演講題目：Inference in Probabilistic Models with Applications to Communications and Signal Processing

演  講  者：Prof. Yik-Chung Wu, The University of Hong Kong (HKU)

時　　間：2018年8月1日（星期三）10:30

地　　點：清華大學台達館 304室

ABSTRACT

This talk shall briefly overview two inference techniques in machine learning, namely Gaussian Belief Propagation and Variational Inference, and demonstrates how they are applied to communications and signal processing problems.  Surprisingly, these two machine learning inference techniques are general enough to tackle a wide range of problems.  The applications to be covered include synchronization in large-scale networks, channel estimation under high mobility, power state estimation in smart grid, massive MIMO channel estimation, tensor decomposition, face classification, surveillance video objects separation, and image de-noising.

BIOGRAPHY

Yik-Chung Wu received the B.Eng. (EEE) degree in 1998 and the M.Phil. degree in 2001 from the University of Hong Kong (HKU).  He received the Croucher Foundation scholarship in 2002 to study Ph.D. degree at Texas A&M University, College Station, and graduated in 2005.  From August 2005 to August 2006, he was with the Thomson Corporate Research, Princeton, NJ, as a Member of Technical Staff.  Since September 2006, he has been with HKU, currently as an Associate Professor.  His research interests are in general area of signal processing, machine learning and communication systems, and in particular distributed signal processing and communications; and large-scale and robust optimization.  Dr. Wu served as an Editor for IEEE Communications Letters, and IEEE Transactions on Communications.  He is currently an Editor for Journal of Communications and Networks.