York City. These selected teams will share over $1 million in available funding, take part in a nine-month support program, and join the Solve community made of cross-sector social impact leaders, to scale their solutions.

      Solve’s 2023 Global Challenges are: 

      For its second year, Solve will select a cohort of entrepreneurs among the 2023 Solver Class to join the Black and Brown Innovators in the U.S. Program. The program offers culturally-responsive support and partnership opportunities, and selected teams will participate in Solve’s annual U.S. Equity Summit. 

      In addition to the Global Challenges, Solve is also opening applications for the 2023 Indigenous Communities Fellowship. The fellowship, which looks for Native innovators in the United States and its territories, has now expanded eligibility to Canada. 

      “Every year we are inspired by people’s ingenuity and their determination to solve the most pressing issues of our time,” says Hala Hanna, acting executive director of MIT Solve. “We are excited to shine a spotlight on the most promising ones and grateful for our supporters who will help scale their impact.”

      Interested applicants can learn more and apply online at solve.mit.edu/challenges. 

      To date, the funding available for selected Solver teams and fellows includes:

      MIT Solve Funding — $400,000 with a $10,000 grant to each Solver team and fellow selected
      The GM Prize (supported by General Motors) — up to $150,000 across up to six solutions from the Learning for Civic Action Challenge, the Climate Adaptation & Low-Carbon Housing Challenge, and the 2023 Indigenous Communities Fellowship
      The AI for Humanity Prize (supported by The Patrick J. McGovern Foundation) — up to $150,000 to solutions that leverage data science, artificial intelligence, and/or machine learning to benefit humanity, selected from any of the 2023 Global Challenges
      The GSR Foundation Prize (supported by GSR Foundation) — up to $200,000 to innovative technology solutions from any of the 2023 Global Challenges, with a focus on solutions that use blockchain to improve financial inclusion
      Living Forests Prize (supported by Good Energies Foundation) — up to $100,000 across up to four solutions that help restore ecosystems or increase the use of sustainable forest products, selected from the Climate Adaptation & Low-Carbon Housing Challenge
      Those interested in sponsoring a prize should contact sue.kim@solve.mit.edu.

      Additionally, Solve Innovation Future will offer investment capital to Solver teams selected as a part of the 2023 class. To date, Solve Innovation Future has deployed over $1.3 million to more than 13 for-profit Solver team companies that are driving impact toward UN Sustainable Development Goals, and has catalyzed nearly seven times its investment in additional investment capital toward the Solver teams.

      The Solve community will convene on MIT’s campus for its flagship event Solve at MIT May 4-6 to celebrate the 2022 Solver Class. You may request an invitation here. Press interested in attending the event should contact maya.bingaman@solve.mit.edu. 

      Solve is a marketplace for social impact innovation. Through open innovation challenges, Solve finds incredible tech-based social entrepreneurs all around the world. Solve then brings together MIT’s innovation ecosystem and a community of members to fund and support these entrepreneurs to drive lasting, transformational impact. Solve has catalyzed over $60 million in commitments for Solver teams and entrepreneurs to date. More

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      in Energy

      Strengthening electron-triggered light emission

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      The way electrons interact with photons of light is a key part of many modern technologies, from lasers to solar panels to LEDs. But the interaction is inherently a weak one because of a major mismatch in scale: A wavelength of visible light is about 1,000 times larger than an electron, so the way the two things affect each other is limited by that disparity.

      Now, researchers at MIT and elsewhere have come up with an innovative way to make much stronger interactions between photons and electrons possible, in the process producing a hundredfold increase in the emission of light from a phenomenon called Smith-Purcell radiation. The finding has potential implications for both commercial applications and fundamental scientific research, although it will require more years of research to make it practical.

      The findings are reported today in the journal Nature, in a paper by MIT postdocs Yi Yang (now an assistant professor at the University of Hong Kong) and Charles Roques-Carmes, MIT professors Marin Soljačić and John Joannopoulos, and five others at MIT, Harvard University, and Technion-Israel Institute of Technology.

