Keynote Speech

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MEET 2025
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Keynote Speech

Prof. Mohsen Guizani

Prof. Mohsen Guizani, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE, IEEE Fellow

Title: Federated Learning for Intelligent Autonomous IoT Systems in Smart City

Abstract: To meet the diverse requirements for intelligent autonomous IoT systems and ensure privacy, the concept of Federated Learning (FL) has been proposed and widely adopted. Combining edge computing, security, and machine learning technologies, FL enables efficient and privacy-preserving smart autonomous systems. These smart services rely on optimized computation and communication resources. In this keynote, Prof. Guizani will showcase his research activities contributing to these efforts and discuss potential solutions that leverage FL models for intelligent IoT systems in smart cities.

Bio: Prof. Mohsen Guizani (Fellow, IEEE) received the BS (with distinction), MS, and PhD degrees in Electrical and Computer Engineering from Syracuse University, USA. He is currently a Professor in the Machine Learning Department at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. Previously, he worked at several leading institutions in the USA. His research interests include applied machine learning, smart cities, Internet of Things (IoT), intelligent autonomous systems, and cybersecurity. He became an IEEE Fellow in 2009 and was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science from 2019 to 2022.

Prof. Guizani has received numerous prestigious awards, including the 2015 IEEE ComSoc Best Survey Paper Award, the 2021 ComSoc Best Journal Paper Award, and five Best Paper Awards from ICC and Globecom conferences. He is the author of 11 books, over 1,000 publications, and several US patents. He also received the IEEE WTC Recognition Award (2017), AdHoc TC Recognition Award (2018), and CISTC Award (2019). He served as Editor-in-Chief of IEEE Network and currently serves on the editorial boards of several IEEE Transactions and Magazines. He was the Chair of IEEE ComSoc’s Wireless Technical Committee and TAOS Technical Committee, and he is currently an IEEE ComSoc Distinguished Lecturer.
Prof. Yusheng Ji

Prof. Yusheng Ji, National Institute of Informatics (NII), Tokyo, Japan, IEEE Fellow

Title: Feedback-Free Transmission and Resource Management in Space-Air-Ground Integrated Networks

Abstract: The Space-Air-Ground Integrated Network (SAGIN) has emerged as one of the key technologies driving the evolution toward 6G, enabling seamless integration of terrestrial, aerial, and satellite communications. In this talk, Prof. Ji will first provide an overview of the 3GPP Non-Terrestrial Network (NTN) evolution and its roadmap toward global standardization. As a case study, she will introduce the design of feedback-free transmission and resource management mechanisms in a SAGIN setting based on the Fully-Decoupled Radio Access Network (FD-RAN) architecture. Finally, she will discuss future research directions and challenges in realizing space-air-ground convergence for next-generation networks.

Bio: Prof. Yusheng Ji received the B.E., M.E., and Ph.D. degrees in Electrical Engineering from the University of Tokyo, Japan. She joined the National Center for Science Information Systems (NACSIS), Tokyo, Japan, in 1990. She is currently a Professor at the National Institute of Informatics (NII), Tokyo, Japan, and a Professor at the Graduate University for Advanced Studies (SOKENDAI), Japan. Her research interests include network resource management, mobile computing, and wireless communication systems. Prof. Ji is a Fellow of the IEEE and a Distinguished Speaker of the IEEE Vehicular Technology Society (VTS). She has received multiple prestigious awards, including the IEEE Communications Society Outstanding Paper Award, and has served as TPC Co-Chair, General Co-Chair, or Symposium Co-Chair for major IEEE conferences, including INFOCOM, ICC, GLOBECOM, and VTC.
Prof. Zhisheng Niu

Prof. Zhisheng Niu, Tsinghua University (THU), Beijing, China, IEEE Fellow, IEICE Fellow

Title: Mobility-Enhanced Edge inTelligence (MEET) for Smart and Green 6G Networks

Abstract: Edge intelligence is an emerging paradigm for real-time training and inference at the wireless edge, thus enabling mission-critical and smart applications. Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs — in particular, energy costs. In this talk, Prof. Niu proposes a new framework called Mobility-Enhanced Edge inTelligence (MEET), which exploits the sensing, communication, computing, and self-powering capabilities of intelligent connected vehicles for smart and green 6G networks. Specifically, operators can incorporate infrastructural vehicles as movable BSs or ESs, and schedule them flexibly to align with communication and computation traffic fluctuations. Meanwhile, the remaining compute resources of opportunistic vehicles are utilized for edge training and inference, so that mobility further enhances edge intelligence by bringing additional computing resources, communication opportunities, and diversified data. In this way, deployment and operational costs are distributed across the vast network of autonomous vehicles, realizing edge intelligence in a cost-effective and sustainable manner. Furthermore, these vehicles can be flexibly charged or powered by renewable energy, reducing grid peak power demand and overall electricity costs.

