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    Study: Shutting down nuclear power could increase air pollution

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    Nearly 20 percent of today’s electricity in the United States comes from nuclear power. The U.S. has the largest nuclear fleet in the world, with 92 reactors scattered around the country. Many of these power plants have run for more than half a century and are approaching the end of their expected lifetimes.

    Policymakers are debating whether to retire the aging reactors or reinforce their structures to continue producing nuclear energy, which many consider a low-carbon alternative to climate-warming coal, oil, and natural gas.

    Now, MIT researchers say there’s another factor to consider in weighing the future of nuclear power: air quality. In addition to being a low carbon-emitting source, nuclear power is relatively clean in terms of the air pollution it generates. Without nuclear power, how would the pattern of air pollution shift, and who would feel its effects?

    The MIT team took on these questions in a new study appearing today in Nature Energy. They lay out a scenario in which every nuclear power plant in the country has shut down, and consider how other sources such as coal, natural gas, and renewable energy would fill the resulting energy needs throughout an entire year.

    Their analysis reveals that indeed, air pollution would increase, as coal, gas, and oil sources ramp up to compensate for nuclear power’s absence. This in itself may not be surprising, but the team has put numbers to the prediction, estimating that the increase in air pollution would have serious health effects, resulting in an additional 5,200 pollution-related deaths over a single year.

    If, however, more renewable energy sources become available to supply the energy grid, as they are expected to by the year 2030, air pollution would be curtailed, though not entirely. The team found that even under this heartier renewable scenario, there is still a slight increase in air pollution in some parts of the country, resulting in a total of 260 pollution-related deaths over one year.

    When they looked at the populations directly affected by the increased pollution, they found that Black or African American communities — a disproportionate number of whom live near fossil-fuel plants — experienced the greatest exposure.

    “This adds one more layer to the environmental health and social impacts equation when you’re thinking about nuclear shutdowns, where the conversation often focuses on local risks due to accidents and mining or long-term climate impacts,” says lead author Lyssa Freese, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS).

    “In the debate over keeping nuclear power plants open, air quality has not been a focus of that discussion,” adds study author Noelle Selin, a professor in MIT’s Institute for Data, Systems, and Society (IDSS) and EAPS. “What we found was that air pollution from fossil fuel plants is so damaging, that anything that increases it, such as a nuclear shutdown, is going to have substantial impacts, and for some people more than others.”

    The study’s MIT-affiliated co-authors also include Principal Research Scientist Sebastian Eastham and Guillaume Chossière SM ’17, PhD ’20, along with Alan Jenn of the University of California at Davis.

    Future phase-outs

    When nuclear power plants have closed in the past, fossil fuel use increased in response. In 1985, the closure of reactors in Tennessee Valley prompted a spike in coal use, while the 2012 shutdown of a plant in California led to an increase in natural gas. In Germany, where nuclear power has almost completely been phased out, coal-fired power increased initially to fill the gap.

    Noting these trends, the MIT team wondered how the U.S. energy grid would respond if nuclear power were completely phased out.

    “We wanted to think about what future changes were expected in the energy grid,” Freese says. “We knew that coal use was declining, and there was a lot of work already looking at the impact of what that would have on air quality. But no one had looked at air quality and nuclear power, which we also noticed was on the decline.”

    In the new study, the team used an energy grid dispatch model developed by Jenn to assess how the U.S. energy system would respond to a shutdown of nuclear power. The model simulates the production of every power plant in the country and runs continuously to estimate, hour by hour, the energy demands in 64 regions across the country.

    Much like the way the actual energy market operates, the model chooses to turn a plant’s production up or down based on cost: Plants producing the cheapest energy at any given time are given priority to supply the grid over more costly energy sources.

    The team fed the model available data on each plant’s changing emissions and energy costs throughout an entire year. They then ran the model under different scenarios, including: an energy grid with no nuclear power, a baseline grid similar to today’s that includes nuclear power, and a grid with no nuclear power that also incorporates the additional renewable sources that are expected to be added by 2030.

    They combined each simulation with an atmospheric chemistry model to simulate how each plant’s various emissions travel around the country and to overlay these tracks onto maps of population density. For populations in the path of pollution, they calculated the risk of premature death based on their degree of exposure.

    System response

    Play video

    Courtesy of the researchers, edited by MIT News

    Their analysis showed a clear pattern: Without nuclear power, air pollution worsened in general, mainly affecting regions in the East Coast, where nuclear power plants are mostly concentrated. Without those plants, the team observed an uptick in production from coal and gas plants, resulting in 5,200 pollution-related deaths across the country, compared to the baseline scenario.

    They also calculated that more people are also likely to die prematurely due to climate impacts from the increase in carbon dioxide emissions, as the grid compensates for nuclear power’s absence. The climate-related effects from this additional influx of carbon dioxide could lead to 160,000 additional deaths over the next century.

    “We need to be thoughtful about how we’re retiring nuclear power plants if we are trying to think about them as part of an energy system,” Freese says. “Shutting down something that doesn’t have direct emissions itself can still lead to increases in emissions, because the grid system will respond.”