      In a combination of computer simulations and laboratory experiments, the team found that using a beam of electrons in combination with a specially designed photonic crystal — a slab of silicon on an insulator, etched with an array of nanometer-scale holes — they could theoretically predict stronger emission by many orders of magnitude than would ordinarily be possible in conventional Smith-Purcell radiation. They also experimentally recorded a one hundredfold increase in radiation in their proof-of-concept measurements.

      Unlike other approaches to producing sources of light or other electromagnetic radiation, the free-electron-based method is fully tunable — it can produce emissions of any desired wavelength, simply by adjusting the size of the photonic structure and the speed of the electrons. This may make it especially valuable for making sources of emission at wavelengths that are difficult to produce efficiently, including terahertz waves, ultraviolet light, and X-rays.

      The team has so far demonstrated the hundredfold enhancement in emission using a repurposed electron microscope to function as an electron beam source. But they say that the basic principle involved could potentially enable far greater enhancements using devices specifically adapted for this function.

      The approach is based on a concept called flatbands, which have been widely explored in recent years for condensed matter physics and photonics but have never been applied to affecting the basic interaction of photons and free electrons. The underlying principle involves the transfer of momentum from the electron to a group of photons, or vice versa. Whereas conventional light-electron interactions rely on producing light at a single angle, the photonic crystal is tuned in such a way that it enables the production of a whole range of angles.

      The same process could also be used in the opposite direction, using resonant light waves to propel electrons, increasing their velocity in a way that could potentially be harnessed to build miniaturized particle accelerators on a chip. These might ultimately be able to perform some functions that currently require giant underground tunnels, such as the 30-kilometer-wide Large Hadron Collider in Switzerland.

      “If you could actually build electron accelerators on a chip,” Soljačić says, “you could make much more compact accelerators for some of the applications of interest, which would still produce very energetic electrons. That obviously would be huge. For many applications, you wouldn’t have to build these huge facilities.”

      The new system could also potentially provide a highly controllable X-ray beam for radiotherapy purposes, Roques-Carmes says.

      And the system could be used to generate multiple entangled photons, a quantum effect that could be useful in the creation of quantum-based computational and communications systems, the researchers say. “You can use electrons to couple many photons together, which is a considerably hard problem if using a purely optical approach,” says Yang. “That is one of the most exciting future directions of our work.”

      Much work remains to translate these new findings into practical devices, Soljačić cautions. It may take some years to develop the necessary interfaces between the optical and electronic components and how to connect them on a single chip, and to develop the necessary on-chip electron source producing a continuous wavefront, among other challenges.

      “The reason this is exciting,” Roques-Carmes adds, “is because this is quite a different type of source.” While most technologies for generating light are restricted to very specific ranges of color or wavelength, and “it’s usually difficult to move that emission frequency. Here it’s completely tunable. Simply by changing the velocity of the electrons, you can change the emission frequency. … That excites us about the potential of these sources. Because they’re different, they offer new types of opportunities.”

      But, Soljačić concludes, “in order for them to become truly competitive with other types of sources, I think it will require some more years of research. I would say that with some serious effort, in two to five years they might start competing in at least some areas of radiation.”

      The research team also included Steven Kooi at MIT’s Institute for Soldier Nanotechnologies, Haoning Tang and Eric Mazur at Harvard University, Justin Beroz at MIT, and Ido Kaminer at Technion-Israel Institute of Technology. The work was supported by the U.S. Army Research Office through the Institute for Soldier Nanotechnologies, the U.S. Air Force Office of Scientific Research, and the U.S. Office of Naval Research. More

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      in Energy

      Pursuing a practical approach to research

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      Koroush Shirvan, the John Clark Hardwick Career Development Professor in the Department of Nuclear Science and Engineering (NSE), knows that the nuclear industry has traditionally been wary of innovations until they are shown to have proven utility. As a result, he has relentlessly focused on practical applications in his research, work that has netted him the 2022 Reactor Technology Award from the American Nuclear Society. “The award has usually recognized practical contributions to the field of reactor design and has not often gone to academia,” Shirvan says.

