10/23/25 | 4:15pm | E51-145
Priya Donti
Assistant Professor, MIT EECS and LIDS
MIT
Abstract: Power grids must be managed at greater speed, scale, and physical fidelity to enable decarbonization and improve resilience. Methods from machine learning (ML) have the potential to play an important role by providing fast, scalable approximations for foundational power system optimization problems, but often struggle to enforce hard physical constraints, maintain robustness in the face of unexpected inputs, or generalize across different grids. To address the issue of physical constraints, we present three different approaches — optimization-in-the-loop ML, learned approximate projection, and a safe exploration approach for reinforcement learning — that represent different tradeoffs in terms of runtime and provable guarantees. To foster robustness and generalization, we present two new ML benchmarks: (a) PF∆, focused on fast approximations to power grid simulation problems that can generalize across topologies and cope with near-infeasible instances, and (b) RL2Grid, focused on reinforcement learning for the combinatorial problem of topology optimization.
Bio: Priya Donti is an Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS. Her research focuses on safe and robust machine learning for high-renewables power grids. Priya is also a 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. She was recognized as part of the MIT Technology Review’s 2021 list of “35 Innovators Under 35” and Vox’s 2023 “Future Perfect 50,” and is a recipient of the Schmidt Sciences AI2050 Early Career Fellowship, 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.