Time: 4:30-5:30 p.m.
Date: Wednesday, April 15, 2026
Speaker: Dr. Bo Li, Stanley A. Sawyer Professor, Department of Statistics and Data Science, Washington University (WashU) in St. Louis.
Abstract: Significant events such as volcanic eruptions can exert global and long-lasting impacts on climate. These impacts, however, are not uniform across space and time. Motivated by the need to understand how the 1991 Mt. Pinatubo eruption influenced global and regional climate, we propose a Bayesian framework to simultaneously detect and estimate spatially varying temporal changepoints. Our approach accounts for the diffusive nature of volcanic effects and leverages spatial correlation. We then extend the changepoint detection problem to large-scale spherical spatiotemporal data and develop a scalable method for global applications. The framework enables Gibbs sampling for changepoints within MCMC, offering great computational efficiency. In addition, the method incorporates spherical harmonic transformations to address the high dimensionality of global data, which further substantially reduces computational burden while preserving accuracy. We demonstrate the effectiveness of our methods using both simulated datasets and real data of stratospheric aerosol optical depth and surface temperature to detect and estimate changepoints associated with the Mt. Pinatubo eruption.
Bio: Dr. Bo Li is a Stanley A. Sawyer Professor in the Department of Statistics and Data Science and Co-director of the Transdisciplinary Institute in Applied Data Sciences at Washington University (WashU) in St. Louis. She received her PhD in Statistics from Texas A&M University in 2006 and then became a Post-Doc at the National Center for Atmospheric Research. Dr. Li began her academic career at Purdue University in 2008 and then moved to the University of Illinois Urbana-Champaign (UIUC) in 2013. Before moving to WashU in 2024, Dr. Li served as Marjorie Roberts Professor and Chair of the Department of Statistics at UIUC. Dr. Li’s research focuses on spatial and spatiotemporal statistics, and environmental applications to broad areas such as atmospheric sciences, climatology, public health, agriculture, ecology and forestry.