Tackling Climate Change with Machine Learning

Abstract: 

Climate change is one of the greatest challenges that society faces today, requiring rapid action from all corners. In this talk, I will describe how machine learning can be a potentially powerful tool for addressing climate change, when applied in coordination with policy, engineering, and other areas of action. From energy to agriculture to disaster response, I will describe high impact problems where machine learning can help through avenues such as distilling decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. I will then dive into some of my own work in this area, which merges data-driven approaches with physical knowledge to facilitate the transition to low-carbon electric power grids.

Bio: 

Priya Donti is a Co-founder and Chair of Climate Change AI, a non-profit initiative to catalyze impactful work at the intersection of climate change and machine learning, which she is currently running through the Cornell Tech Runway Startup Postdoc Program. She will also join MIT EECS as an Assistant Professor in Fall 2023. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her work explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Priya received her Ph.D. in Computer Science and Public Policy from Carnegie Mellon University, and is a recipient of the MIT Technology Review’s 2021 “35 Innovators Under 35” award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.

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