学术报告会：Sparse Optimization and Gene Regulatory Networks

报告题目：Sparse Optimization and Gene Regulatory Networks

报 告 人： 胡耀华    (深圳大学     教授)

报告时间：2025年10月31日周五14:30

报告地点：S3-407

Abstract: Sparse optimization, aiming to find sparse solutions of linear inverse problems, is a popular research topic in applied mathematics and optimization, and nonconvex sparse optimization methods have been shown to enjoy efficient and robust sparsity promotion capability in vast applications. Gene regulatory networks is a fundamental tool in systems biology and biochemistry, while inferring gene regulatory networks from gene expression data at whole genome level is an arduous challenge.

In this talk, we will build up a bridge between sparse optimization and gene regulatory networks, and propose several nonconvex sparse optimization models for gene regulatory networks with different structures. Moreover, we will present the mathematical theory for nonconvex sparse optimization problems, including the consistency theory, the asymptotic theory and the stability theory of the model and the convergence theory of first-order iterative methods.

个人简介：

胡耀华，先后于浙江大学获得学士与硕士学位，香港理工大学获得博士学位。现任深圳大学数学科学学院特聘教授，副院长，博士生导师，香港理工大学兼职博导。主要从事连续优化理论、方法与应用研究，代表性成果发表在SIAM Journal on Optimization, Mathematical Programming, Inverse Problems, Journal of Machine Learning Research, Bioinformatics等期刊，授权多项国家发明专利，开发多个生物信息学工具包，先后主持国家自然科学基金优秀青年科学基金等10余项国家与省市级科研项目。