      One of these “practical contributions” is in the field of accident-tolerant fuels, a program launched by the U.S. Nuclear Regulatory Commission in the wake of the 2011 Fukushima Daiichi incident. The goal within this program, says Shirvan, is to develop new forms of nuclear fuels that can tolerate heat. His team, with students from over 16 countries, is working on numerous possibilities that range in composition and method of production.

      Another aspect of Shirvan’s research focuses on how radiation impacts heat transfer mechanisms in the reactor. The team found fuel corrosion to be the driving force. “[The research] informs how nuclear fuels perform in the reactor, from a practical point of view,” Shirvan says.

      Optimizing nuclear reactor design

      A summer internship when Shirvan was an undergraduate at the University of Florida at Gainesville seeded his drive to focus on practical applications in his studies. A nearby nuclear utility was losing millions because of crud accumulating on fuel rods. Over time, the company was solving the problem by using more fuel, before it had extracted all the life from earlier batches.

      Placement of fuel rods in nuclear reactors is a complex problem with many factors — the life of the fuel, location of hot spots — affecting outcomes. Nuclear reactors change their configuration of fuel rods every 18-24 months to optimize close to 15-20 constraints, leading to roughly 200-800 assemblies. The mind-boggling nature of the problem means that plants have to rely on experienced engineers.

      During his internship, Shirvan optimized the program used to place fuel rods in the reactor. He found that certain rods in assemblies were more prone to the crud deposits, and reworked their configurations, optimizing for these rods’ performance instead of adding assemblies.

      In recent years, Shirvan has applied a branch of artificial intelligence — reinforcement learning — to the configuration problem and created a software program used by the largest U.S. nuclear utility. “This program gives even a layperson the ability to reconfigure the fuels and the reactor without having expert knowledge,” Shirvan says.

      From advanced math to counting jelly beans

      Shirvan’s own expertise in nuclear science and engineering developed quite organically. He grew up in Tehran, Iran, and when he was 14 the family moved to Gainesville, where Shirvan’s aunt and family live. He remembers an awkward couple of years at the new high school where he was grouped in with newly arrived international students, and placed in entry-level classes. “I went from doing advanced mathematics in Iran to counting jelly beans,” he laughs.

      Shirvan applied to the University of Florida for his undergraduate studies since it made economic sense; the school gave full scholarships to Floridian students who received a certain minimum SAT score. Shirvan qualified. His uncle, who was a professor in the nuclear engineering department then, encouraged Shirvan to take classes in the department. Under his uncle’s mentorship, the courses Shirvan took, and his internship, cemented his love of the interdisciplinary approach that the field demanded.

      Having always known that he wanted to teach — he remembers finishing his math tests early in Tehran so he could earn the reward of being class monitor — Shirvan knew graduate school was next. His uncle encouraged him to apply to MIT and to the University of Michigan, home to reputable programs in the field. Shirvan chose MIT because “only at MIT was there a program on nuclear design. There were faculty dedicated to designing new reactors, looking at multiple disciplines, and putting all of that together.” He went on to pursue his master’s and doctoral studies at NSE under the supervision of Professor Mujid Kazimi, focusing on compact pressurized and boiling water reactor designs. When Kazimi passed away suddenly in 2015, Shirvan was a research scientist, and switched to tenure track to guide the professor’s team.

      Another project that Shirvan took in 2015: leadership of MIT’s course on nuclear reactor technology for utility executives. Offered only by the Institute, the program is an introduction to nuclear engineering and safety for personnel who might not have much background in the area. “It’s a great course because you get to see what the real problems are in the energy sector … like grid stability,” Shirvan says.

      A multipronged approach to savings

      Another very real problem nuclear utilities face is cost. Contrary to what one hears on the news, one of the biggest stumbling blocks to building new nuclear facilities in the United States is cost, which today can be up to three times that of renewables, Shirvan says. While many approaches such as advanced manufacturing have been tried, Shirvan believes that the solution to decrease expenditures lies in designing more compact reactors.

