Unadorned Statistics in the Light of AI
HEPING ZHANG – YALE UNIVERSITY
ABSTRACT
Regression, clustering, and sequential analysis are fundamental techniques in statistics. Today, these same concepts are often relabeled as supervised learning, unsupervised learning, deep learning, reinforcement learning, or, more broadly, artificial intelligence. In this talk, I will present several of our statistical methods, developed in response to real-world applications, including the analysis of high-dimensional data for building-related occupant syndromes, inference of risk factors with uncertain frequencies from haplotype data, and residual diagnostics for generalized linear models. By revisiting these examples, I will highlight the essential ideas and techniques that our approaches share with modern AI methods. My goal is to reflect on why our statistical methods appear so “unadorned,” and to ask whether—and how—we might close the gap in how statistics and AI are recognized and valued.
BIOGRAPHY
Heping Zhang, Ph.D., is the Susan Dwight Bliss Professor of Biostatistics at the Yale University School of Public Health. He also holds secondary appointments as Professor in the Child Study Center and the Department of Obstetrics, Gynecology, and Reproductive Sciences at the Yale School of Medicine, and in the Department of Statistics and Data Science at Yale University. He is the founding director of the Collaborative Center for Statistics in Science at Yale. Dr. Zhang is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. He was the founding Editor-in-Chief of Statistics and Its Interface and previously served as an editor of the Journal of the American Statistical Association – Applications and Case Studies. His honors include the 2008 Myrto Lefkopoulou Distinguished Lecture at the Harvard School of Public Health, the 2011 IMS Medallion Lecture and Award, the 2022 Neyman Lecture and Award, the 2023 Distinguished Achievement Award from the International Chinese Statistical Association, and recognition as a 2023 Highly Cited Researcher by the Web of Science.