报告题目：Tensor Representations in Data Science
报告人：Michael K. Ng 香港浸会大学
报告时间：2026年3月27日下午15：30—17：00
报告地点：教2-327
主办单位：南京邮电大学理学院
邀请人：金正猛
报告内容：
Higher-order tensors are suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. In this talk, we discuss the recent development of tensor representations in data science. Both theoretical results and numerical examples are presented to demonstrate the usefulness of tensor representations.
报告人简介：
Michael K. Ng received the B.Sc. and M.Phil. degrees from The University of Hong Kong, Hong Kong, in 1990 and 1992, respectively, and the Ph.D. degree from The Chinese University of HongKong, HongKong, in 1995. From 2020 to 2023, he was the Chair Professor of the Research Division of Mathematical and Statistical Science, The University of Hong Kong. He is a the Chair Professor of the Department of Mathematics and the Dean of science with Hong Kong Baptist University, Hong Kong. His research interests include bioinformatics, image processing, fellows of the Society for Industrial and Applied Mathematies. He received scientific computing, and datamining. He is selected for the 2017 class of the Feng Kang Prize for his significant contributions to scientific computing. He serves as the editorial board member for several international journals.