Jianguo Ma received the doctoral degree in engineering in 1996 from Duisburg University, Duisburg, Germany. He was a faculty member of Nanyang Technological University (NTU) of Singapore and the funding director of the Center for Integrated Circuits & Systems of NTU from Sept 1997 to Dec. 2005 after his post-doctoral fellowship with Dalhousie University of Canada in Apr 1996 – Sept 1997. He was with the University of Electronic Science and Technology of China in Jan 2006 – Oct 2009 and he served as the Dean for the School of Electronic Information Engineering and the founding director of the Qingdao Institute of Oceanic Engineering of Tianjin University in Oct. 2009 – Aug 2016; he joined Guangdong University of Technology as a distinguished professor in Sept 2016 – Aug 2021. Dr. Ma served as the Vice Dean for the School of Micro-Nano Electronics of Zhejiang University in Sept, 2021 – Oct 2022 and as the Director of the Research Center of Intelligent Chips & Deices/Research Center of Novel Sensing & Intelligent Processing in Nov 2022 – April 2024. Since April 2024 he joins Zhongyuan University of Technology as the Academic Vice President. His research interests are: RFIC Applications to Wireless Infrastructures; Microwave and THz Microelectronic Systems; and AI empowered microwave system designs and optimizations. He served as the Associate Editor for IEEE Microwave and Wireless Components Letters in 2003 –2005; He was the member for IEEE University Program ad hoc Committee (2011~2013). Dr. Ma was the Member of the Editorial Board for Proceedings of IEEE in 2013-2018 He is Fellow of IEEE for the Leadership in Microwave Electronics and RFICs Applications Dr. Ma was serving as the Editor-in-Chief of IEEE Transactions on Microwave Theory and Techniques in 2020 –2022.

Speech Title: Modified Shannon’s Formula

Abstract: Shannon published his Theorem 2 with the Channel formula in 1949, which has been named as Shannon’s Theorem and Shannon’s Formula. The original Shannon’s formula is investigated first, then, multiple-input-multiple-output (MIMO) has been discussed and it is noticed that MIMO does NOT fully satisfy the original Shannon’s formula. Lots of wireless scenarios are not under the so-called far-field assumption which is one of the fundamental assumptions designing wireless systems. A modified Shannon’s formula has been proposed which coupled with the Poynting Vector in the original Shannon’s formula, combining Information Theory with Electromagnetic Field Theory for the first time, which can be used to design wireless system.

Haijun Zhang (Fellow of IEEE and AAIA) is currently a Full Professor and Dean at the University of Science and Technology Beijing, China. He was a postdoctoral research fellow in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Canada. He serves/ served as Track Co-Chair of WCNC 2020, Symposium Chair of Globecom'19, TPC Co-Chair of INFOCOM 2018 Workshop on Integrating Edge Computing, Caching, and Offloading in Next Generation Networks, and General Co-Chair of GameNets'16. He serves as an Editor of IEEE Transactions on Information Forensics and Security, IEEE Transactions on Network Science and Engineering, and IEEE Transactions on Communications. He received the IEEE CSIM Technical Committee Best Journal Paper Award, in 2018, IEEE ComSoc Young Author Best Paper Award, in 2017, and IEEE ComSoc Asia-Pacific Best Young Researcher Award, in 2019. He is the Chair of IEEE TCGCC and a distinguished Lecturer of IEEE. He is a Fellow of IEEE and AAIA.

Speech Title: 6G Network and Resource Optimization

Abstract: This talk will identify and discuss technical challenges and recent results related to resource optimization in 6G networks. The talk is mainly divided into four parts. The first part will introduce 6G mobile networks, discuss about the 6G mobile networks architecture, and provide some main technical challenges in 6G mobile networks. The second part will focus on the issue of resource management in 6G networks and provide different recent research findings that help to develop engineering insights. The third part will address the machine learning and deep learning method based future 6G networks and address some key research problems. The last part will summarize by providing a future outlook of 6G network and resource optimization.

Jiangchuan Liu is currently a Full Professor (with University Professorship) in the School of Computing Science at Simon Fraser University, British Columbia, Canada. He is a Fellow of The Canadian Academy of Engineering, an IEEE Fellow, and an NSERC E.W.R. Steacie Memorial Fellow. He is also an EMC-Endowed Visiting Chair Professor of Tsinghua University, Beijing, China (2013-2016), an Adjunct Professor of Tsinghua Shenzhen Graduate School (2016-2017), and a Distinguished Guest Professor of Tsinghua Shenzhen International Graduate School (2022-2024). He was a Microsoft Research Fellow and worked at Microsoft Research Asia (MSRA) in the summers of 2000, 2001, 2002, 2007, and 2011.
He received BEng (Cum Laude) from Tsinghua University in 1999, and PhD from The Hong Kong University of Science and Technology in 2003 (through HKUST's MED program with recommendation from Education Ministry of China and scholarship from Hong Kong Jockey Club; recipient of 2004 Hong Kong Young Scientist Award for the PhD work). From 2003 to 2004, he was an Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong.