    “This might mean that we need to deploy even more renewables, in order to fill the hole left by nuclear, which is essentially a zero-emissions energy source,” Selin adds. “Otherwise we will have a reduction in air quality that we weren’t necessarily counting on.”

    This study was supported, in part, by the U.S. Environmental Protection Agency. More

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      Helping the cause of environmental resilience

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      Haruko Wainwright, the Norman C. Rasmussen Career Development Professor in Nuclear Science and Engineering (NSE) and assistant professor in civil and environmental engineering at MIT, grew up in rural Japan, where many nuclear facilities are located. She remembers worrying about the facilities as a child. Wainwright was only 6 at the time of the Chernobyl accident in 1986, but still recollects it vividly.

      Those early memories have contributed to Wainwright’s determination to research how technologies can mold environmental resilience — the capability of mitigating the consequences of accidents and recovering from contamination.

      Wainwright believes that environmental monitoring can help improve resilience. She co-leads the U.S. Department of Energy (DOE)’s Advanced Long-term Environmental Monitoring Systems (ALTEMIS) project, which integrates technologies such as in situ sensors, geophysics, remote sensing, simulations, and artificial intelligence to establish new paradigms for monitoring. The project focuses on soil and groundwater contamination at more than 100 U.S. sites that were used for nuclear weapons production.

      As part of this research, which was featured last year in Environmental Science & Technology Journal, Wainwright is working on a machine learning framework for improving environmental monitoring strategies. She hopes the ALTEMIS project will enable the rapid detection of anomalies while ensuring the stability of residual contamination and waste disposal facilities.

      Childhood in rural Japan

      Even as a child, Wainwright was interested in physics, history, and a variety of other subjects.

      But growing up in a rural area was not ideal for someone interested in STEM. There were no engineers or scientists in the community and no science museums, either. “It was not so cool to be interested in science, and I never talked about my interest with anyone,” Wainwright recalls.

      Television and books were the only door to the world of science. “I did not study English until middle school and I had never been on a plane until college. I sometimes find it miraculous that I am now working in the U.S. and teaching at MIT,” she says.

      As she grew a little older, Wainwright heard a lot of discussions about nuclear facilities in the region and many stories about Hiroshima and Nagasaki.

      At the same time, giants like Marie Curie inspired her to pursue science. Nuclear physics was particularly fascinating. “At some point during high school, I started wondering ‘what are radiations, what is radioactivity, what is light,’” she recalls. Reading Richard Feynman’s books and trying to understand quantum mechanics made her want to study physics in college.

      Pursuing research in the United States

      Wainwright pursued an undergraduate degree in engineering physics at Kyoto University. After two research internships in the United States, Wainwright was impressed by the dynamic and fast-paced research environment in the country.

      And compared to Japan, there were “more women in science and engineering,” Wainwright says. She enrolled at the University of California at Berkeley in 2005, where she completed her doctorate in nuclear engineering with minors in statistics and civil and environmental engineering.

      Before moving to MIT NSE in 2022, Wainwright was a staff scientist in the Earth and Environmental Area at Lawrence Berkeley National Laboratory (LBNL). She worked on a variety of topics, including radioactive contamination, climate science, CO2 sequestration, precision agriculture, and watershed science. Her time at LBNL helped Wainwright build a solid foundation about a variety of environmental sensors and monitoring and simulation methods across different earth science disciplines.   

      Empowering communities through monitoring

      One of the most compelling takeaways from Wainwright’s early research: People trust actual measurements and data as facts, even though they are skeptical about models and predictions. “I talked with many people living in Fukushima prefecture. Many of them have dosimeters and measure radiation levels on their own. They might not trust the government, but they trust their own data and are then convinced that it is safe to live there and to eat local food,” Wainwright says.

      She has been impressed that area citizens have gained significant knowledge about radiation and radioactivity through these efforts. “But they are often frustrated that people living far away, in cities like Tokyo, still avoid agricultural products from Fukushima,” Wainwright says.

      Wainwright thinks that data derived from environmental monitoring — through proper visualization and communication — can address misconceptions and fake news that often hurt people near contaminated sites.

      Wainwright is now interested in how these technologies — tested with real data at contaminated sites — can be proactively used for existing and future nuclear facilities “before contamination happens,” as she explored for Nuclear News. “I don’t think it is a good idea to simply dismiss someone’s concern as irrational. Showing credible data has been much more effective to provide assurance. Or a proper monitoring network would enable us to minimize contamination or support emergency responses when accidents happen,” she says.

      Educating communities and students

      Part of empowering communities involves improving their ability to process science-based information. “Potentially hazardous facilities always end up in rural regions; minorities’ concerns are often ignored. The problem is that these regions don’t produce so many scientists or policymakers; they don’t have a voice,” Wainwright says, “I am determined to dedicate my time to improve STEM education in rural regions and to increase the voice in these regions.”

