Computers for the health of the planet | MIT News

The health of the planet is one of the most important challenges facing humanity today. From climate change to unsafe 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 requires approaches that combine scientific, technical, social, economic and political aspects. New computational methods can play a crucial role, providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better preserved biodiversity and sustainable energy sources.

MIT’s Schwarzman College of Computing has committed to hiring several new climate and environmental computing faculties as part of MIT’s plan to hire 20 climate-focused faculties as part of its climate action plan. That year, the college, with multiple engineering and science departments, sought a joint faculty in Informatics for the Health of the Planet, one of six strategic areas of inquiry identified in an MIT-wide planning process to focus joint hiring efforts. The college also sought a core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

The search is part of MIT’s Schwarzman College of Computing’s ongoing effort to hire 50 new faculty — 25 jointly with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to further penetrate computer science and other disciplines in the departments.

Four interdisciplinary scientists were hired for this search. You will join MIT faculty in the coming year to engage in research and teaching that advances the physical understanding of low-carbon energy solutions, modeling of Earth’s climate, biodiversity monitoring and conservation, and agricultural management through high-performance computing and transformative numerical methods advance , and machine learning techniques.

“By coordinating hiring efforts with multiple departments and schools, we have been able to attract a cohort of exceptional scholars in this field to MIT. Each of them develops and uses advanced computational methods and tools to find solutions to a range of climate and environmental problems,” says Daniel Huttenlocher, dean of MIT’s Schwarzman College of Computing and Henry Warren Ellis Professor of Electrical Engineering and Computer Science. “You will also help strengthen cross-departmental connections in computer science in an important, critical area for MIT and the world.”

“These strategic climate and environmental computing hirings are an incredible opportunity for the college to deepen its academic offering and create new collaborative opportunities at MIT,” said 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 central role in MIT’s overarching effort to hire climate-focused faculty – introducing the critical role computers can play in improving the health of the planet through innovative research and curricula.”

The four new faculty members are:

Sara Beery will join MIT in September 2023 as an Assistant Professor in the Department of Artificial Intelligence and Decision Making at EECS. Beery received her PhD in Computer Science and Mathematical Sciences from Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global environmental and biodiversity monitoring across data modalities, and tackles real-world challenges such as strong spatio-temporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She collaborates with non-governmental organizations and government agencies to apply her methods in the wild worldwide, and works to increase the diversity and accessibility of academic research in the field of artificial intelligence through interdisciplinary capacity building and education.

Priya Donti will join MIT in the 2023-24 academic year as an Assistant Professor in the Departments of Electrical Engineering and Artificial Intelligence and Decision Making in the EECS. Donti recently completed her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, jointly advised by Zico Kolter and Inês Azevedo. Her work focuses on machine learning for prediction, optimization and control in heavily renewable power grids. In particular, her research investigates methods to integrate the physics and harsh constraints associated with electrical power systems into deep learning models. Donti is also co-founder and chair of Climate Change AI, a non-profit initiative promoting high-impact work at the intersection of climate change and machine learning, currently underway as part of the Cornell Tech Runway Startup Postdoc program.

Ericmoore Jossou will join MIT in July 2023 as an Assistant Professor in a joint position between the Department of Nuclear Science and Engineering and the Department of Electrical Engineering at EECS. He is currently an Assistant Scientist at Brookhaven National Laboratory, a US Department of Energy-affiliated laboratory conducting research in nuclear and high-energy physics, energy science and technology, environmental and life sciences, nanosciences, and national security. His research at MIT focuses on understanding the correlation between processing, structure, and properties of materials for nuclear energy applications through advanced experimentation, multiscale simulations, and data science. Jossou received his PhD in Mechanical Engineering from the University of Saskatchewan in 2019.

Sherrie Wang will join MIT in the 2023-24 academic year as an assistant professor in a joint position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society. Wang is currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California, Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for earth observation data. Their main areas of application are the improvement of agricultural management and the prediction of climate phenomena. She received her PhD in Computational and Mathematical Engineering from Stanford University in 2021, where she was advised by David Lobell.

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