Speech Title: Networked Live Video Analytics: From Design to Deployment

Abstract: Live video analytics over wide-area networks have seen a wide range of applications, e,g., environment monitoring, industry automation, and self-driving, to name but a few. In this talk, based on our recent research and development experiences, I will discuss our works on the algorithm and system design in this field, from severless-based pipeline optimization, 360-degree video analytics, to streaming analytics over space networking. We will then discuss the challenges and solutions toward realworld deployment in remote ecosystems.

Xiaoli is currently the Department Head and Senior Principal Scientist at the Institute for Infocomm Research, A*STAR, Singapore. He also serves as an adjunct full professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. With a diverse range of research interests, Xiaoli focuses on cutting-edge areas such as AI, data mining, machine learning, and bioinformatics. His contributions to these fields are evident through his extensive publication record, boasting over 350 peer-reviewed papers, and the recognition he has received, including over ten best paper awards. He has been serving as Editor-in-chief of the Annual Review of Artificial Intelligence and an Associate Editor for prestigious journals like IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems, as well as conference chairs and area chairs of leading AI, machine learning, and data science conferences, such as AAAI, IJCAI, ICLR, NeurIPS, KDD, ICDM etc. Beyond academia, Xiaoli possesses extensive industry experience, where he has successfully spearheaded over 10 R&D projects in collaboration with major industry players across diverse sectors, such as aerospace, telecom, insurance, and professional service companies. Xiaoli is an IEEE Fellow and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA). He has been recognized as one of the world's top 2% scientists in the AI domain by Stanford University and one of the top ranked computer scientists by Research.com.

Speech Title: Driving Transformation Across Industries with AI

Abstract: This talk highlights the transformative impact of AI across key industries, including manufacturing, aerospace, transportation, and education. Beginning with manufacturing and aerospace, we explore how AI-driven time series analytics are redefining predictive maintenance and condition monitoring, minimizing downtime, and significantly boosting productivity. In transportation, AI’s role in predictive modeling and real-time analytics is ushering in a new era of safety and efficiency, with smart traffic management systems that streamline traffic flow and reduce congestion. We delve into AI-driven HR analytics, where predictive models for employee attrition empower organizations to retain top talent and build a stable, motivated workforce. In education, online learning platforms generate vast amounts of data, opening opportunities for enhancing and personalizing the learning experience. We will explore how AI can be used for knowledge tracing, predicting student performance, and identifying at-risk students, enabling timely interventions and tailored learning pathways. Join us to discover how AI is driving innovation and enabling real-world transformation across diverse sectors.

Jianbin Qiu received the B.Eng. and Ph.D. degrees in Mechanical and Electrical Engineering from the University of Science and Technology of China, Hefei, China, in 2004 and 2009, respectively. He also received the Ph.D. degree in Mechatronics Engineering from the City University of Hong Kong, Kowloon, Hong Kong, in 2009. He is currently a Full Professor at the School of Astronautics, Harbin Institute of Technology, Harbin, China. He was an Alexander von Humboldt Research Fellow at the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg, Germany. His current research interests include intelligent and hybrid control systems, signal processing, and robotics. Prof. Qiu is a Fellow of IEEE and serves as the chair of the IEEE Industrial Electronics Society Harbin Chapter, China. He is an Associate Editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Industrial Informatics.

Speech Title: Adaptive Output-Feedback Boundary Control of Distributed Parameter Systems

Abstract: Distributed parameter systems, which are described by partial differential equations, widely exist in aerospace engineering, bioengineering, chemical engineering, and electrical engineering. Over the past decades, the control issues for distributed parameter systems have attracted considerable attention. In particular, the output-feedback adaptive control of distributed parameter systems is very challenging due to limited sensor measurements, unknown spatially varying parameters, and infinite-dimensional coupled dynamics. This talk will introduce some recent results on output-feedback adaptive boundary control for several classes of distributed parameter systems. The basic tools include observer canonical form, swapping identifier, and infinite-dimensional backstepping approach.

Dr. Jun Luo has been working on wireless sensing, deep learning, and computing system integration for more than two decades and has established a solid foundation on all these topics. Building upon this foundation, his research team has made contributions on mobile/ pervasive computing and smart sensing technologies, by leading several national research projects and corporate labs, aiming to transfer the research outcomes to practical applications. In the meantime, Dr. Luo and his team have kept publishing at relevant venues such as IEEE and ACM top conferences and journals, including MobiCom, CVPR, SenSys, INFOCOM, UbiComp, ToN, and TMC. With only 130+ publications, he has earned over 8300 Google Scholar citations, with two top-cited papers both bearing more than 1,000 of them.