The Statistical Foundation of Artificial Intelligence

Speaker 

Rongling Wu received a Ph.D. in Quantitative Genetics from the University of Washington (Seattle) in 1995. He was a Distinguished Professor of Statistics and Public Health Sciences at Pennsylvania State University, and Director of the Center for Statistical Genetics. He is also a researcher at Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, and also serves as editor-in-chief, associate editor, special editor and editorial board member of several journals in the fields of genetics, bioinformatics and computational biology.

He was selected as a fellow of the American Association for the Advancement of Science and the American Statistical Association, and won the Distinguished Researcher Award of the American Institute of Applied Mathematics and Statistics (SAMSI), the University of Florida Research Fund Professor Award, the Pennsylvania State University Distinguished University Professor Award, and the Floyd Science Innovation Award. Research interests include: developing interdisciplinary statistical methods to reveal the genetic control mechanisms of complex traits and human complex diseases.

Abstract 

Artificial intelligence (AI) is profoundly impacting science and society by applyingalgorithms and machine learning to enable machines to perform humanlike tasks. As a branch of mathematics to collect, analyze, interpret, display, and organize data, statistics is the theoretical core of AI to improve performance and accuracy. In this talk, I will present several state-of-the-art statistical methods that have been widely used in AIacross various fields. My team attempts to develop statistically principled reasoning and theory to validate the application of AI and enhance its interpretability and sustainability. Our approach builds on statistical mechanics theory and methodology derived from interdisciplinary integration. I will illustrate the methodology with a wide range of applications.