      His team has developed an open-source advanced nuclear cost tool and has focused on two different designs: a small water reactor using compact steam technology and a horizontal gas reactor. Compactness also means making fuels more efficient, as Shirvan’s work does, and in improving the heat exchange device. It’s all back to the basics and bringing “commercial viable arguments in with your research,” Shirvan explains.

      Shirvan is excited about the future of the U.S. nuclear industry, and that the 2022 Inflation Reduction Act grants the same subsidies to nuclear as it does for renewables. In this new level playing field, advanced nuclear still has a long way to go in terms of affordability, he admits. “It’s time to push forward with cost-effective design,” Shirvan says, “I look forward to supporting this by continuing to guide these efforts with research from my team.” More

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      in Energy

      Machine learning facilitates “turbulence tracking” in fusion reactors

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      Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.

      A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions.

      Monitoring the formation and movements of these structures, called filaments or “blobs,” is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics. Individual blob information must be tracked by marking them manually in video data. 

      The researchers built a synthetic video dataset of plasma turbulence to make this process more effective and efficient. They used it to train four computer vision models, each of which identifies and tracks blobs. They trained the models to pinpoint blobs in the same ways that humans would.

      When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved.

      Because millions of video frames are captured during just one fusion experiment, using machine-learning models to track blobs could give scientists much more detailed information.

      “Before, we could get a macroscopic picture of what these structures are doing on average. Now, we have a microscope and the computational power to analyze one event at a time. If we take a step back, what this reveals is the power available from these machine-learning techniques, and ways to use these computational resources to make progress,” says Theodore Golfinopoulos, a research scientist at the MIT Plasma Science and Fusion Center and co-author of a paper detailing these approaches.

      His fellow co-authors include lead author Woonghee “Harry” Han, a physics PhD candidate; senior author Iddo Drori, a visiting professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL), faculty associate professor at Boston University, and adjunct at Columbia University; as well as others from the MIT Plasma Science and Fusion Center, the MIT Department of Civil and Environmental Engineering, and the Swiss Federal Institute of Technology at Lausanne in Switzerland. The research appears today in Nature Scientific Reports.

      Heating things up

      For more than 70 years, scientists have sought to use controlled thermonuclear fusion reactions to develop an energy source. To reach the conditions necessary for a fusion reaction, fuel must be heated to temperatures above 100 million degrees Celsius. (The core of the sun is about 15 million degrees Celsius.)

      A common method for containing this super-hot fuel, called plasma, is to use a tokamak. These devices utilize extremely powerful magnetic fields to hold the plasma in place and control the interaction between the exhaust heat from the plasma and the reactor walls.

      However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor.

      “Knowing what the blobs are doing strongly constrains the engineering performance that your tokamak power plant needs at the edge,” adds Golfinopoulos.

      Researchers use a unique imaging technique to capture video of the plasma’s turbulent edge during experiments. An experimental campaign may last months; a typical day will produce about 30 seconds of data, corresponding to roughly 60 million video frames, with thousands of blobs appearing each second. This makes it impossible to track all blobs manually, so researchers rely on average sampling techniques that only provide broad characteristics of blob size, speed, and frequency.

      “On the other hand, machine learning provides a solution to this by blob-by-blob tracking for every frame, not just average quantities. This gives us much more knowledge about what is happening at the boundary of the plasma,” Han says.

      He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.

      Simulating blobs

      To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.

      “Sometimes they change direction or speed, sometimes multiple blobs merge, or they split apart. These kinds of events were not considered before with traditional approaches, but we could freely simulate those behaviors in the synthetic data,” Han says.

      Creating synthetic data also allowed them to label each blob, which made the training process more effective, Drori adds.

      Using these synthetic data, they trained the models to draw boundaries around blobs, teaching them to closely mimic what a human scientist would draw.

      Then they tested the models using real video data from experiments. First, they measured how closely the boundaries the models drew matched up with actual blob contours.

      But they also wanted to see if the models predicted objects that humans would identify. They asked three human experts to pinpoint the centers of blobs in video frames and checked to see if the models predicted blobs in those same locations.

