主题：Attention Seeking in Genome-wide Association Studies
主讲人：耶鲁大学公共卫生学院生物统计学系张和平教授
主持人：统计与数据科学学院林华珍教授
时间：2026年8月27日（周四）下午4：00-5：00
地点：柳林校区弘远楼408会议室
主办单位：统计与数据科学学院和统计研究中心 科研处
主讲人简介：
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.
张和平（Heping Zhang）博士是耶鲁大学公共卫生学院生物统计学系的Susan Dwight Bliss教授。他还兼任耶鲁大学医学院儿童研究中心、妇产科与生殖科学系以及耶鲁大学统计与数据科学系的教授职务。他是耶鲁大学统计科学合作中心的创始主任。张博士是美国统计学会（ASA）和国际数理统计学会(IMS)的Fellow 。他是《Statistics and Its Interface》的创刊主编，并曾担任《Journal of the American Statistical Association - Applications and Case Studies》的编辑。他的荣誉包括2008年哈佛大学公共卫生学院Myrto Lefkopoulou杰出讲座、2011年IMS Medallion讲座及奖、2022年Neyman讲座及奖、2023年国际泛华统计协会杰出成就奖、2023年科睿唯安高被引研究者，以及2026年美国统计协会Samuel S. Wilks奖。他在顶尖科学、医学和统计学期刊上发表了超过420篇论文。
内容提要：
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.