      In a project funded by DOE, she collaborates with the team of researchers at the University of Alaska — the Alaska Center for Energy and Power and Teaching Through Technology program — aiming to improve STEM education for rural and indigenous communities. “Alaska is an important place for energy transition and environmental justice,” Wainwright says. Micro-nuclear reactors can potentially improve the life of rural communities who bear the brunt of the high cost of fuel and transportation. However, there is a distrust of nuclear technologies, stemming from past nuclear weapon testing. At the same time, Alaska has vast metal mining resources for renewable energy and batteries. And there are concerns about environmental contamination from mining and various sources. The teams’ vision is much broader, she points out. “The focus is on broader environmental monitoring technologies and relevant STEM education, addressing general water and air qualities,” Wainwright says.

      The issues also weave into the courses Wainwright teaches at MIT. “I think it is important for engineering students to be aware of environmental justice related to energy waste and mining as well as past contamination events and their recovery,” she says. “It is not OK just to send waste to, or develop mines in, rural regions, which could be a special place for some people. We need to make sure that these developments will not harm the environment and health of local communities.” Wainwright also hopes that this knowledge will ultimately encourage students to think creatively about engineering designs that minimize waste or recycle material.

      The last question of the final quiz of one of her recent courses was: Assume that you store high-level radioactive waste in your “backyard.” What technical strategies would make you and your family feel safe? “All students thought about this question seriously and many suggested excellent points, including those addressing environmental monitoring,” Wainwright says, “that made me hopeful about the future.” More

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      Minimizing electric vehicles’ impact on the grid

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      National and global plans to combat climate change include increasing the electrification of vehicles and the percentage of electricity generated from renewable sources. But some projections show that these trends might require costly new power plants to meet peak loads in the evening when cars are plugged in after the workday. What’s more, overproduction of power from solar farms during the daytime can waste valuable electricity-generation capacity.

      In a new study, MIT researchers have found that it’s possible to mitigate or eliminate both these problems without the need for advanced technological systems of connected devices and real-time communications, which could add to costs and energy consumption. Instead, encouraging the placing of charging stations for electric vehicles (EVs) in strategic ways, rather than letting them spring up anywhere, and setting up systems to initiate car charging at delayed times could potentially make all the difference.

      The study, published today in the journal Cell Reports Physical Science, is by Zachary Needell PhD ’22, postdoc Wei Wei, and Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society.

      In their analysis, the researchers used data collected in two sample cities: New York and Dallas. The data were gathered from, among other sources, anonymized records collected via onboard devices in vehicles, and surveys that carefully sampled populations to cover variable travel behaviors. They showed the times of day cars are used and for how long, and how much time the vehicles spend at different kinds of locations — residential, workplace, shopping, entertainment, and so on.

      The findings, Trancik says, “round out the picture on the question of where to strategically locate chargers to support EV adoption and also support the power grid.”

      Better availability of charging stations at workplaces, for example, could help to soak up peak power being produced at midday from solar power installations, which might otherwise go to waste because it is not economical to build enough battery or other storage capacity to save all of it for later in the day. Thus, workplace chargers can provide a double benefit, helping to reduce the evening peak load from EV charging and also making use of the solar electricity output.

      These effects on the electric power system are considerable, especially if the system must meet charging demands for a fully electrified personal vehicle fleet alongside the peaks in other demand for electricity, for example on the hottest days of the year. If unmitigated, the evening peaks in EV charging demand could require installing upwards of 20 percent more power-generation capacity, the researchers say.

      “Slow workplace charging can be more preferable than faster charging technologies for enabling a higher utilization of midday solar resources,” Wei says.

      Meanwhile, with delayed home charging, each EV charger could be accompanied by a simple app to estimate the time to begin its charging cycle so that it charges just before it is needed the next day. Unlike other proposals that require a centralized control of the charging cycle, such a system needs no interdevice communication of information and can be preprogrammed — and can accomplish a major shift in the demand on the grid caused by increasing EV penetration. The reason it works so well, Trancik says, is because of the natural variability in driving behaviors across individuals in a population.

      By “home charging,” the researchers aren’t only referring to charging equipment in individual garages or parking areas. They say it’s essential to make charging stations available in on-street parking locations and in apartment building parking areas as well.

      Trancik says the findings highlight the value of combining the two measures — workplace charging and delayed home charging — to reduce peak electricity demand, store solar energy, and conveniently meet drivers’ charging needs on all days. As the team showed in earlier research, home charging can be a particularly effective component of a strategic package of charging locations; workplace charging, they have found, is not a good substitute for home charging for meeting drivers’ needs on all days.

      “Given that there’s a lot of public money going into expanding charging infrastructure,” Trancik says, “how do you incentivize the location such that this is going to be efficiently and effectively integrated into the power grid without requiring a lot of additional capacity expansion?” This research offers some guidance to policymakers on where to focus rules and incentives.

      “I think one of the fascinating things about these findings is that by being strategic you can avoid a lot of physical infrastructure that you would otherwise need,” she adds. “Your electric vehicles can displace some of the need for stationary energy storage, and you can also avoid the need to expand the capacity of power plants, by thinking about the location of chargers as a tool for managing demands — where they occur and when they occur.”

