Stephen Lee is a Postdoctoral Associate at MIT CEEPR. His research focuses on the development of geospatial machine learning systems that produce high-resolution maps of electricity and heating demand in the U.S. and across the globe. He specifically combines deep learning and Bayesian inference methods to build novel systems for multimodal data fusion capable of encoding constraints from physics- and economics-based theory.

Stephen received a Ph.D. and S.M. in Electrical Engineering and Computer Science from MIT, an S.M. in Technology and Policy from MIT, and a B.S. in Materials Science and Engineering with a second major in Economics from Johns Hopkins University.