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Bio: Yiqun Chen is a Stanford Data Science Postdoctoral Fellow hosted by Professor James Zou. His research focuses on quantifying, calibrating, and communicating the uncertainty in modern data analysis, with applications to biomedical and health data. Previously, Yiqun received his Ph.D. in Biostatistics from the University of Washington under the supervision of Professor Daniela Witten. He completed undergraduate degrees in Statistics, Computer Science, and Chemical Biology at the University of California Berkeley. Yiqun is the recipient of a young investigator award at CROI 2020, a best paper award at WNAR 2021, a student research award at NESS 2022, a University of Washington Outstanding Teaching Assistant Award, and the Thomas R. Fleming Excellence in Biostatistics Award.

Talk Title: Selective inference for k-means clustering

Talk Abstract: We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we take a selective inference approach. We propose a finite-sample p-value that controls the selective Type I error for a test of the difference in means between a pair of clusters obtained using k-means clustering, and show that it can be efficiently computed. We apply our proposal in simulation, and on hand-written digits data and single-cell RNA-sequencing data.

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