About

I am an assistant professor at the University of Wisconsin–Madison in the Department of Computer Sciences. Prior to that, I was a postdoc at the University of Washington with Sewoong Oh. I completed my PhD at Boston University, where I was advised by Adam Smith. I work on machine learning and data privacy. The outputs of data analysis depend on the details of individual data points, sometimes heavily. When is this necessary, and when can we avoid it? I am interested in understanding when and why machine learning models memorize large amounts of training examples. I also study this topic through the lens of differential privacy, a formal framework for reasoning about privacy in data analysis. In this area, I design algorithms for fundamental statistical problems.

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