Speaker: Prof.Yaohua Hu
Time: 16:00-17:00, Apr. 15
Location: SIST 1B 105
Host: Prof. Xufeng Kou
Inferring gene regulatory networks from gene expression data and identifying key factors for cell fate conversion are two arduous challenges in biology and regenerative medicine, especially in higher organisms (like human and mouse) where the number of genes is large but the number of experimental samples is small. In this talk, we will formulate these two systems biology problems into group sparse optimization problem by employing the special structure of the involved regulatory networks. The lower-order regularization method for group sparse optimization will be introduced in a unified framework. Theoretical guarantee of the lower-order regularization method is provided via the oracle property and recovery bound, and the numerical performance of the proximal gradient algorithm is presented via the linear convergence property. The applications of group sparse optimization will facilitate biologists to study the gene regulation of higher model organisms in a genome-wide scale.
Dr. Yaohua Hu received his B.S. in Mathematics (Chu Kechen Honors College) and M.S. in Computational Mathematics from Zhejiang University in 2006 and 2009, respectively, and Ph.D. in Applied Mathematics from Hong Kong Polytechnic University (supervised by Prof. Xiaoqi YANG) in 2013. Then he continued his research work in Hong Kong Polytechnic University and Zhejiang University as a postdoctoral fellow. Yaohua Hu joined College of Mathematics and Statistics at Shenzhen University in May 2015, and now he is an associate professor there. His current research interests include theory and algorithms for large-scale optimization, and their various applications in statistics, machine learning, bioinformatics, and image processing. He has published many papers in SIAM Journal on Optimization, Inverse Problems, Journal of Machine Learning Research, European Journal of Operational Research, BMC Genomics. He has also developed several tools/solvers and web servers in bioinformatics.