报告题目：Some open problems concerning the tight frame in data science

报告人：沈益教授，浙江理工大学

报告时间：2024年11月19日（周二）9:30-10:30

报告地点：20-308

报告摘要：In the realm of mathematical approximation, tight frames represent a fundamental concept with wide-ranging implications, particularly in data science and functional analysis. A tight frame can be viewed as a generalized basis that not only captures the essential components of a data set but also does so with minimal redundancy. This characteristic is especially beneficial in applications where maintaining the integrity of the information while optimizing space and efficiency is crucial. In the fields of structural engineering and architectural design, the equal angle tight frame and mutual unbiased bases are vital solutions for creating resilient and efficient frameworks. In this talk, we will explore several open problems related to these specific tight frames. One originates from compressed sensing, while the other arises from machine learning.

报告人简介：沈益，浙江理工大学数学科学系教授，博导，浙江省应用数学研究会副理事长；毕业于浙江大学数学系，获数学博士学位（导师：李松教授）；从事应用调和分析、信息论、逼近论等相关领域的研究；曾主持国家级青年人才项目，国家自然科学基金面上项目、青年项目等；成果发表于Appl Comput Harmon A、IEEE T Inform Theory、IEEE T Signal Proces、J Approx Theory、J Fourier Anal Appl、Comput Aided Geom D、J Complexity等期刊，曾获“浙江省优秀数学教师”称号。

邀请人：陈杰诚