COPSS-NISS Leadership Webinar: Measuring Variable/Feature Importance

Join us for the COPSS-NISS Leadership Webinar: Measuring Variable/Feature Importance, an engaging and timely session designed for statisticians, data scientists, and analytics leaders seeking a deeper understanding of how to quantify the influence of variables in modern statistical and machine‑learning models.

In this webinar, expert speakers will explore the theory and practice of measuring variable or feature importance across a range of modeling frameworks—from traditional regression approaches to contemporary machine‑learning methods such as random forests, gradient boosting, and neural networks. Participants will gain insight into when and how to apply different importance metrics, how to interpret them responsibly, and how these measures can guide decision‑making in scientific, business, and policy contexts.

Event Flyer Coming Soon!

Larry A. Wasserman is a professor in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University. I received my Ph.D. from the University of Toronto in 1988. I was awarded the COPSS Presidents’ Award in 1999 for the outstanding statistician under age 40, and the CRM-SSC Prize in 2002. I am an elected fellow of the American Statistical Association, Institute of Mathematical Statistics, and the American Association for the Advancement of Science. My research papers frequently focus on nonparametric inference, asymptotic theory, causality, and applications of statistics to astrophysics, bioinformatics, and genetics. I have also written two advanced statistics textbooks: “All of Statistics,” and “All of Nonparametric Statistics.” His research interests include machine learning, high dimensional inference, nonparametric inference, statistical topology, and astrostatistics. Areas of Research: Causal Inference, DELPHI, STAMPS, Foundations of Inference, Statistical Machine Learning, Optimal Transport, and Nonparametric Methods. See Profile

Dr. Art Owen is the Max H. Stein Professor of Statistics, jointly appointed with Data Science and Bio-X at Stanford University. His research interests are in high dimensional integration, nonparametric confidence intervals, variable importance measures and crossed random effects. The classes that I teach are primarily graduate courses in applied statistics. I also frequently teach the statistical consulting course. I am a co-founder and co-director of the Stanford statistics industrial affiliates program. This program connects our departments to industrial settings in which statistical ideas are applied. See Profile