Date: 2026-02-20

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/82441217734

Meeting ID: 824 4121 7734

Passcode: None

Abstract:

Hierarchical clustering is one of the most widely used approaches for exploring data. However, its greedy nature makes it highly sensitive to small perturbations, blurring the lines between genuine structure and spurious patterns.  In this work, we show how randomizing hierarchical clustering can be useful not just for assessing clustering stability but also for designing valid hypothesis testing procedures based on clustering results.  In particular, we propose a method for constructing a valid p-value at each node of the hierarchical clustering dendrogram that quantifies evidence against performing the greedy merge.  Furthermore, we show how our p-values can be used to estimate the number of clusters, with a probabilistic guarantee on overestimation of the number of clusters.  This is joint work with Di Wu and Snigdha Panigrahi.

Speaker

Jacob Bien’s research focuses on statistical machine learning and in particular the development of novel methods that balance flexibility and interpretability for analyzing complex data. He combines ideas from convex optimization and statistics to develop methods that are of direct use to scientists and others with large datasets. His work has been supported by a NSF CAREER award, a three-year NSF grant on high-dimensional covariance estimation, an NIH R01 grant on methods for multi-view data, and grants from the Simons Foundation on developing new statistical methodology for oceanography. He is a fellow of the Institute of Mathematical Statistics and a fellow of the American Statistical Association.  He serves as an associate editor of the Journal of the American Statistical Association; he was previously an associate editor for the Journal of the Royal Statistical Society (Series B), Biometrika, the Journal of Computational and Graphical Statistics, and Biostatistics. Before joining USC, he was an assistant professor at Cornell.