"What always needs to be at the forefront: what physical and regulatory constraints is your system contending with at any given time and how do you design a suite of methods that actually satisfy those constraints" — Priya L. Donti

Priya L. Donti is a Ph.D. student in the Computer Science Department and the Department of Engineering & Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. She is also co-founder and chair of Climate Change AI, an initiative to catalyze impactful work at the intersection of climate change and machine learning.

Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, Priya's research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Please see here for a list of her recent publications.

Priya is a member of the MIT Technology Review 2021 list of 35 Innovators Under 35, and a 2022 Siebel Scholar. She was previously a U.S. Department of Energy Computational Science Graduate Fellow, an NSF Graduate Research Fellow, and a Thomas J. Watson Fellow. Priya received her undergraduate degree at Harvey Mudd College in computer science and math with an emphasis in environmental analysis.

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