Computing for a healthy planet | MIT news

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

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

The MIT Schwarzman School of Computing is committed to recruiting several new faculty members in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its framework. Climate Action Plan. This year, the college conducted searches with several departments in colleges of engineering and sciences for joint faculty in computing and planet health, and is one of the Six strategic areas to investigate They were identified in an MIT-wide planning process to help focus joint staffing efforts. The college also conducted searches for basic computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

The searches are part of an ongoing effort by the MIT Schwarzman School of Computing to recruit 50 new faculty — 25 with other academic departments and 25 in computer science, artificial intelligence, and decision-making. The goal is to build capacity at MIT to help embed computing and other disciplines deeper into the departments.

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

“By coordinating recruitment efforts with multiple departments and schools, we have been able to attract a group of distinguished scientists in the field to MIT. Each one develops and uses advanced computational methods and tools to help find solutions to a range of climate and environmental issues,” says Daniel Huttenlocher, Dean of the MIT Schwarzman School of Computing and Henry Warren Ellis Professor of Electrical Engineering and Computer Science. “They will also help strengthen interdepartmental relationships in computing across an important and critical area for MIT and the world.”

says Anantha P. Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “The college plays a pivotal role in MIT’s overall effort to recruit climate-focused faculty—delivering the critical role of computing to address the health of the planet through innovative research and curricula.”

The four new faculty members are:

Sarah Perry She will join MIT as an assistant professor in the School of Artificial Intelligence and Decision Making at EECS in September 2023. Berry received her Ph.D. in Computing Science and Mathematics at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable environmental and biodiversity monitoring on a global scale via data methods, addressing real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine classes, and long-tailed distributions. It partners with NGOs and government agencies to spread its methods out into the wild around the world and works to increase the diversity and accessibility of academic research in AI through capacity building and interdisciplinary education.

Priya Donte He will join MIT as an Assistant Professor in the Schools of Electrical Engineering, Artificial Intelligence, and Decision Making at EECS in the 2023-24 academic year. Donte recently completed her Ph.D. in the Department of Computer Science and the Department of Engineering and Public Policy at Carnegie Mellon University, with the assistance of Zico Coulter and Ines Azevedo. Her work focuses on machine learning to predict, improve, and control high-energy renewable energy networks. Specifically, her research explores ways to incorporate the challenging physics and limitations associated with electrical power systems into deep learning models. Donti is also the co-founder and president of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning currently powered by the Cornell Tech Runway Startup Postdoc.

Ericmore Gusu He will join MIT as an Assistant Professor in a joint position between the Department of Nuclear Science and Engineering and the School of Electrical Engineering at EECS in July 2023. He is currently an Assistant Scientist at Brookhaven National Laboratory, a US Department of Energy laboratory that conducts research in nuclear and high-energy physics, and energy science and technology environment, biosciences, nanosciences, and national security. His research at MIT will focus on understanding the correlation of material processing structure properties for nuclear power applications through advanced experimentation, multi-scale simulations, and data science. Gusu received his Ph.D. in Mechanical Engineering in 2019 from the University of Saskatchewan.

Sherry Wang He will join MIT as an assistant professor in a joint position between the Department of Mechanical Engineering and the Institute for Data, Systems and Society in the 2023-24 academic year. Wang is currently a Ciriacy-Wantrup Postdoctoral Fellow at UC Berkeley, hosted by Solomon Hsiang and the Global Policy Laboratory. It develops machine learning for Earth observation data. Its main areas of application are the improvement of agricultural management and forecasting of climatic phenomena. She received her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobel.

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