      The models were able to draw accurate blob boundaries, overlapping with brightness contours which are considered ground-truth, about 80 percent of the time. Their evaluations were similar to those of human experts, and successfully predicted the theory-defined regime of the blob, which agrees with the results from a traditional method.

      Now that they have shown the success of using synthetic data and computer vision models for tracking blobs, the researchers plan to apply these techniques to other problems in fusion research, such as estimating particle transport at the boundary of a plasma, Han says.

      They also made the dataset and models publicly available, and look forward to seeing how other research groups apply these tools to study the dynamics of blobs, says Drori.

      “Prior to this, there was a barrier to entry that mostly the only people working on this problem were plasma physicists, who had the datasets and were using their methods. There is a huge machine-learning and computer-vision community. One goal of this work is to encourage participation in fusion research from the broader machine-learning community toward the broader goal of helping solve the critical problem of climate change,” he adds.

      This research is supported, in part, by the U.S. Department of Energy and the Swiss National Science Foundation. More

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      in Energy

      Computing for the health of the planet

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      The health of the planet is one of the most important challenges facing humankind today. From climate change to unsafe levels of air and water pollution to coastal and agricultural land erosion, a number of serious challenges threaten human and ecosystem health.

      Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects. New computational methods can play a critical role by providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better-preserved biodiversity, and sustainable sources of energy.

      The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its climate action plan. This year the college undertook searches with several departments in the schools of Engineering and Science for shared faculty in computing for health of the planet, one of the six strategic areas of inquiry identified in an MIT-wide planning process to help focus shared hiring efforts. The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

      The searches are part of an ongoing effort by the MIT Schwarzman College of Computing to hire 50 new faculty — 25 shared with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in departments.

      Four interdisciplinary scholars were hired in these searches. They will join the MIT faculty in the coming year to engage in research and teaching that will advance physical understanding of low-carbon energy solutions, Earth-climate modeling, biodiversity monitoring and conservation, and agricultural management through high-performance computing, transformational numerical methods, and machine-learning techniques.

      “By coordinating hiring efforts with multiple departments and schools, we were able to attract a cohort of exceptional scholars in this area to MIT. Each of them is developing and using advanced computational methods and tools to help find solutions for a range of climate and environmental issues,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Ellis Professor of Electrical Engineering and Computer Science. “They will also help strengthen cross-departmental ties in computing across an important, critical area for MIT and the world.”

      “These strategic hires in the area of computing for climate and the environment are an incredible opportunity for the college to deepen its academic offerings and create new opportunity for collaboration across MIT,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “The college plays a pivotal role in MIT’s overarching effort to hire climate-focused faculty — introducing the critical role of computing to address the health of the planet through innovative research and curriculum.”

      The four new faculty members are:

      Sara Beery will join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023. Beery received her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide and works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.

      Priya Donti will join MIT as an assistant professor in the faculties of Electrical Engineering and Artificial Intelligence and Decision-Making in EECS in academic year 2023-24. Donti recently finished her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is also co-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning that is currently running through the Cornell Tech Runway Startup Postdoc Program.

      Ericmoore Jossou will join MIT as an assistant professor in a shared position between the Department of Nuclear Science and Engineering and the faculty of electrical engineering in EECS in July 2023. He is currently an assistant scientist at the Brookhaven National Laboratory, a U.S. Department of Energy-affiliated lab that conducts research in nuclear and high energy physics, energy science and technology, environmental and bioscience, nanoscience, and national security. His research at MIT will focus on understanding the processing-structure-properties correlation of materials for nuclear energy applications through advanced experiments, multiscale simulations, and data science. Jossou obtained his PhD in mechanical engineering in 2019 from the University of Saskatchewan.

      Sherrie Wang will join MIT as an assistant professor in a shared position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society in academic year 2023-24. Wang is currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California at Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for Earth observation data. Her primary application areas are improving agricultural management and forecasting climate phenomena. She obtained her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobell. More

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      in Energy

      Taking a magnifying glass to data center operations

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      When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner — and the team is looking for ways to improve.

