Professor Shuai Li

 IEEE Fellow

University of Oulu, Norway

Biography: 

Prof. Li currently a full Professor with Faculty of Information Technology and Electrical Engineering, University of Oulu and also an Adjunct Professor with National Technology Research Center of Finland (VTT). His main research interests are nonlinear optimization and intelligent control with applications to robotics. He has published over 300 SCI indexed papers on peer reviewed journals, including more than 130 on IEEE transactions, with a total citation for 22,000+ and H-index for 86. He is Fellow of IEEE, IET and AAIA.

Speech Title: Transformer based Deformable Object Manipulation

Abstract: TBD

Young Chief Professor Yong Zeng

 IEEE Fellow

Southeast University, China

Biography: 

Yong Zeng, IEEE Fellow, Young Chief Professor of Southeast University and Purple Mountain Laboratory, Nanjing, China. He received the Bachelor of Engineering (First-Class Honours) and Ph.D. degrees from Nanyang Technological University (NTU), Singapore. From 2013 to 2018, he was a Research Fellow and Senior Research Fellow at the National University of Singapore (NUS). From 2018 to 2019, he was a Lecturer at the University of Sydney, Australia. 

Prof. Zeng was listed as Clarivate Analytics Highly Cited Researcher for 7 consecutive years (2019-2025), AI2000 Most Influential Scholars in the field of Internet of Things for 4 consecutive years (2021-2024), Stanford "Top 2% of Scientists in the World - Lifetime Influence". Prof. Zeng is the recipient of Australia Research Council (ARC) Discovery Early Career Researcher Award (DECRA), IEEE Communications Society Asia-Pacific Outstanding Young Researcher Award, and won 10 international and domestic best paper awards including IEEE Marconi Award (2020 and 2024), Heinrich Hertz Award (2017 and 2020), etc. Prof. Zeng proposed the concept of channel knowledge map (CKM), and his works have been cited by more than 36,000 times. He serves on the editorial board of SCI journals such as IEEE Transactions on Communications, IEEE Transactions on Mobile Computing, and IEEE Communications Letters, and leading guest editor of journals including IEEE ComMag, Wireless ComMag, China Communications, and Science China Information Sciences. Prof. Zeng was elevated to IEEE Fellow “for contributions to unmanned aerial vehicle communications and wireless power transfer”.

Speech Title: 6G Intelligent Channel Knowledge Map Construction and Utilization with Generative AI

Abstract: Existing wireless communication and sensing systems are mainly based on the traditional “environment-unaware” paradigm, which fails to fully exploit the prior information of the local wireless environment, resulting in inefficient environment sensing and channel acquisition. This makes it difficult to meet the future needs with the developing trends such as larger channel dimensions, higher node densities, and more cost-effective hardware. On the other hand, the recently proposed concept of channel knowledge map (CKM) aims to build channel knowledge foundations that learn the intrinsic characteristics of the local wireless environment by fusing massive historical data of all terminals in the area, thereby enables the direct acquisition of environmental priors in advance based on (virtual) terminal location information. This enables the paradigm shift from the traditional environment-unaware to the future environment-aware communication and sensing, offering new ideas for efficient environment sensing and channel acquisition. This talk will introduce the latest research progress in the construction and application of CKM. By discussing the basic principles of CKM, typical cases of communication and sensing based on CKM, the theories and methods of CKM construction based on generative AI, as well as preliminary experimental verification, we will try to answer the five fundamental questions about CKM (2W+3H): What is CKM, why needs CKM, how to build and utilize CKM, and how to build prototypes?

Professor Guan Gui

 IEEE Fellow

Nanjing University of Posts and Telecommunications, China

Biography: 

Guan Gui (Fellow, IEEE) received his Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. From 2009 to 2014, he was a research assistant and postdoctoral research fellow at Tohoku University, Japan. From 2014 to 2015, he was an Assistant Professor at Akita Prefectural University in Japan. Since 2015, he has been a Professor at Nanjing University of Posts and Telecommunications, China. His research focuses on intelligent sensing and recognition, intelligent signal processing, and physical layer security. Dr. Gui has authored over 200 IEEE journal and conference papers and received several best paper awards, including at ICC 2017, ICC 2014, and VTC 2014-Spring. He is a fellow of IEEE, IET, and AAIA, and he is recognized for his contributions to intelligent signal analysis and wireless resource optimization. Among his accolades, he received the IEEE Communications Society Heinrich Hertz Award in 2021 and was named a Clarivate Analytics Highly Cited Researcher from 2021 to 2024. Dr. Gui is a Distinguished Lecturer for the IEEE Vehicular Technology Society (VTS) and the IEEE Communications Society (ComSoc). He is an editorial board member for several leading journals, including the IEEE Transactions on Information Forensics and Security, IEEE Internet of Things Journal, and IEEE Transactions on Vehicular Technology. Additionally, he serves as the Editor-in-Chief of KSII Transactions on Internet and Information Systems. He has also held prominent roles in international conferences, such as Executive Chair of IEEE ICCT 2023, Executive Chair of VTC 2021-Fall, and Vice Chair of WCNC 2021.

Speech Title: Key Technologies for Intelligent Recognition and Efficient Control of Electromagnetic Spectrum 

Abstract: The electromagnetic spectrum, as a national strategic resource, is an important support for the operation of the information warfare system and the release of combat effectiveness. Precise understanding and efficient control of the electromagnetic spectrum have become key core elements for the overall command and control of the electromagnetic spectrum. At present, China has established an electromagnetic spectrum recognition and control system based on artificial intelligence and framework of OODA, and has made significant progress in this field. However, due to its late start, there are still challenges such as incomplete measurement, unclear recognition, unstable control, and inaccurate evaluation in complex electromagnetic environments. To address the above challenges, the research team, supported by key projects such as the Military Science and Technology Commission and the Natural Science Foundation, has conducted research on key technologies for precise and efficient electromagnetic spectrum recognition and control from three aspects: spectrum measurement, spectrum cognition, and spectrum management. They have designed a technology roadmap based on data, cognition as the process, and control as the goal, and have achieved a series of results including datasets, algorithms, software, and hardware, achieving a leap from incomplete measurement, unclear recognition, unstable control, and inaccurate evaluation to complete measurement, clear recognition, stable control, and accurate evaluation.