Keynote Speakers

Prof. Yuan He
Tsinghua University

Prof. Yuan He is a Tenured Full Professor at the School of Software, Tsinghua University, where he also serves as Deputy Director of the Institute of Trustworthy Networks and Systems. He is a recipient of the National Science Fund for Distinguished Young Scholars and a Distinguished Member of the China Computer Federation (CCF). His research focuses on the Internet of Things (IoT), wireless networking, and mobile computing. Prof. He actively contributes to the academic community as a member of the steering committee of prestigious conferences like ACM SenSys and EWSN, the Acting Editor-in-Chief for ACM Transactions on IoT, and an Editorial Board member for journals like IEEE IoT, ACM TOSN, and JCST. He was the Program Co-chair for multiple conferences, including ACM SenSys 2024 and IEEE SECON 2022. His pioneering work has been recognized with the CCF Natural Science Award (Second Class), the SenSys 2023 Test-of-Time Award, and multiple Best Paper Awards from top venues such as MobiSys and SenSys.

Speech Title:
RF Computing: Progress and Perspectives

Abstract:
RF Computing is an innovative paradigm that exploits radio frequency (RF) signals to simultaneously serve as information carriers and computational operands. By enabling direct information processing via RF signal manipulation, it effectively overcomes the latency and energy bottlenecks of traditional digital systems, delivering exceptional efficiency. This talk presents a systematic definition of RF Computing, classifies its operational modalities, and showcases representative applications highlighting its transformative potential. We introduce recent progress with emphasis on novel system architectures and their applications in battery-free communication and wireless sensing. Finally, open challenges and future directions in this emerging field are discussed.

 

Prof. Yejun He
Shenzhen University

Yejun He received the Ph.D. degree in information and communication engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2005. In 2006, he joined Shenzhen University, where he is currently a Distinguished Professor and the Director of Sino-British Antennas and Propagation Joint Laboratory of MOST, the Director of Guangdong Engineering Research Center of Base Station Antennas and Propagation, and the Director of the Shenzhen Key Laboratory of Antennas and Propagation. He was selected as an Expert with Special Government Allowance from the State Council in China in 2024. He has authored or coauthored more than 360 research articles, seven books, and holds about 40 patents, with an h-index of 48 and over 10,000 citations. His research interests include wireless communications, antennas, and radio frequency. Dr. He is a Fellow of IET and a Fellow of the China Institute of Communications.

Speech Title:
AI-Enabled Integrated Sensing, Communication, and Computation Survey: Techniques, Status, and Perspectives

Abstract:
This presentation provides a comprehensive overview of integrated sensing, communication, and computation (ISCC), a key paradigm in the evolution toward 6G networks. ISCC aims to jointly design sensing, communication, and computation functionalities within a unified framework to enhance system performance, including improved energy efficiency, spectral efficiency, and reduced latency. The presentation first introduces the fundamental technologies of ISCC and their roles in the integrated architecture. It then categorizes and analyzes existing research from both computational and communication perspectives, highlighting recent advances and emerging trends. In addition, the interaction between ISCC and artificial intelligence (AI) is discussed, emphasizing their synergistic potential in enabling intelligent resource management and efficient system operation. Finally, key challenges and open research issues are outlined, along with potential future directions.

 

 

Prof. Junhui Zhao
Beijing Jiaotong University

Junhui Zhao received the M.S. and Ph.D. degrees from Southeast University, Nanjing, China, in 1998 and 2004, respectively. From 1998 to 1999, he worked with Nanjing Institute of Engineers at ZTE Corporation. He is currently a Professor at the School of Electronics and Information Engineering in Beijing Jiaotong University. His current research interests include wireless communication, internet of things, and information processing in transportation.

Speech Title:
Intelligent Transportation Systems: Advancing IoV through Sensing-Communication Integration and Collaborative Perception

Abstract:
This talk explores recent advances in communications and AI for intelligent transportation. It focuses on integrated sensing and communication for the Internet of Vehicles (IoV), covering waveform design and resource management to improve spectral efficiency in high-mobility settings. For autonomous driving, it discusses perception enhancements like multi-sensor lane detection and Bird's Eye View systems. A multi-vehicle collaborative perception framework is proposed to boost accuracy in complex traffic. The talk also introduces visual-aided beam alignment for IoV. These integrated approaches outline key pathways for next-generation ITS development.

 

 

Prof. Pasi Fränti
University of Eastern Finland

Pasi Fränti received his MSc and PhD in 1991 and 1994 from the University of Turku, Finland. His research interests include clustering algorithms, location-based services, analysis, and the optimization of health care systems, among others. He has been a professor in the University of Eastern Finland since 2000. During this time, he has published 138 journal and 189 conference papers. Prof. Fränti has served as associate editor for Pattern Recognition Letters, Journal of Electronic Imaging, Machine Learning with Applications, Applied Sciences, and AI+. He is one of the founding editors of the AIMS Journal of Applied Computing & Intelligence.

Speech Title:
Balanced clustering

Abstract:
Clustering is a pre-processing step before data analysis. The motivation is to group data objects based on similarity, detect communities, or simply create a representative sub-sample when complete data is too large. K-means and other clustering algorithms have two common issues. First, they can create unbalanced cluster sizes. Second, the clustering result may be inaccurate. In this talk, we present k-means variants that avoid these problems. We discuss them from three different viewpoints: (1) when a balanced cluster is needed; (2) when balance can be harmful; (3) how to achieve balanced clustering with high accuracy. We demonstrate the algorithms by examples from real-life applications.

 

 

Prof. Fan Liu
Southeast University

Fan Liu is currently a Professor with the National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China. He previously served as an Assistant Professor at Southern University of Science and Technology. He received his Ph.D. and BEng. degrees from Beijing Institute of Technology. He has also worked at University College London as a Visiting Researcher and Marie Curie Research Fellow. His research focuses on signal processing and wireless communications, particularly Integrated Sensing and Communications (ISAC). He holds key roles in IEEE communities, including founding Academic Chair of IEEE ComSoc ISAC-ETI and leadership roles in IEEE SPS. He serves as Associate Editor for several IEEE journals and has received numerous prestigious Best Paper Awards and recognitions, including Clarivate Highly Cited Researcher 2025.

Speech Title:
Sensing With Random Communication Signals

Abstract:
To maximize the efficiency of wireless resource utilization, 6G integrated sensing and communication (ISAC) systems must exploit the inherently random communication data payloads to serve both sensing and communication functions. This lecture provides a comprehensive technical overview of signal processing methodologies for communication-centric ISAC. We revisit the deterministic-random tradeoff and review signal models and processing pipelines. A key focus is the auto-correlation function (ACF) of random ISAC signals for multi-target ranging. Based on theoretical insights, design principles for modulation, constellation, and pulse shaping are discussed to enhance sensing without degrading communication. The lecture concludes with open challenges and future research directions.