2023 Department Dissertation Awards

The Statistics Department is excited to announce our annual doctoral dissertation awards. Each of these distinctions is accompanied by a prize of $1,000 and recipients will be presented with their certificates during our diploma ceremony on June 18th. Please join us in congratulating these outstanding scholars for making such creative contributions to their fields.

Theodore W. Anderson Theory of Statistics Dissertation Award

Theo Misiakiewicz — for important contributions to the interface between statistics and machine learning, in particular theoretical results on the behavior of large-scale neural networks, focusing on neural networks in the linear regime, mean-field neural networks, and convex neural networks, to characterize the test error of these models for a given target function in a realistic framework for modern high-dimensional applications.

Jerome H. Friedman Applied Statistics Dissertation Award

Kevin Guo — for insightful critiques of causal inference methods that are used by applied statisticians and his proposals to rethink the role these methods play in causal inference.

Ingram Olkin Interdisciplinary Research Dissertation Award

Ben Seiler — for careful and thorough explanations of what it means for a variable to be important, with implications for algorithmic fairness and for privacy.