Prof. Steve Liu, Mohamed bin Zayed University of Artificial Intelligence

Dr. Steve Liu is the Associate VP for Research and Professor of Machine Learning and Professor of Computer Science at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He is also a Professor (on leave) in the School of Computer Science at McGill University since 2007. From 2019 to 2024, he served as VP R&D, Chief Scientist, and Co-Director of the Samsung AI Center Montreal, where he led the R&D of AI innovations in multiple areas, including telecommunications, mobile computing, IoT, and robotics. He was also the Chief Scientist at Tinder Inc., leading the research and innovation for the world’s largest dating and social discovery app valued at over 10 billion US$. He worked briefly as the Samuel R. Thompson Chair Associate Professor in the Department of Computer Science and Engineering at The University of Nebraska-Lincoln, at Hewlett-Packard Labs in Palo Alto, California, and at IBM T. J. Watson Research Center in New York.

Dr. Liu is an IEEE Fellow, and a Fellow of the Canadian Academy of Engineering.  He is an associate member at the Quebec AI Institute (Mila), and McGill Center for Intelligent Machines (CIM). He was the chair of ACM SIGBED from 2021-2025. His research interests focus on AI/Machine Learning, Intelligent Computing and Communications Systems, Sustainable Computing, IoT, and CPS. He has published 5 books and over 400 research papers in major peer-reviewed international journals and conference proceedings, and received 10 best paper awards from IEEE or ACM. He has served as Associate editors/advisors of several international academic journals and has served on the technical or organization committees of over 100 international conferences/workshops.

Title:  LLMs for NextG Communication Networks: Fundamentals, Key Techniques, and Future Directions

Abstract: Large language models (LLMs) are reshaping numerous fields, and their potential in telecommunications is only beginning to be realized. This talk explores emerging LLM applications in the telecom domain. We will start with a brief overview of LLM fundamentals—covering prompt engineering, retrieval-augmented generation (RAG), and practical considerations for deployment. Next, we highlight key techniques and case studies, including self-refined LLMs for network traffic prediction, in-context learning for transmission power optimization, RAG combined with knowledge graphs for telecom applications, and multi-LLM debate frameworks for complex task planning in 6G networks. Together, these studies showcase how LLMs can advance network prediction, optimization, knowledge representation, and automated planning. We conclude by outlining future directions, with emphasis on domain-specific datasets, LLM-based agents, and lightweight small language models tailored for telecom.