I am a research fellow in the Center for Computational Mathematics at the Flatiron Institute, where I am a member of the machine learning group. I am broadly interested in developing robust and reliable methods for data analysis and understanding their properties. I’m particularly interested in probabilistic inference and uncertainty quantification, with a focus on Bayesian methods under model misspecification, approximate inference, active learning, and applications in science.
I completed a Ph.D. in Computer Science from Princeton University and was supported in part by a Google PhD Fellowship in Machine Learning. Previously, I received an A.B. in Computer Science and Statistics from Harvard University, an M.S. in Statistics from the University of Chicago, and an M.A. in Computer Science from Princeton University.