Title: The Importance of Statistical Thinking in an AI-Augmented World

Presented by Bhramar Mukherjee, Anna M.R. Lauder Professor of Biostatistics and inaugural Senior Associate Dean of Public Health Data Science and Data Equity, Yale University

DATE: Wednesday, April 8, 2026, 3:30 PM, AUST 434

Meeting Link: WebEx link

Coffee will be available at 3:00 PM in the Noether Lounge (AUST 326)

Abstract: Standing at this transformative moment for data science and higher education, it is natural to debate the role statisticians will play in science and society in the next ten years. I would like to argue that foundational statistical thinking always has a role to play, even in a world consumed by AI. I will start with the obvious: AI algorithms and systems developed on exclusionary datasets can lead to erroneous conclusions and misguided policies. However, while we wait for the ideal scenario of globally representative datasets or training corpora, statisticians play a pivotal role in mitigating systematic sources of bias in analyzing LARGE datasets—an expertise that few other quantitative disciplines possess. I will illustrate my point by using analysis of electronic health records as an example where clever statistical thinking around selection bias and missing data can prevent analytic disasters. Proper design, collection, and measurement of data reside at the heart of doing good science.

I will conclude the talk with a call to arms for statisticians to lead efforts for creating, curating, collecting "good" data and pioneering new scientific studies, not just remain on the design and analytic fringes.

Bio: Dr. Bhramar Mukherjee is the Anna M.R. Lauder Professor of Biostatistics and inaugural Senior Associate Dean of Public Health Data Science and Data Equity at the Yale School of Public Health, with additional appointments in Chronic Disease Epidemiology and Statistics & Data Science. Before joining Yale in 2024, she spent nearly two decades at the University of Michigan, where she served as Chair of Biostatistics and held several distinguished professorships.

Her research centers on statistical methods for electronic health records, gene-environment interactions, data equity, and environmental epidemiology. A prolific scholar with 500+ publications and nearly 19,000 citations, she was elected to the U.S. National Academy of Medicine in 2022 and is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the AAAS.