Bio: Prof. Zhisheng Niu received his B.E. degree from Beijing Jiaotong University, China, in 1985, and his M.E. and D.E. degrees from Toyohashi University of Technology, Japan, in 1989 and 1992, respectively. From 1992 to 1994, he worked at Fujitsu Laboratories Ltd., Japan, and in 1994 he joined Tsinghua University, Beijing, China, where he is currently a Professor in the Department of Electronic Engineering. From 1997 to 1998, he visited Hitachi Central Research Laboratory as a HIVIPS senior researcher. His research interests include queueing theory and traffic engineering, wireless communications and mobile Internet, vehicular communications and smart networking, and green communication and networks. Dr. Niu has been actively serving the IEEE Communications Society since 2000 — as Chair of the Beijing Chapter, Director of the Asia-Pacific Board, Director for Conference Publications, Chair of the Emerging Technologies Committee, Director for Online Contents, and Editor-in-Chief of IEEE Transactions on Green Communications and Networking. He received the Distinguished Technical Achievement Recognition Award from the IEEE Communications Society Green Communications and Computing Technical Committee in 2018. Prof. Niu is a Distinguished Lecturer of both the IEEE Communications Society and IEEE Vehicular Technology Society. He is a Fellow of both IEEE and IEICE.
Prof. Junshan Zhang

Prof. Junshan Zhang, University of California, Davis (UC Davis), California, USA, IEEE Fellow, NAI Fellow

Title: Smart IoT in GenAI Era — A World Model Perspective

Abstract: Generative AI is redefining smart IoT ecosystems by embedding reasoning and intelligent decision-making capabilities directly into physical devices and systems. Through embodied intelligence, IoT devices are evolving from passive data collectors into active agents capable of predicting physical interactions and dynamically adapting to environmental changes, user behaviors, and system dynamics. In this talk, Prof. Zhang will present his recent research on world-model-based autonomous driving (AD) as a compelling example of this transformation. By leveraging the ability to extrapolate and anticipate outcomes in previously unseen situations, world-model-based agents embody the generative and predictive strengths of AI, making them particularly adept at tasks that demand foresight and planning. Their self-supervised learning and proactive decision-making capabilities enable autonomous systems to go beyond reactive control, reasoning instead about the future. He will also introduce CarDreamer, an open-source reinforcement learning platform that integrates world models with CARLA to advance research in autonomous driving. In summary, Prof. Zhang envisions that smart IoT systems will evolve into an “Internet of Agents” — a connected ecosystem of intelligent, adaptive, and proactive entities shaping the physical world through generative intelligence.

Bio: Prof. Junshan Zhang has been a Professor in the Department of Electrical and Computer Engineering and a faculty member of the Computer Science graduate program at the University of California, Davis since 2021. He received his Ph.D. degree from the School of Electrical and Computer Engineering at Purdue University in August 2000 and served on the faculty of Arizona State University from 2000 to 2021. His research interests span the broad areas of information networks and data science, including edge AI, reinforcement learning, world models, continual learning, wireless networks, and information theory. He is a Fellow of the National Academy of Inventors (Class of 2024) and the IEEE (Class of 2012), and the recipient of the ONR Young Investigator Award (2005) and the NSF CAREER Award (2003). His papers have received multiple honors, including the Best Student Paper at WiOPT 2018, the Kenneth C. Sevcik Outstanding Student Paper Award of ACM SIGMETRICS/IFIP Performance 2016, Best Paper Runner-Up Awards at IEEE INFOCOM 2009 and 2014, and Best Paper Awards at IEEE ICC 2008 and 2017. He currently serves as the Editor-in-Chief of the IEEE/ACM Transactions on Networking.

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