Department of Electronic and Computer Engineering Seminar - Context-tree algorithms: From universal data compression to universal channel decoding
In many practical situations, including communication, prediction, and data analysis, people are interested in learning the distribution of one or more sequences (or datasets). A powerful tool from information theory for this purpose is the universal data compression that has some provable performance guarantee. The Lempel-Ziv and context-tree weighting (CTW) algorithms are two well-known examples. In this talk, we shall present some potentials of context-tree based methods in data compression and channel decoding. In particular, we propose a CTW-based decoder that is universal for the class of finite-memory stationary channels, i.e., achieves the same error exponent as if the true channel distribution were perfectly known.
Sheng Yang received the B.E. degree in electrical engineering from Jiaotong University, Shanghai, China, in 2001, and both the engineer degree and the M.Sc. degree in electrical engineering from Telecom Paris, France, in 2004, respectively. In 2007, he obtained his Ph.D. from Sorbonne University (formerly Univ. Paris VI). From October 2007 to November 2008, he was with Motorola Research Center in Gif-sur-Yvette, France, as a senior staff research engineer. Since December 2008, he has joined CentraleSupélec, Paris-Saclay University, where he is currently a full professor. He has held visiting professorship at the University of Hong Kong (HKU) and the Hong Kong University of Science and Technology (HKUST). He received the 2015 IEEE ComSoc Young Researcher Award for the Europe, Middle East, and Africa Region (EMEA). He was an associate editor of the IEEE transactions on wireless communications from 2015 to 2020, and now an associate editor of the IEEE transactions on information theory.