| Prof. Pingyi FanTsinghua University (Director of the Open Source Data Cognition Innovation Center, Academician of IEAS, Member of NAAI of the United States, IET Fellow) Prof. Pingyi Fan, a tenured professor in the Department of Electronic Engineering at Tsinghua University, Director of the Open Source Data Cognition Innovation Center, Academician of International Eurasian Academy of Sciences (IEAS), Member of the National Academy of Artificial Intelligence (NAAI) of the United States, recipient of the NAAI 2025 AI Exploration Award, recipient of the NAAI Outstanding Scientist Award, co-chair of the Academic Committee of the NAAI Asia Research Institute, and member of the NAAI Global Governance and AI Safety Committee, Fellow of the Institution of Engineering and Technology (IET) and member of the IET Fellow International Review Committee. Currently, he serves as an editorial board member of IEEE Transactions on Cognitive Communications and Networking (TCCN) (2024-2026), Editor-in-chief for the microwave and wireless communication field of MDPI Electronics, and Chair of China 6G-ANA TG4 etc.. His main research areas include 6G wireless communication networks and machine learning, semantic information theory and big data processing theory, as well as intelligent network and system detection etc. Speech Title: Generative AI-Empowered Semantic Communications Abstract: This report focuses on cutting-edge advancements at the intersection of Semantic Communication (SC) and Generative AI techniques, and presents some examples to show key technological breakthroughs in the three following aspects: (1) Generative Anti-Distortion Semantic Transmission, (2) Source-Free Domain Adaptive Multi-Task Communication, (3) LLM-Driven Collaborative Resource Optimization. These works have validated the effectiveness of AI-Assistance and Semantic Information Processing in the design of 6G intelligent communication systems, i.e. image transmission and vehicular networks. Finally, we give a short summary and outline the future research directions, including security enhancement and architectural scalability. |
| Prof. Lei LeiNanjing University of Aeronautics and Astronautics (Dean of College of Electronic and Information Engineering) Lei Lei is a professor and doctoral advisor at Nanjing University of Aeronautics and Astronautics. He currently serves as Dean of the School of Aerospace Engineering and leads the Aerospace Communication Networks Laboratory team. He has been selected as a Distinguished Professor under the National Major Talent Program and as a Young Top-tier Talent. His primary research areas include intelligent networking and collaboration of aerial vehicles, integrated space-ground computing and networking technologies, and digital and intelligent testing and evaluation of networks; In recent years, he has led more than 40 research projects, including those funded by the National Natural Science Foundation of China, key projects under the National Defense Basic Research Program, projects under the National Defense Science and Technology Innovation Special Zone, and equipment development projects; he has published more than 80 academic papers in journals such as IEEE COMST, IEEE Network, and IEEE TMC, and has been granted more than 50 national invention patents; his research findings have been applied in the defense industry, and as the principal investigator, he received the First Prize for National Defense Science and Technology Progress. In teaching, he has led the development of a national-level first-class undergraduate course and served as the chief editor of a national-level planned textbook. He currently serves as the director of the National Experimental Teaching Demonstration Center for Electrical Engineering and Electronics, a member of the Ministry of Education’s Subcommittee on Basic Electrical and Electronic Course Instruction, and the leader of an outstanding teaching team under Jiangsu Province’s “Qinglan Project” for higher education institutions. As the second principal investigator, he received the Second Prize of the National Teaching Achievement Award. |
| Prof. Tiesong ZhaoFuzhou University (IET Fellow, IEEE Senior Member) Tiesong Zhao (Senior Member, IEEE; Fellow, IET) is currently a Minjiang Distinguished Professor with the College of Physics and Information Engineering at Fuzhou University, Fuzhou, China, and the Director of the Fujian Key Laboratory of Intelligent Processing and Wireless Transmission of Media Information. His research interests include visual-haptics analytics, coding, quality assessment, transmission, display, and their applications in embodied AI. He has been serving as an Associate Editor for IEEE Transactions on Image Processing (2024–present), an Associate Editor for ACM Transactions on Multimedia Computing, Communications, and Applications (2025–present), a Senior Area Editor for IEEE Signal Processing Letters (2025–present), and an Editor/Executive Editor for CSIG Communications (2022–present). He has previously served as Associate Editors and Area Chairs for conferences and journals including IEEE SMC, IET Electronics Letters, ACM Multimedia, ACM Multimedia Asia, etc. He has also served as a General Chair of MLMC 2026, a Technical Program Chair for IEEE MLMC 2025 and a Publicity and Demo Chair for IEEE HAVE 2018. Speech Title: Haptic Coding and Interaction for Multisensorial Media Environments Abstract: Multimedia information, as machine information that humans can intuitively perceive, has always played an important role in consumer internet, human-computer interaction, and virtual reality. To achieve an immersive and interactive media environment, introducing multi-sensory information elements such as vision, hearing, and touch is a crucial way to enhance user experience. This report will introduce haptic applications in multi-sensory media interaction environments. Using motion and vibration signals as examples, it will explore the data characteristics and compression tasks of haptic information, design corresponding encoding and decoding frameworks based on traditional methods and deep learning methods, and summarize the development trends and future hot topics in related research. |
