Hello! I am an Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS. I am 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.

My 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.

I am a recipient of the MIT Technology Review’s 2021 “35 Innovators Under 35” award and the 2022 ACM SIGEnergy Doctoral Dissertation Award.

I am currently accepting PhD students. Students who are interested in working with me should apply through MIT EECS and list me in their application; more instructions are here. I am unfortunately unlikely to be able to respond to individual email inquiries or meet with prospective PhD students outside of the formal application process.

Previously, I was a Runway Startup Postdoc at Cornell Tech and the Jacobs Institute. I received my Ph.D. from the Computer Science Department and the Department of Engineering & Public Policy at Carnegie Mellon University (CMU), co-advised by Zico Kolter and Inês Azevedo. At CMU, I held the U.S. Department of Energy Computational Science Graduate Fellowship, the Siebel Scholarship, and the NSF Graduate Research Fellowship. Before starting my Ph.D., I was a Thomas J. Watson Fellow, traveling the world to study the people, technologies, and policies behind next-generation electricity systems. I received my undergraduate degree from Harvey Mudd College, with a major in computer science and math as well as an emphasis in environmental analysis.