内容简介：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：Michael Ng (吴国宝)  教授

报告人简介：Michael Ng (AMS Fellow, SIAM Fellow, AAIA Fellow) is the Dean of Science, Dr. Elizabeth K.S. Law Endowed Professor in Data Science, Chair Professor in Mathematics, Chair Professor in Data Science, and Chair Professor (Affiliate) in Department of Computer Science, Hong Kong Baptist University. His research interests include bioinformatics, image processing, scientific computing, and data mining. He is selected for the 2025 Class of Fellows of the American Mathematical Society and the 2017 Class of Fellows of the Society for Industrial and Applied Mathematics. He obtained the Feng Kang Prize for his significant contributions in scientific computing. He serves on the Editorial Board members of several international journals. (https://sites.google.com/view/michael-ng-math/home)

题目二：The tail-atomic norm methodology and the profile analyses of the tail-l2 minimization

内容简介：An effective tail-atomic norm methodology for gridless spectral estimations are developed with a tail minimization mechanism. We prove that the tail-atomic norm is equivalent to a positive semi-definite programming (PSD) problem. The tail-atomic norm algorithm is more robust to noise than other related methodologies. Moreover, we conduct profile analyses on the tail-l2 minimization and establish an equivalent two-stage formulation. A novel error bound of tail-l2 minimization problem is derived and the tail-l2 profile algorithm shows superior performance on sparse recovery and robustness against noise.

报告人：冼军  教授

报告人简介：博士生导师, 现为中山大学数学学院教授，中国数学会理事、广东省数学会理事、广东省工业与应用数学学会副理事长、广东省计算数学重点实验室副主任。2004年毕业于中山大学基础数学专业，获理学博士学位, 同年进入浙江大学数学博士后流动站, 2006年博士后出站至今在中山大学数学学院工作。主要研究方向为小波分析与应用调和分析、采样理论及其在信号处理中的应用。在Appl. Comput. Harmon. Anal., Inverse Probl., J. Fourier Anal. Appl., Proc. Amer. Math. Soc., J. Approx. Theory等国内外主流专业期刊发表多篇关于信号的采样与重构理论及其应用的论文, 部分结果获得同行们的关注；曾作为项目负责人主持包括国家优秀青年基金在内的多项国家级和省部级基金项目；以第一负责人获2023年度广东省自然科学二等奖。

时  间：2024年11月9日（周六）上午9：30开始

地  点：南海楼124室

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信息科学技术学院

2024年11月5日