      Delayed home charging could make a surprising amount of difference, the team found. “It’s basically incentivizing people to begin charging later. This can be something that is preprogrammed into your chargers. You incentivize people to delay the onset of charging by a bit, so that not everyone is charging at the same time, and that smooths out the peak.”

      Such a program would require some advance commitment on the part of participants. “You would need to have enough people committing to this program in advance to avoid the investment in physical infrastructure,” Trancik says. “So, if you have enough people signing up, then you essentially don’t have to build those extra power plants.”

      It’s not a given that all of this would line up just right, and putting in place the right mix of incentives would be crucial. “If you want electric vehicles to act as an effective storage technology for solar energy, then the [EV] market needs to grow fast enough in order to be able to do that,” Trancik says.

      To best use public funds to help make that happen, she says, “you can incentivize charging installations, which would go through ideally a competitive process — in the private sector, you would have companies bidding for different projects, but you can incentivize installing charging at workplaces, for example, to tap into both of these benefits.” Chargers people can access when they are parked near their residences are also important, Trancik adds, but for other reasons. Home charging is one of the ways to meet charging needs while avoiding inconvenient disruptions to people’s travel activities.

      The study was supported by the European Regional Development Fund Operational Program for Competitiveness and Internationalization, the Lisbon Portugal Regional Operation Program, and the Portuguese Foundation for Science and Technology. More

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      A healthy wind

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      Nearly 10 percent of today’s electricity in the United States comes from wind power. The renewable energy source benefits climate, air quality, and public health by displacing emissions of greenhouse gases and air pollutants that would otherwise be produced by fossil-fuel-based power plants.

      A new MIT study finds that the health benefits associated with wind power could more than quadruple if operators prioritized turning down output from the most polluting fossil-fuel-based power plants when energy from wind is available.

      In the study, published today in Science Advances, researchers analyzed the hourly activity of wind turbines, as well as the reported emissions from every fossil-fuel-based power plant in the country, between the years 2011 and 2017. They traced emissions across the country and mapped the pollutants to affected demographic populations. They then calculated the regional air quality and associated health costs to each community.

      The researchers found that in 2014, wind power that was associated with state-level policies improved air quality overall, resulting in $2 billion in health benefits across the country. However, only roughly 30 percent of these health benefits reached disadvantaged communities.

      The team further found that if the electricity industry were to reduce the output of the most polluting fossil-fuel-based power plants, rather than the most cost-saving plants, in times of wind-generated power, the overall health benefits could quadruple to $8.4 billion nationwide. However, the results would have a similar demographic breakdown.

      “We found that prioritizing health is a great way to maximize benefits in a widespread way across the U.S., which is a very positive thing. But it suggests it’s not going to address disparities,” says study co-author Noelle Selin, a professor in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences at MIT. “In order to address air pollution disparities, you can’t just focus on the electricity sector or renewables and count on the overall air pollution benefits addressing these real and persistent racial and ethnic disparities. You’ll need to look at other air pollution sources, as well as the underlying systemic factors that determine where plants are sited and where people live.”

      Selin’s co-authors are lead author and former MIT graduate student Minghao Qiu PhD ’21, now at Stanford University, and Corwin Zigler at the University of Texas at Austin.

      Turn-down service

      In their new study, the team looked for patterns between periods of wind power generation and the activity of fossil-fuel-based power plants, to see how regional electricity markets adjusted the output of power plants in response to influxes of renewable energy.

      “One of the technical challenges, and the contribution of this work, is trying to identify which are the power plants that respond to this increasing wind power,” Qiu notes.

      To do so, the researchers compared two historical datasets from the period between 2011 and 2017: an hour-by-hour record of energy output of wind turbines across the country, and a detailed record of emissions measurements from every fossil-fuel-based power plant in the U.S. The datasets covered each of seven major regional electricity markets, each market providing energy to one or multiple states.

      “California and New York are each their own market, whereas the New England market covers around seven states, and the Midwest covers more,” Qiu explains. “We also cover about 95 percent of all the wind power in the U.S.”

      In general, they observed that, in times when wind power was available, markets adjusted by essentially scaling back the power output of natural gas and sub-bituminous coal-fired power plants. They noted that the plants that were turned down were likely chosen for cost-saving reasons, as certain plants were less costly to turn down than others.

      The team then used a sophisticated atmospheric chemistry model to simulate the wind patterns and chemical transport of emissions across the country, and determined where and at what concentrations the emissions generated fine particulates and ozone — two pollutants that are known to damage air quality and human health. Finally, the researchers mapped the general demographic populations across the country, based on U.S. census data, and applied a standard epidemiological approach to calculate a population’s health cost as a result of their pollution exposure.

      This analysis revealed that, in the year 2014, a general cost-saving approach to displacing fossil-fuel-based energy in times of wind energy resulted in $2 billion in health benefits, or savings, across the country. A smaller share of these benefits went to disadvantaged populations, such as communities of color and low-income communities, though this disparity varied by state.

