I am a research fellow in the Center for Computational Mathematics at the Flatiron Institute, where I am a member of the ML@CCM group and ML@FI. I am broadly interested in developing robust and reliable methods for data analysis and understanding their properties.
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.
Research interests: approximate inference [variational inference, MCMC]; robust Bayes, nonparametric methods, and misspecification [mixture modeling, graphs]; scientific applications [material science, genomics].