Qilian Liang & Wei Wang 
Artificial Intelligence in China [PDF ebook] 
Proceedings of the 3rd International Conference on Artificial Intelligence in China

Deep Learning and Neural Networks.- Machine Learning.- Computer Vision.- Knowledge Representation and Reasoning.- Probabilistic Reasoning.- Statistical Learning.- Reinforcement Learning.- Decision Making.- Fuzzy Systems.- Evolutionary Computing.- Inference and Prediction.- Human-Inspired Learning and Computing.- Autonomous Vehicle.- Data Mining.- Spoken Language Processing.- Pattern Analysis and Recognition.- Robotics.- AI for Sensing.- AI for Communications.- AI for Internet of Things.- AI for Signal Processing.- AI for Education.- AI for Healthcare.- AI for Other Applications.

Dr. Qilian Liang is a Distinguished University Professor in the Department of Electrical Engineering, University of Texas, at Arlington (UTA), USA, where he has been working since August 2002. Prior to this, he was a member of the technical staff at Hughes Network Systems Inc. in San Diego, California. Dr. Liang received his B.S. degree from Wuhan University, China, in 1993; his M.S. degree from Beijing University of Posts and Telecommunications, China, in 1996; and a Ph.D. degree from the University of Southern California (USC), Los Angeles, USA, in May 2000, all in Electrical Engineering. His research interests include communications, signal processing and fuzzy logic systems. Dr. Liang has published more than 330 journal and conference papers and 9 book chapters and has 6 US patents pending. He received 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award; 2003 U.S. Office of Naval Research (ONR) Young Investigator Award; 2005 UTA College of Engineering Outstanding Young Faculty Award; 2007, 2009, 2010 U.S. Air Force Summer Faculty Fellowship Program Award; 2012 UTA College of Engineering Excellence in Research Award; 2013 UTA Outstanding Research Achievement or Creative Activity Award and was inducted into UTA Academy of Distinguished Scholars in 2015. He is an IEEE Fellow.