I'm a postdoctoral researcher with Michael I. Jordan in the Statistics and EECS departments at UC Berkeley. I work on developing methods to analyze modern scientific data sets, leveraging sophisticated black box models while providing rigorous statistical guarantees. Specifically, I work on problems in high-dimensional statistics (especially false discovery rate control), statistical machine learning, conformal prediction and causal inference.

Previously, I completed my Ph.D. in the Stanford Department of Statistics advised by Emmanuel Candès. My thesis introduced methods for conditional independence testing and false discovery rate control in genomics, and I was honored to receive the Ric Weiland Graduate Fellowship and the Theodore W. Anderson Theory of Statistics Dissertation Award for this work. Before my Ph.D., I studied statistics and mathematics at Harvard University, and spent a year teaching mathematics at NYU Shanghai. Outside research, I enjoy triathlons, sailing, hiking, and reading speculative fiction novels.