【Master Forum】Statistics in the Age of AI

May 12, 2026 Upcoming

Topic:?Statistics in the Age of AI: Challenges and Opportunities

Speaker: Professor Tony Cai

Host: Professor Jeff Yao

Date: Tuesday, May 12, 2026

Time: 3:00 PM - 4:00 PM

Venue: SIN Wai Kin International Conference Centre (W201), Administration Building

Language: English

Abstract:?

The world is undergoing a profound technological transformation driven by Big Data and data-driven AI. At the heart of this revolution lie statistics, machine learning, and AI—tools reshaping how we work, live, and communicate across virtually every sector. The field of statistics, built on more than a century of foundational achievements, now stands at a pivotal moment. The rise of AI, complex high-dimensional data, and growing demands from science, industry, and society are reshaping not only how we conduct research but also our identity and role as statisticians. These changes are not purely technical—they challenge our place in a rapidly evolving scientific ecosystem. In this talk, I will discuss the pressing challenges and exciting opportunities facing statistics in the Age of AI.

Speaker Profile:

Tony Cai is the Daniel H. Silberberg Professor of Statistics and Data Science at the Wharton School, University of Pennsylvania. He also holds joint appointments as a professor in the Applied Mathematics & Computational Science Graduate Group and as a senior scholar in the Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine.

Tony Cai received the COPSS Presidents'?Award?in 2008 and was elected as a Fellow of the American Association for the Advancement of Science (AAAS) in 2024. He is the Past-President of the Institute of Mathematical Statistics (IMS). He previously served as Editor of The Annals of Statistics (2020–2022), President of the International Chinese Statistical Association (ICSA, 2017), and Vice Dean at the Wharton School (2017–2020).

His research spans statistical machine learning, high-dimensional statistics, large-scale inference, decision theory, with applications in genomics and financial engineering.

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