JI Keynote Lecture 2021
Time : 15:00-17:00, July 19(Monday) & 15:00-17:00, July 20 (Tuesday)
Topic: Graphical Models and Message Passing for Sparse Bayesian Learning and Grant-Free NOMA Receiver Design
Speaker: .A. P. Qinghua Guo
Host by: Dr. Yann Berquin
Qinghua Guo received his PhD degree from City University of Hong Kong in 2008. He is now an Associate Professor with the University of Wollongong, and an Adjunct Associate Professor with the University of Western Australia. His research interest includes signal processing, telecommunications, etc.
Graphical Models are a powerful tool that finds numerous applications in computer vision, communications, language processing, etc. In this keynote lecture, I will talk about our recent research and advances on graphical models based fast and robust sparse Bayesian learning and graphical model based Bayesian receiver design to achieve efficient grant-free non-orthogonal multiple access (NOMA) in wireless communications.
Central China Normal University Wollongong Joint Institute