      “We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can,” says Siddharth Samsi, a research scientist in the LLSC. 

      To gain insight into this challenge, the LLSC has been collecting detailed data on TX-GAIA usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community.

      Their goal is to empower computer scientists and data center operators to better understand avenues for data center optimization — an important task as processing needs continue to grow. They also see potential for leveraging AI in the data center itself, by using the data to develop models for predicting failure points, optimizing job scheduling, and improving energy efficiency. While cloud providers are actively working on optimizing their data centers, they do not often make their data or models available for the broader high-performance computing (HPC) community to leverage. The release of this dataset and associated code seeks to fill this space.

      “Data centers are changing. We have an explosion of hardware platforms, the types of workloads are evolving, and the types of people who are using data centers is changing,” says Vijay Gadepally, a senior researcher at the LLSC. “Until now, there hasn’t been a great way to analyze the impact to data centers. We see this research and dataset as a big step toward coming up with a principled approach to understanding how these variables interact with each other and then applying AI for insights and improvements.”

      Papers describing the dataset and potential applications have been accepted to a number of venues, including the IEEE International Symposium on High-Performance Computer Architecture, the IEEE International Parallel and Distributed Processing Symposium, the Annual Conference of the North American Chapter of the Association for Computational Linguistics, the IEEE High-Performance and Embedded Computing Conference, and International Conference for High Performance Computing, Networking, Storage and Analysis. 

      Workload classification

      Among the world’s TOP500 supercomputers, TX-GAIA combines traditional computing hardware (central processing units, or CPUs) with nearly 900 graphics processing unit (GPU) accelerators. These NVIDIA GPUs are specialized for deep learning, the class of AI that has given rise to speech recognition and computer vision.

      The dataset covers CPU, GPU, and memory usage by job; scheduling logs; and physical monitoring data. Compared to similar datasets, such as those from Google and Microsoft, the LLSC dataset offers “labeled data, a variety of known AI workloads, and more detailed time series data compared with prior datasets. To our knowledge, it’s one of the most comprehensive and fine-grained datasets available,” Gadepally says. 

      Notably, the team collected time-series data at an unprecedented level of detail: 100-millisecond intervals on every GPU and 10-second intervals on every CPU, as the machines processed more than 3,000 known deep-learning jobs. One of the first goals is to use this labeled dataset to characterize the workloads that different types of deep-learning jobs place on the system. This process would extract features that reveal differences in how the hardware processes natural language models versus image classification or materials design models, for example.   

      The team has now launched the MIT Datacenter Challenge to mobilize this research. The challenge invites researchers to use AI techniques to identify with 95 percent accuracy the type of job that was run, using their labeled time-series data as ground truth.

      Such insights could enable data centers to better match a user’s job request with the hardware best suited for it, potentially conserving energy and improving system performance. Classifying workloads could also allow operators to quickly notice discrepancies resulting from hardware failures, inefficient data access patterns, or unauthorized usage.

      Too many choices

      Today, the LLSC offers tools that let users submit their job and select the processors they want to use, “but it’s a lot of guesswork on the part of users,” Samsi says. “Somebody might want to use the latest GPU, but maybe their computation doesn’t actually need it and they could get just as impressive results on CPUs, or lower-powered machines.”

      Professor Devesh Tiwari at Northeastern University is working with the LLSC team to develop techniques that can help users match their workloads to appropriate hardware. Tiwari explains that the emergence of different types of AI accelerators, GPUs, and CPUs has left users suffering from too many choices. Without the right tools to take advantage of this heterogeneity, they are missing out on the benefits: better performance, lower costs, and greater productivity.

      “We are fixing this very capability gap — making users more productive and helping users do science better and faster without worrying about managing heterogeneous hardware,” says Tiwari. “My PhD student, Baolin Li, is building new capabilities and tools to help HPC users leverage heterogeneity near-optimally without user intervention, using techniques grounded in Bayesian optimization and other learning-based optimization methods. But, this is just the beginning. We are looking into ways to introduce heterogeneity in our data centers in a principled approach to help our users achieve the maximum advantage of heterogeneity autonomously and cost-effectively.”

