COPSS-NISS Leadership Webinar: The Role of Bayesian Statistics in an Age of AI

Overview

The January 27, 2026, COPSS-NISS Leadership Webinar, The Role of Bayesian Statistics in an Age of AI, will feature distinguished statisticians David Dunson (Duke University) and Xuming He (Washington University in St. Louis) in a discussion exploring how Bayesian methods can enhance transparency, interpretability, and decision-making in modern AI systems. Moderated by Prof. Dr. Nadja Klein (Karlsruhe Institute of Technology), the session will highlight the latest advances in Bayesian learning, robust inference, and interdisciplinary applications, offering insights into how these approaches address complex, high-dimensional problems across fields such as neuroscience, genomics, environmental science, and beyond. Part of the COPSS-NISS Leadership Webinar Series, this event continues the mission of fostering leadership and innovation in statistics and data science.

Speakers

David Dunson, Arts and Sciences Distinguished Professor of Statistical Science & Mathematics, Duke University

Xuming He, Chair of the Department of Statistics & Data Science and Kotzubei-Beckmann Distinguished Professor, Washington University in Saint Louis

Moderator

Nadja Klein (SCC), Professor and Leading the Research Group Methods for Big Data, Karlsruhe Institute of Technology 

About the Speakers

David Dunson is an Arts and Sciences Distinguished Professor of Statistical Science & Mathematics at Duke University. David Dunson's research focuses on developing statistical and machine learning methodology for analysis and interpretation of complex and high-dimensional data, with a particular emphasis on scientific applications, Bayesian statistics and probability modeling approaches. Methods development and theory is directly motivated by challenging applications in neuroscience, genomics, environmental health, and ecology among others. His work has had a substantial impact, with an H-index of 94. He has received numerous awards, including a gold medal from the US Environmental Protection Agency, the COPSS Presidents' Award given to one outstanding statistician each year, the Mortimer Spiegelman Award given to one outstanding public health statistician each year, a highly cited researcher award from Web of Science, an IMS Medallion lecture, and most recently the G.W. Snedecor Award of the Committee of the Presidents of Statistical Societies (COPSS).

Xuming He joined Washington University in July 2023 as the inaugural chair of the Department of Statistics & Data Science. Previously, he served as the H.C. Carver Collegiate Professor of Statistics at the University of Michigan. He is a renowned leader in the fields of robust statistics, quantile regression, Bayesian inference, and post-selection inference; he is also a proponent of interdisciplinary research in data science. Before joining the University of Michigan in 2011, He held positions at the National University of Singapore and the University of Illinois at Urbana-Champaign and served as program director of statistics at the National Science Foundation. He is a fellow of the American Association for the Advancement of Science and the American Statistical Association. Currently, He serves as President (2023-2025) of the International Statistical Institute. He received his bachelor's of science from Fudan University and his master's (mathematics) and PhD (statistics) from the University of Illinois at Urbana-Champaign.

About the Moderator

Prof. Dr. Nadja Klein is a professor at SCC leading the research group Methods for Big Data. She is also an Emmy Noether Research Group Leader and a member of AcademiaNet, Die Junge Akademie, among others. Prof. Dr. Klein was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award. After completing her doctoral studies in Mathematics, she served as a postdoctoral researcher at the University of Melbourne as a Feodor-Lynen Fellow of the Alexander von Humboldt Foundation. She later became Professor of Statistics and Data Science at Humboldt-Universität zu Berlin before joining KIT. Her research focuses on Bayesian learning, a powerful approach that enables the incorporation of prior knowledge, quantification of uncertainties, and deeper insight into the “black boxes” of machine learning. By combining the precision and reliability of Bayesian statistics with the flexibility of machine and deep learning, her work aims to achieve the best of both worlds. More information about her activities can be found at https://kleinlab-statml.github.io/.

About the COPSS-NISS Leadership Webinar Series

COPSS (Committee of the Presidents of Statistical Societies) and NISS have come together to organize and host a new webinar series focusing on leadership in statistics and data science. Plan to attend these webinars every month during the academic year! Visit the COPSS-NISS Leadership Series Page for previous webinars.

The COPSS-NISS Leadership Webinar Series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) Emerging Leaders in Statistics and the National Institute of Statistical Sciences (NISS). The purpose of the webinar series is to promote leadership skills for members of the statistical societies at any stage in their careers. The series features conversations with leaders throughout the discipline, including leaders from major academic and government institutions, and companies. Invited speakers share their leadership stories and answer questions about their experiences. Each webinar is moderated by a member of the COPSS Emerging Leaders in Statistics program.

Access the Full COPSS-NISS Leadership Webinar Series YouTube Playlist | COPSS-NISS Leadership Webinar Series: https://www.youtube.com/playlist?list=PLoRtupvDJTjvFukMcO6NfDr0GvxDsIj81