Seminar Announcement Nov. 12 , Wednesday, 9:00-11:00am, 武汉光电国家实验室F401 Title: Structured Probabilistic Model Learning in High-Dimensional Data Compression Prof. Hongkai Xiong (熊红凯) Dept. of Electronic Engineering, Shanghai Jiao Tong University Abstract With massive volume of high-dimensional data with complex structures, structured probabilistic model learning is endowed to investigate probabilistic graphical model, make model-based inference for learning, and adopt reasoning algorithms to optimize learning process. In this talk, we will address a generalized context modeling (GCM) to capture complex structures in heterogeneous data. It extends the contexts from the suffix of predicted subsequences in classical context modeling to the arbitrary combination of symbols in all the directions with combinatorial structuring and multi-directional extension. The model tree for GCM is constructed to address the selection of contexts, which is composed of a combinatorial structuring of finite order combination of predicted symbols in multiple directions. Consequently, the estimated probability for prediction is derived based on the model tree. On such basis, its learning is applied to high-dimensional data compression applications, e.g. genomic data compression, lossless image coding, and intra-frame video coding. Biography Hongkai Xiong is currently a Full Professor in the Department of Electronic Engineering, Shanghai Jiao Tong University (SJTU). Since he received Ph. D degree from SJTU in 2003, he has been with the Department of Electronic Engineering, SJTU. From Dec. 2007 to Dec. 2008, Dr. Xiong was a Research Scholar with the Department of Electrical and Computer Engineering, Carnegie Mellon University (CMU), PA, USA. From 2011 to 2012, he was a scientist with the Division of Biomedical Informatics at the University of California, San Diego (UCSD).