I am a PhD student at Princeton University in Computer Science, advised by Ryan P. Adams and Barbara Engelhardt. I am a member of the Laboratory for Intelligent Probabilistic Systems Group and the Biological and Evolutionary Explorations using Hierarchical Integrative Statistical Models Group. 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. My research was supported by a Google PhD Fellowship in Machine Learning.
Research interests: 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 to material science and biomedical data science.
Making probabilistic machine learning more robust to model misspecification
Accelerating machine learning algorithms for inference, design, and search
Developing flexible Bayesian models and studying their properties