      Workload classification is the first of many problems to be posed through the Datacenter Challenge. Others include developing AI techniques to predict job failures, conserve energy, or create job scheduling approaches that improve data center cooling efficiencies.

      Energy conservation 

      To mobilize research into greener computing, the team is also planning to release an environmental dataset of TX-GAIA operations, containing rack temperature, power consumption, and other relevant data.

      According to the researchers, huge opportunities exist to improve the power efficiency of HPC systems being used for AI processing. As one example, recent work in the LLSC determined that simple hardware tuning, such as limiting the amount of power an individual GPU can draw, could reduce the energy cost of training an AI model by 20 percent, with only modest increases in computing time. “This reduction translates to approximately an entire week’s worth of household energy for a mere three-hour time increase,” Gadepally says.

      They have also been developing techniques to predict model accuracy, so that users can quickly terminate experiments that are unlikely to yield meaningful results, saving energy. The Datacenter Challenge will share relevant data to enable researchers to explore other opportunities to conserve energy.

      The team expects that lessons learned from this research can be applied to the thousands of data centers operated by the U.S. Department of Defense. The U.S. Air Force is a sponsor of this work, which is being conducted under the USAF-MIT AI Accelerator.

      Other collaborators include researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Charles Leiserson’s Supertech Research Group is investigating performance-enhancing techniques for parallel computing, and research scientist Neil Thompson is designing studies on ways to nudge data center users toward climate-friendly behavior.

      Samsi presented this work at the inaugural AI for Datacenter Optimization (ADOPT’22) workshop last spring as part of the IEEE International Parallel and Distributed Processing Symposium. The workshop officially introduced their Datacenter Challenge to the HPC community.

      “We hope this research will allow us and others who run supercomputing centers to be more responsive to user needs while also reducing the energy consumption at the center level,” Samsi says. More

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      in Energy

      Building better batteries, faster

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      To help combat climate change, many car manufacturers are racing to add more electric vehicles in their lineups. But to convince prospective buyers, manufacturers need to improve how far these cars can go on a single charge. One of their main challenges? Figuring out how to make extremely powerful but lightweight batteries.

      Typically, however, it takes decades for scientists to thoroughly test new battery materials, says Pablo Leon, an MIT graduate student in materials science. To accelerate this process, Leon is developing a machine-learning tool for scientists to automate one of the most time-consuming, yet key, steps in evaluating battery materials.

      With his tool in hand, Leon plans to help search for new materials to enable the development of powerful and lightweight batteries. Such batteries would not only improve the range of EVs, but they could also unlock potential in other high-power systems, such as solar energy systems that continuously deliver power, even at night.

      From a young age, Leon knew he wanted to pursue a PhD, hoping to one day become a professor of engineering, like his father. Growing up in College Station, Texas, home to Texas A&M University, where his father worked, many of Leon’s friends also had parents who were professors or affiliated with the university. Meanwhile, his mom worked outside the university, as a family counselor in a neighboring city.

      In college, Leon followed in his father’s and older brother’s footsteps to become a mechanical engineer, earning his bachelor’s degree at Texas A&M. There, he learned how to model the behaviors of mechanical systems, such as a metal spring’s stiffness. But he wanted to delve deeper, down to the level of atoms, to understand exactly where these behaviors come from.

      So, when Leon applied to graduate school at MIT, he switched fields to materials science, hoping to satisfy his curiosity. But the transition to a different field was “a really hard process,” Leon says, as he rushed to catch up to his peers.

      To help with the transition, Leon sought out a congenial research advisor and found one in Rafael Gómez-Bombarelli, an assistant professor in the Department of Materials Science and Engineering (DMSE). “Because he’s from Spain and my parents are Peruvian, there’s a cultural ease with the way we talk,” Leon says. According to Gómez-Bombarelli, sometimes the two of them even discuss research in Spanish — a “rare treat.” That connection has empowered Leon to freely brainstorm ideas or talk through concerns with his advisor, enabling him to make significant progress in his research.

