报告人：王超 博士（南方科技大学助理教授）

时间：2021年12月30日，16：00

地点：工西416

摘要：Although big data is ubiquitous in data science, one often faces challenges with small data, as the amount of data that can be taken or transmitted is limited by technical or economic constraints.  To retrieve useful information from the insufficient amount of data, an additional assumption or leveraging information from other sources are required.  In this presentation, I will talk about two kinds of two biomedical applications related to small data. In the first application, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction. We design a specific splitting framework for an unconstrained optimization model so that the alternating direction method of multipliers (ADMM) has guaranteed convergence under certain conditions. In the second application, we consider a grey-matter/white-matter segmentation problem using gigapixel pathology images from autopsied human brain tissues. The need for manual and detailed annotations limits the applicability of supervised deep learning algorithms in this problem. To tackle this challenge, we propose a semi-supervised active learning framework with a region-based selection criterion. This framework iteratively selects regions for annotation query to quickly expand the diversity and volume of the labeled set.

报告人简介：Chao Wang, Ph.D., is an Assistant Professor at Department of Statistics and Data Science of Southern University of Science and Technology (SUSTech). Before joining SUSTech, Chao was a postdoctoral researcher at TETRAPODS Institute of Data Science, the University of California Davis. During 2018-2012, he worked at the University of Texas (UT) Dallas and UT Southwestern Medical Center. He completed his Ph.D. in Mathematics from The Chinese University of Hong Kong. PhD, His research interests include scientific computing, image processing, deep learning, interdisciplinary mathematical modeling, convex and nonconvex optimization. He has published papers in top tier journals like IEEE Trans. Signal Process, SIAM J. Sci. Comput. SIAM J. Imag. Sci., etc.