      “It’s a more complex story than we initially thought,” Qiu says. “Certain population groups are exposed to a higher level of air pollution, and those would be low-income people and racial minority groups. What we see is, developing wind power could reduce this gap in certain states but further increase it in other states, depending on which fossil-fuel plants are displaced.”

      Tweaking power

      The researchers then examined how the pattern of emissions and the associated health benefits would change if they prioritized turning down different fossil-fuel-based plants in times of wind-generated power. They tweaked the emissions data to reflect several alternative scenarios: one in which the most health-damaging, polluting power plants are turned down first; and two other scenarios in which plants producing the most sulfur dioxide and carbon dioxide respectively, are first to reduce their output.

      They found that while each scenario increased health benefits overall, and the first scenario in particular could quadruple health benefits, the original disparity persisted: Communities of color and low-income communities still experienced smaller health benefits than more well-off communities.

      “We got to the end of the road and said, there’s no way we can address this disparity by being smarter in deciding which plants to displace,” Selin says.

      Nevertheless, the study can help identify ways to improve the health of the general population, says Julian Marshall, a professor of environmental engineering at the University of Washington.

      “The detailed information provided by the scenarios in this paper can offer a roadmap to electricity-grid operators and to state air-quality regulators regarding which power plants are highly damaging to human health and also are likely to noticeably reduce emissions if wind-generated electricity increases,” says Marshall, who was not involved in the study.

      “One of the things that makes me optimistic about this area is, there’s a lot more attention to environmental justice and equity issues,” Selin concludes. “Our role is to figure out the strategies that are most impactful in addressing those challenges.”

      This work was supported, in part, by the U.S. Environmental Protection Agency, and by the National Institutes of Health. More

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      A breakthrough on “loss and damage,” but also disappointment, at UN climate conference

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      As the 2022 United Nations climate change conference, known as COP27, stretched into its final hours on Saturday, Nov. 19, it was uncertain what kind of agreement might emerge from two weeks of intensive international negotiations.

      In the end, COP27 produced mixed results: on the one hand, a historic agreement for wealthy countries to compensate low-income countries for “loss and damage,” but on the other, limited progress on new plans for reducing the greenhouse gas emissions that are warming the planet.

      “We need to drastically reduce emissions now — and this is an issue this COP did not address,” said U.N. Secretary-General António Guterres in a statement at the conclusion of COP27. “A fund for loss and damage is essential — but it’s not an answer if the climate crisis washes a small island state off the map — or turns an entire African country to desert.”

      Throughout the two weeks of the conference, a delegation of MIT students, faculty, and staff was at the Sharm El-Sheikh International Convention Center to observe the negotiations, conduct and share research, participate in panel discussions, and forge new connections with researchers, policymakers, and advocates from around the world.

      Loss and damage

      A key issue coming in to COP27 (COP stands for “conference of the parties” to the U.N. Framework Convention on Climate Change, held for the 27th time) was loss and damage: a term used by the U.N. to refer to harms caused by climate change — either through acute catastrophes like extreme weather events or slower-moving impacts like sea level rise — to which communities and countries are unable to adapt. 

      Ultimately, a deal on loss and damage proved to be COP27’s most prominent accomplishment. Negotiators reached an eleventh-hour agreement to “establish new funding arrangements for assisting developing countries that are particularly vulnerable to the adverse effects of climate change.” 

      “Providing financial assistance to developing countries so they can better respond to climate-related loss and damage is not only a moral issue, but also a pragmatic one,” said Michael Mehling, deputy director of the MIT Center for Energy and Environmental Policy Research, who attended COP27 and participated in side events. “Future emissions growth will be squarely centered in the developing world, and offering support through different channels is key to building the trust needed for more robust global cooperation on mitigation.”

      Youssef Shaker, a graduate student in the MIT Technology and Policy Program and a research assistant with the MIT Energy Initiative, attended the second week of the conference, where he followed the negotiations over loss and damage closely. 

      “While the creation of a fund is certainly an achievement,” Shaker said, “significant questions remain to be answered, such as the size of the funding available as well as which countries receive access to it.” A loss-and-damage fund that is not adequately funded, Shaker noted, “would not be an impactful outcome.” 

      The agreement on loss and damage created a new committee, made up of 24 country representatives, to “operationalize” the new funding arrangements, including identifying funding sources. The committee is tasked with delivering a set of recommendations at COP28, which will take place next year in Dubai.

      Advising the U.N. on net zero

      Though the decisions reached at COP27 did not include major new commitments on reducing emissions from the combustion of fossil fuels, the transition to a clean global energy system was nevertheless a key topic of conversation throughout the conference.

      The Council of Engineers for the Energy Transition (CEET), an independent, international body of engineers and energy systems experts formed to provide advice to the U.N. on achieving net-zero emissions globally by 2050, convened for the first time at COP27. Jessika Trancik, a professor in the MIT Institute for Data, Systems, and Society and a member of CEET, spoke on a U.N.-sponsored panel on solutions for the transition to clean energy.

      Trancik noted that the energy transition will look different in different regions of the world. “As engineers, we need to understand those local contexts and design solutions around those local contexts — that’s absolutely essential to support a rapid and equitable energy transition.”

