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.

Larry A. Wasserman is a professor in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University. He received his Ph.D. from the University of Toronto in 1988. He was awarded the COPSS Presidents’ Award in 1999 for the outstanding statistician under age 40, and the CRM-SSC Prize in 2002. He is an elected fellow of the American Statistical Association, Institute of Mathematical Statistics, and the American Association for the Advancement of Science. His research papers frequently focus on nonparametric inference, asymptotic theory, causality, and applications of statistics to astrophysics, bioinformatics, and genetics. He has 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. See Profile

Art Owen is the Max H. Stein Professor of Statistics at Stanford University. He is best known for inventing the empirical likelihood and for developing and studying randomized quasi-Monte Carlo methods. His research interests are centered on ways to measure uncertainty and ways to sample. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. He received the 2020 Senior Noether Prize in nonparametric statistics from the ASA and the 2021 Gold Medal from the Statistical Society of Canada and became a SIAM Fellow in 2024. He was elected to the National Academy of Sciences in 2026. See Profile