题目：高维张量数据的统计学习

主讲人：威斯康辛大学/杜克大学  张安如助理教授

主持人：统计学院 常晋源教授

时间：2021年4月16日（周五）上午10:00-11:00

直播平台及会议ID：腾讯会议，788 824 896

报告摘要：

The analysis of tensor data has become an active research topic in statistics and data science recently. Many high order datasets arising from a wide range of modern applications, such as genomics, material science, and neuroimaging analysis, requires modeling with high-dimensional tensors. In addition, tensor methods provide unique perspectives and solutions to many high-dimensional problems where the observations are not necessarily tensors. High-dimensional tensor problems generally possess distinct characteristics that pose unprecedented challenges to the statistical community. There is a clear need to develop novel methods, algorithms, and theory to analyze the high-dimensional tensor data.

In this talk, we discuss some recent advances in high-dimensional tensor data analysis through several fundamental topics and their applications in microscopy imaging and neuroimaging. We will also illustrate how we develop new statistically optimal methods, computationally efficient algorithms, and fundamental theories that exploit information from high-dimensional tensor data based on the modern theory of computation, non-convex optimization, applied linear algebra, and high-dimensional statistics.

主讲人简介：

张安如，威斯康辛大学助理教授以及杜克生物统计生物信息系访问教授。他于2010年取得北京大学学士学位，2015年取得宾夕法尼亚大学博士学位（师承蔡天文老师）。他现在的研究方向主要包括：高维数据分析、非凸优化、统计学习理论、以及在基因组、微生物、计算成像学的应用。他取得了一系列奖项，包括2020年美国自然科学基金委员会职业开展奖(NSF Career Award)、2015宾夕法尼亚大学院长奖(Dean's Fellow)。