      At the same time, Trancik noted that there is now a set of “low-cost, ready-to-scale tools” available to every region — tools that resulted from a globally competitive process of innovation, stimulated by public policies in different countries, that dramatically drove down the costs of technologies like solar energy and lithium-ion batteries. The key, Trancik said, is for regional transition strategies to “tap into global processes of innovation.”

      Reinventing climate adaptation

      Elfatih Eltahir, the H. M. King Bhumibol Professor of Hydrology and Climate, traveled to COP27 to present plans for the Jameel Observatory Climate Resilience Early Warning System (CREWSnet), one of the five projects selected in April 2022 as a flagship in MIT’s Climate Grand Challenges initiative. CREWSnet focuses on climate adaptation, the term for adapting to climate impacts that are unavoidable.

      The aim of CREWSnet, Eltahir told the audience during a panel discussion, is “nothing short of reinventing the process of climate change adaptation,” so that it is proactive rather than reactive; community-led; data-driven and evidence-based; and so that it integrates different climate risks, from heat waves to sea level rise, rather than treating them individually.

      “However, it’s easy to talk about these changes,” said Eltahir. “The real challenge, which we are now just launching and engaging in, is to demonstrate that on the ground.” Eltahir said that early demonstrations will happen in a couple of key locations, including southwest Bangladesh, where multiple climate risks — rising sea levels, increasing soil salinity, and intensifying heat waves and cyclones — are combining to threaten the area’s agricultural production.

      Building on COP26

      Some members of MIT’s delegation attended COP27 to advance efforts that had been formally announced at last year’s U.N. climate conference, COP26, in Glasgow, Scotland.

      At an official U.N. side event co-organized by MIT on Nov. 11, Greg Sixt, the director of the Food and Climate Systems Transformation (FACT) Alliance led by the Abdul Latif Jameel Water and Food Systems Lab, provided an update on the alliance’s work since its launch at COP26.

      Food systems are a major source of greenhouse gas emissions — and are increasingly vulnerable to climate impacts. The FACT Alliance works to better connect researchers to farmers, food businesses, policymakers, and other food systems stakeholders to make food systems (which include food production, consumption, and waste) more sustainable and resilient. 

      Sixt told the audience that the FACT Alliance now counts over 20 research and stakeholder institutions around the world among its members, but also collaborates with other institutions in an “open network model” to advance work in key areas — such as a new research project exploring how climate scenarios could affect global food supply chains.

      Marcela Angel, research program director for the Environmental Solutions Initiative (ESI), helped convene a meeting at COP27 of the Afro-InterAmerican Forum on Climate Change, which also launched at COP26. The forum works with Afro-descendant leaders across the Americas to address significant environmental issues, including climate risks and biodiversity loss. 

      At the event — convened with the Colombian government and the nonprofit Conservation International — ESI brought together leaders from six countries in the Americas and presented recent work that estimates that there are over 178 million individuals who identify as Afro-descendant living in the Americas, in lands of global environmental importance. 

      “There is a significant overlap between biodiversity hot spots, protected areas, and areas of high Afro-descendant presence,” said Angel. “But the role and climate contributions of these communities is understudied, and often made invisible.”    

      Limiting methane emissions

      Methane is a short-lived but potent greenhouse gas: When released into the atmosphere, it immediately traps about 120 times more heat than carbon dioxide does. More than 150 countries have now signed the Global Methane Pledge, launched at COP26, which aims to reduce methane emissions by at least 30 percent by 2030 compared to 2020 levels.

      Sergey Paltsev, the deputy director of the Joint Program on the Science and Policy of Global Change and a senior research scientist at the MIT Energy Initiative, gave the keynote address at a Nov. 17 event on methane, where he noted the importance of methane reductions from the oil and gas sector to meeting the 2030 goal.

      “The oil and gas sector is where methane emissions reductions could be achieved the fastest,” said Paltsev. “We also need to employ an integrated approach to address methane emissions in all sectors and all regions of the world because methane emissions reductions provide a near-term pathway to avoiding dangerous tipping points in the global climate system.”

      “Keep fighting relentlessly”

      Arina Khotimsky, a senior majoring in materials science and engineering and a co-president of the MIT Energy and Climate Club, attended the first week of COP27. She reflected on the experience in a social media post after returning home. 

      “COP will always have its haters. Is there greenwashing? Of course! Is everyone who should have a say in this process in the room? Not even close,” wrote Khotimsky. “So what does it take for COP to matter? It takes everyone who attended to not only put ‘climate’ on front-page news for two weeks, but to return home and keep fighting relentlessly against climate change. I know that I will.” More

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      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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      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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      MIT announces five flagship projects in first-ever Climate Grand Challenges competition

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      MIT today announced the five flagship projects selected in its first-ever Climate Grand Challenges competition. These multiyear projects will define a dynamic research agenda focused on unraveling some of the toughest unsolved climate problems and bringing high-impact, science-based solutions to the world on an accelerated basis.

