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 group’s 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 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 am a recipient of the Schmidt Sciences AI2050 Early Career Fellowship and the 2022 ACM SIGEnergy Doctoral Dissertation Award.
PhD 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.