Abstract: Attention seeking is a critical step in the development of artificial intelligence (AI) tools, as highlighted by the phrase “attention is all you need.” This technique leverages domain knowledge together with statistical and machine learning methods to create features that are useful for downstream analyses but are not directly available in the original input data. I will introduce several methods that we developed both before the resurgence of modern AI and more recently for the analysis of genome-wide association study (GWAS) data, particularly the construction of super-variants and the use of regional association scores. I will demonstrate the utility of these approaches in confirming known genetic variants and discovering novel variants associated with complex diseases.

Bio: 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, recognition as a 2023 Highly Cited Researcher by the Web of Science, and the 2026 Samuel S. Wilks Award from the American Statistical Association. He has published over 420 peer-reviewed research articles in top scientific, medical, and statistical journals.