Leadership in next generation wireless (NextG) is a national priority, and one of the key enabling technologies is to incorporate machine learning in the design of NextG. Machine learning algorithms have revolutionized image and natural language processing, but if they are to revolutionize wireless then they need to learn at the speed of wireless. This requires fundamental innovation by developing algorithms that can conduct online real-time learning with extremely limited training overheads. This talk describes some of our efforts toward this end.

Lingjia Liu received his Ph.D. in Electrical and Computer Engineering from Texas A&M University and his B.S. in Electronic Engineering from Shanghai Jiao Tong University. At Virginia Tech, he is a Professor & Bradley Senior Faculty Fellow in the Bradley Department of Electrical and Computer Engineering and the Director of Wireless@Virginia Tech. At the national level, he serves as a member of the Executive Committee of the National Spectrum Consortium (NSC). Prior to joining academia, he worked at Samsung Research America (SRA), leading efforts on 3GPP LTE/LTE-Advanced standards. His research interests are focused on enabling technologies for 6G networks, including machine learning for NextG, massive MIMO, non-terrestrial networks (NTN), O-RAN, and integrated sensing and communications. Dr. Liu has received 8 Best Paper Awards, and his research program has attracted more than $132M in research funding with him serving as the principal investigator (PI) on $25+ Million research projects.