Jinyuan Chang University of Melbourne
Hao Chen University of California, Davis
Xi Chen New York University
Xiaohui Chen University of Illinois at Urbana–Champaign
Yang Feng Columbia University
JinzhuJia Peking University
Danning Li University of Cambridge
Wei Lin Peking University
Weidong Liu Shanghai Jiao Tong University
Zongming Ma University of Pennsylvania
Qing Mai Florida State University
Zhao Ren University of Pittsburgh
Tingni Sun University of Maryland
Wenguang Sun University of Southern California
Cheng Wang Shanghai Jiao Tong University
Yihong Wu University of Illinois at Urbana–Champaign
Yin Xia University of North Carolina at Chapel Hill
Lingzhou Xue Pennsylvania State University
Jianxin Yin Renmin University of China
Kai Zhang University of North Carolina at Chapel Hill
Dave Zhao University of Illinois at Urbana–Champaign
Wei Zhong Xiamen University
Wen Zhou Colorado State University
Changliang Zou Nankai University
四、短期课程内容
课程一:Learning with Sparsity
主讲人:Ming Yuan(袁明),University of Wisconsin–Madison
课程摘要:This course will cover high-dimensional statistical inferences with the focus on the recovery of high-dimensional sparse signals and the estimation of large matrices. These and other related problems have attracted much recent interest in a range of fields including statistics, computer science, applied mathematics and electrical engineering. This course will present an overview of the basic techniques and the latest results on a number of related topics.
课程二:Optimalities in Estimation of Graphical Models and Network Analysis
主讲人:Harrison Zhou(周慧斌),Yale University
课程摘要:Two topics will be discussed in this short course: (1) inference and minimax estimation for a class of graphical models including Gaussian graphical model and Ising model; (2) minimax estimation for graphon estimation and community detection for stochastic block models and its extensions. For both topics we will introduce computationally efficient algorithms to attain the optimalities.