      Representing the most promising concepts to emerge from the two-year competition, the five flagship projects will receive additional funding and resources from MIT and others to develop their ideas and swiftly transform them into practical solutions at scale.

      “Climate Grand Challenges represents a whole-of-MIT drive to develop game-changing advances to confront the escalating climate crisis, in time to make a difference,” says MIT President L. Rafael Reif. “We are inspired by the creativity and boldness of the flagship ideas and by their potential to make a significant contribution to the global climate response. But given the planet-wide scale of the challenge, success depends on partnership. We are eager to work with visionary leaders in every sector to accelerate this impact-oriented research, implement serious solutions at scale, and inspire others to join us in confronting this urgent challenge for humankind.”

      Brief descriptions of the five Climate Grand Challenges flagship projects are provided below.

      Bringing Computation to the Climate Challenge

      This project leverages advances in artificial intelligence, machine learning, and data sciences to improve the accuracy of climate models and make them more useful to a variety of stakeholders — from communities to industry. The team is developing a digital twin of the Earth that harnesses more data than ever before to reduce and quantify uncertainties in climate projections.

      Research leads: Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in the Department of Earth, Atmospheric and Planetary Sciences, and director of the Program in Atmospheres, Oceans, and Climate; and Noelle Eckley Selin, director of the Technology and Policy Program and professor with a joint appointment in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences

      Center for Electrification and Decarbonization of Industry

      This project seeks to reinvent and electrify the processes and materials behind hard-to-decarbonize industries like steel, cement, ammonia, and ethylene production. A new innovation hub will perform targeted fundamental research and engineering with urgency, pushing the technological envelope on electricity-driven chemical transformations.

      Research leads: Yet-Ming Chiang, the Kyocera Professor of Materials Science and Engineering, and Bilge Yıldız, the Breene M. Kerr Professor in the Department of Nuclear Science and Engineering and professor in the Department of Materials Science and Engineering

      Preparing for a new world of weather and climate extremes

      This project addresses key gaps in knowledge about intensifying extreme events such as floods, hurricanes, and heat waves, and quantifies their long-term risk in a changing climate. The team is developing a scalable climate-change adaptation toolkit to help vulnerable communities and low-carbon energy providers prepare for these extreme weather events.

      Research leads: Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in the Department of Earth, Atmospheric and Planetary Sciences and co-director of the MIT Lorenz Center; Miho Mazereeuw, associate professor of architecture and urbanism in the Department of Architecture and director of the Urban Risk Lab; and Paul O’Gorman, professor in the Program in Atmospheres, Oceans, and Climate in the Department of Earth, Atmospheric and Planetary Sciences

      The Climate Resilience Early Warning System

      The CREWSnet project seeks to reinvent climate change adaptation with a novel forecasting system that empowers underserved communities to interpret local climate risk, proactively plan for their futures incorporating resilience strategies, and minimize losses. CREWSnet will initially be demonstrated in southwestern Bangladesh, serving as a model for similarly threatened regions around the world.

      Research leads: John Aldridge, assistant leader of the Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory, and Elfatih Eltahir, the H.M. King Bhumibol Professor of Hydrology and Climate in the Department of Civil and Environmental Engineering

      Revolutionizing agriculture with low-emissions, resilient crops

      This project works to revolutionize the agricultural sector with climate-resilient crops and fertilizers that have the ability to dramatically reduce greenhouse gas emissions from food production.

      Research lead: Christopher Voigt, the Daniel I.C. Wang Professor in the Department of Biological Engineering

      “As one of the world’s leading institutions of research and innovation, it is incumbent upon MIT to draw on our depth of knowledge, ingenuity, and ambition to tackle the hard climate problems now confronting the world,” says Richard Lester, MIT associate provost for international activities. “Together with collaborators across industry, finance, community, and government, the Climate Grand Challenges teams are looking to develop and implement high-impact, path-breaking climate solutions rapidly and at a grand scale.”

      The initial call for ideas in 2020 yielded nearly 100 letters of interest from almost 400 faculty members and senior researchers, representing 90 percent of MIT departments. After an extensive evaluation, 27 finalist teams received a total of $2.7 million to develop comprehensive research and innovation plans. The projects address four broad research themes:

      To select the winning projects, research plans were reviewed by panels of international experts representing relevant scientific and technical domains as well as experts in processes and policies for innovation and scalability.

      “In response to climate change, the world really needs to do two things quickly: deploy the solutions we already have much more widely, and develop new solutions that are urgently needed to tackle this intensifying threat,” says Maria Zuber, MIT vice president for research. “These five flagship projects exemplify MIT’s strong determination to bring its knowledge and expertise to bear in generating new ideas and solutions that will help solve the climate problem.”

      “The Climate Grand Challenges flagship projects set a new standard for inclusive climate solutions that can be adapted and implemented across the globe,” says MIT Chancellor Melissa Nobles. “This competition propels the entire MIT research community — faculty, students, postdocs, and staff — to act with urgency around a worsening climate crisis, and I look forward to seeing the difference these projects can make.”

