Priya Donti is an Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS, whose research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Methodologically, this entails exploring ways to incorporate relevant physics, hard constraints, and decision-making procedures into deep learning workflows. Priya is also the co-founder and Chair of Climate Change AI, a global nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. 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 ACM SIGEnergy Doctoral Dissertation 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.

AI2050 Project

Power grids are responsible for a quarter of global greenhouse gas emissions, and must be rapidly transitioned to using low-carbon energy sources (such as solar and wind) in order to address climate change. Unfortunately, the amount of power produced by solar panels and wind turbines changes based on the weather, which is tricky to manage given that power grids must always exactly balance between power supply and demand and satisfy other important physical constraints. Priya Donti’s AI2050 project develops physics-informed machine learning methods that help safely maintain this balance by optimizing batteries and other flexible devices. This improves our ability to integrate more low-carbon energy into power grids.