Title：Matrix differential calculus with applications in statistical learning

Abstract：In this talk, we begin with a review of several fundamental mathematical topics—including calculus, linear algebra, probability, and statistics—as they relate to statistical learning. We then focus specifically on matrix differential calculus and its applications in the multivariate linear model, including efficiency comparisons, sensitivity analysis, and local influence diagnostics.

Speaker：Professor Shuangzhe Liu, University of Canberra, Australia. Professor Shuangzhe Liu is currently the Head of the Data Science Group at the Faculty of Science and Technology, University of Canberra, Australia. He received his Ph.D. in Economics from the Tinbergen Institute, University of Amsterdam, the Netherlands. His main research interests include matrix differential calculus, multivariate analysis, and statistical learning. He has published over100 research papers in international journals in mathematics, statistics, and related fields, contributed 15 book chapters, and authored or edited several English-language books and volumes. He currently serves as an Associate Editor for Journal of Multivariate Analysis, Journal of Statistical Computation and Simulation, REVSTAT – Statistical Journal, and as the Editor-in-Chief of Statistical Papers.

Date：Tuesday, June 3, 2025  3:00 PM – 5:00 PM

Venue: Lecture Hall 208, School of Mathematical Sciences, Shouxi Lake Campus, Yangzhou University

Inviter: Jun Jin

Organizer: School of Mathematical Science 

Students and teachers who are interested in statistics or matrix differential calculus are warmly welcome to attend.