      “MIT’s efforts on climate research amid the climate crisis was a primary reason that I chose to attend MIT, and remains a reason that I view the Institute favorably. MIT has a clear opportunity to be a thought leader in the climate space in our own MIT way, which is why CGC fits in so well,” says senior Megan Xu, who served on the Climate Grand Challenges student committee and is studying ways to make the food system more sustainable.

      The Climate Grand Challenges competition is a key initiative of “Fast Forward: MIT’s Climate Action Plan for the Decade,” which the Institute published in May 2021. Fast Forward outlines MIT’s comprehensive plan for helping the world address the climate crisis. It consists of five broad areas of action: sparking innovation, educating future generations, informing and leveraging government action, reducing MIT’s own climate impact, and uniting and coordinating all of MIT’s climate efforts. More

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      Using artificial intelligence to find anomalies hiding in massive datasets

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      Identifying a malfunction in the nation’s power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second.

      Researchers at the MIT-IBM Watson AI Lab have devised a computationally efficient method that can automatically pinpoint anomalies in those data streams in real time. They demonstrated that their artificial intelligence method, which learns to model the interconnectedness of the power grid, is much better at detecting these glitches than some other popular techniques.

      Because the machine-learning model they developed does not require annotated data on power grid anomalies for training, it would be easier to apply in real-world situations where high-quality, labeled datasets are often hard to come by. The model is also flexible and can be applied to other situations where a vast number of interconnected sensors collect and report data, like traffic monitoring systems. It could, for example, identify traffic bottlenecks or reveal how traffic jams cascade.

      “In the case of a power grid, people have tried to capture the data using statistics and then define detection rules with domain knowledge to say that, for example, if the voltage surges by a certain percentage, then the grid operator should be alerted. Such rule-based systems, even empowered by statistical data analysis, require a lot of labor and expertise. We show that we can automate this process and also learn patterns from the data using advanced machine-learning techniques,” says senior author Jie Chen, a research staff member and manager of the MIT-IBM Watson AI Lab.

      The co-author is Enyan Dai, an MIT-IBM Watson AI Lab intern and graduate student at the Pennsylvania State University. This research will be presented at the International Conference on Learning Representations.

      Probing probabilities

      The researchers began by defining an anomaly as an event that has a low probability of occurring, like a sudden spike in voltage. They treat the power grid data as a probability distribution, so if they can estimate the probability densities, they can identify the low-density values in the dataset. Those data points which are least likely to occur correspond to anomalies.

      Estimating those probabilities is no easy task, especially since each sample captures multiple time series, and each time series is a set of multidimensional data points recorded over time. Plus, the sensors that capture all that data are conditional on one another, meaning they are connected in a certain configuration and one sensor can sometimes impact others.

      To learn the complex conditional probability distribution of the data, the researchers used a special type of deep-learning model called a normalizing flow, which is particularly effective at estimating the probability density of a sample.

      They augmented that normalizing flow model using a type of graph, known as a Bayesian network, which can learn the complex, causal relationship structure between different sensors. This graph structure enables the researchers to see patterns in the data and estimate anomalies more accurately, Chen explains.

      “The sensors are interacting with each other, and they have causal relationships and depend on each other. So, we have to be able to inject this dependency information into the way that we compute the probabilities,” he says.

      This Bayesian network factorizes, or breaks down, the joint probability of the multiple time series data into less complex, conditional probabilities that are much easier to parameterize, learn, and evaluate. This allows the researchers to estimate the likelihood of observing certain sensor readings, and to identify those readings that have a low probability of occurring, meaning they are anomalies.

      Their method is especially powerful because this complex graph structure does not need to be defined in advance — the model can learn the graph on its own, in an unsupervised manner.

      A powerful technique

      They tested this framework by seeing how well it could identify anomalies in power grid data, traffic data, and water system data. The datasets they used for testing contained anomalies that had been identified by humans, so the researchers were able to compare the anomalies their model identified with real glitches in each system.

      Their model outperformed all the baselines by detecting a higher percentage of true anomalies in each dataset.

      “For the baselines, a lot of them don’t incorporate graph structure. That perfectly corroborates our hypothesis. Figuring out the dependency relationships between the different nodes in the graph is definitely helping us,” Chen says.

      Their methodology is also flexible. Armed with a large, unlabeled dataset, they can tune the model to make effective anomaly predictions in other situations, like traffic patterns.

      Once the model is deployed, it would continue to learn from a steady stream of new sensor data, adapting to possible drift of the data distribution and maintaining accuracy over time, says Chen.

      Though this particular project is close to its end, he looks forward to applying the lessons he learned to other areas of deep-learning research, particularly on graphs.

      Chen and his colleagues could use this approach to develop models that map other complex, conditional relationships. They also want to explore how they can efficiently learn these models when the graphs become enormous, perhaps with millions or billions of interconnected nodes. And rather than finding anomalies, they could also use this approach to improve the accuracy of forecasts based on datasets or streamline other classification techniques.

      This work was funded by the MIT-IBM Watson AI Lab and the U.S. Department of Energy. More