
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 highdimensional 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.

News
I'm coorganizing the 2021 ICML Workshop on Distributionfree Uncertainty Quantification, which will take place on Saturday, July 24, 2021.
Select recent papers
 “Crossvalidation: what does it estimate and how well does it do it?”
S. Bates, T. Hastie, and R. Tibshirani. arXiv preprint, 2021.
[arXiv]
[code]
[bibtex]
 “DistributionFree, RiskControlling Prediction Sets”
S. Bates, A. Angelopoulos, L. Lei, J. Malik, and M. I. Jordan. arXiv preprint, 2021.
[arXiv]
[video]
[blog]
[code]
[bibtex]
 “Causal Inference in Genetic Trio Studies”
S. Bates, M. Sesia, C. Sabatti, and E. Candès. PNAS, 2020.
[arXiv]
[journal]
[video]
[tutorials+code]
[bibtex]
*Selected as a cover article and for invited commentary.