      Leveraging machine learning to research battery materials

      Scientists investigating new battery materials generally use computer simulations to understand how different combinations of materials perform. These simulations act as virtual microscopes for batteries, zooming in to see how materials interact at an atomic level. With these details, scientists can understand why certain combinations do better, guiding their search for high-performing materials.

      But building accurate computer simulations is extremely time-intensive, taking years and sometimes even decades. “You need to know how every atom interacts with every other atom in your system,” Leon says. To create a computer model of these interactions, scientists first make a rough guess at a model using complex quantum mechanics calculations. They then compare the model with results from real-life experiments, manually tweaking different parts of the model, including the distances between atoms and the strength of chemical bonds, until the simulation matches real life.

      With well-studied battery materials, the simulation process is somewhat easier. Scientists can buy simulation software that includes pre-made models, Leon says, but these models often have errors and still require additional tweaking.

      To build accurate computer models more quickly, Leon is developing a machine-learning-based tool that can efficiently guide the trial-and-error process. “The hope with our machine learning framework is to not have to rely on proprietary models or do any hand-tuning,” he says. Leon has verified that for well-studied materials, his tool is as accurate as the manual method for building models.

      With this system, scientists will have a single, standardized approach for building accurate models in lieu of the patchwork of approaches currently in place, Leon says.

      Leon’s tool comes at an opportune time, when many scientists are investigating a new paradigm of batteries: solid-state batteries. Compared to traditional batteries, which contain liquid electrolytes, solid-state batteries are safer, lighter, and easier to manufacture. But creating versions of these batteries that are powerful enough for EVs or renewable energy storage is challenging.

      This is largely because in battery chemistry, ions dislike flowing through solids and instead prefer liquids, in which atoms are spaced further apart. Still, scientists believe that with the right combination of materials, solid-state batteries can provide enough electricity for high-power systems, such as EVs. 

      Leon plans to use his machine-learning tool to help look for good solid-state battery materials more quickly. After he finds some powerful candidates in simulations, he’ll work with other scientists to test out the new materials in real-world experiments.

      Helping students navigate graduate school

      To get to where he is today, doing exciting and impactful research, Leon credits his community of family and mentors. Because of his upbringing, Leon knew early on which steps he would need to take to get into graduate school and work toward becoming a professor. And he appreciates the privilege of his position, even more so as a Peruvian American, given that many Latino students are less likely to have access to the same resources. “I understand the academic pipeline in a way that I think a lot of minority groups in academia don’t,” he says.

      Now, Leon is helping prospective graduate students from underrepresented backgrounds navigate the pipeline through the DMSE Application Assistance Program. Each fall, he mentors applicants for the DMSE PhD program at MIT, providing feedback on their applications and resumes. The assistance program is student-run and separate from the admissions process.

      Knowing firsthand how invaluable mentorship is from his relationship with his advisor, Leon is also heavily involved in mentoring junior PhD students in his department. This past year, he served as the academic chair on his department’s graduate student organization, the Graduate Materials Council. With MIT still experiencing disruptions from Covid-19, Leon noticed a problem with student cohesiveness. “I realized that traditional [informal] modes of communication across [incoming class] years had been cut off,” he says, making it harder for junior students to get advice from their senior peers. “They didn’t have any community to fall back on.”

      To help fix this problem, Leon served as a go-to mentor for many junior students. He helped second-year PhD students prepare for their doctoral qualification exam, an often-stressful rite of passage. He also hosted seminars for first-year students to teach them how to make the most of their classes and help them acclimate to the department’s fast-paced classes. For fun, Leon organized an axe-throwing event to further facilitate student cameraderie.

      Leon’s efforts were met with success. Now, “newer students are building back the community,” he says, “so I feel like I can take a step back” from being academic chair. He will instead continue mentoring junior students through other programs within the department. He also plans to extend his community-building efforts among faculty and students, facilitating opportunities for students to find good mentors and work on impactful research. With these efforts, Leon hopes to help others along the academic pipeline that he’s become familiar with, journeying together over their PhDs. More