From Worst to Average Case in Statistical Problems | Richard M. Karp Distinguished Lecture

While many fundamental problems in statistical learning have been studied by both statisticians and theoretical computer scientists alike, one sometimes observes a bifurcation of perspectives: statisticians naturally adopt an "average-case" approach — e.g., assuming i.i.d. draws from an underlying distribution — whereas computer scientists emphasize the limits of learning in worst-case scenarios. This talk will explore several such examples, outlining similarities and differences between the approaches and, in particular, highlighting the benefits of adopting an average-case perspective for analyzing problems that were previously shown to be intractable in the worst case.

Po-Ling Loh received her PhD in statistics from UC Berkeley in 2014. From 2014 to 2016, she was an assistant professor of statistics at the University of Pennsylvania. From 2016 to 2018, she was an assistant professor of electrical & computer engineering at the University of Wisconsin–Madison, and from 2019 to 2020, she was an associate professor of statistics at UW–Madison and a visiting associate professor of statistics at Columbia University. She began her appointment in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge in 2021, where she is currently a professor of statistics and a fellow of St. John's College. Po-Ling's current research interests include high-dimensional statistics, robustness, and differential privacy. She is a recipient of a Philip Leverhulme Prize, NSF CAREER Award, ARO Young Investigator Program Award, IMS Tweedie New Researcher Award, Bernoulli Society New Researcher Award, and Hertz Fellowship.

The Richard M. Karp Distinguished Lectures were created in Fall 2019 to celebrate the role of Simons Institute Founding Director Dick Karp in establishing the field of theoretical computer science, formulating its central problems, and contributing stunning results in the areas of computational complexity and algorithms. Formerly known as the Simons Institute Open Lectures, the series features visionary leaders in the field of theoretical computer science and is geared toward a broad scientific audience.

Light refreshments will be available at 3 p.m., prior to the start of the lecture. 

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