Prof. Pin-Han Ho

IEEE Fellow

University of Waterloo, Canada

Bio: Dr. Pin-Han Ho is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research spans a wide range of topics, including broadband wired and wireless communication networks, survivable network design, wireless communications, cyber-physical systems, and the Internet of Things (IoT). One of Dr. Ho's significant contributions, in collaboration with his PhD student Dr. James She, is the invention of Wireless Media Express™, a groundbreaking technology designed to address the challenge of wireless channel fading. This fundamental issue limits the effectiveness of wireless service providers in multicasting to intended receivers. Wireless Media Express™ generates an intelligent multicast signal that optimizes video quality for all receivers, regardless of their channel conditions. 

This innovative technology has transformative applications, such as enabling users to access TV channels on handheld devices while maintaining consistent, high-quality live broadcasts. It also allows businesses to efficiently upload video advertising to various digital displays across a city—such as highway billboards, shopping mall screens, and subway terminals—targeted to different audiences based on the time of day. 

Dr. Ho's ongoing research continues to drive advancements in communication technologies, with real-world applications that significantly enhance both consumer experiences and business operations.

Speech Title: Rethinking Millimeter-Wave Radar: Signal Processing, Applications, and Frequency-as-Aperture

Abstract: Millimeter-wave (mmWave) radar has rapidly evolved from a niche sensing technology into a core enabler of perception in future wireless systems. Driven by advances in RF integration and signal processing, mmWave radars now support high-resolution sensing for applications ranging from autonomous driving to human-centered sensing. At the same time, the convergence of sensing and communication has given rise to the paradigm of Integrated Sensing and Communication (ISAC), where radar functionality is increasingly embedded into wireless radios. This talk is organized into three parts. First, we provide a tutorial overview of mmWave radar systems, with an emphasis on the underlying communication and signal processing techniques, including waveform design, MIMO radar, frequency-modulated continuous-wave (FMCW) operation, and spatial sampling strategies. Second, we survey emerging mmWave radar applications within the ISAC framework, highlighting diverse use cases such as automotive radar, human vital sign monitoring, gesture and posture sensing, and smart environments. Finally, we introduce the Frequency-as-Aperture (FaA) paradigm, which reinterprets frequency agility as a virtual sensing aperture, enabling near-field perception using a single RF chain and minimal hardware overhead. The talk concludes with a discussion of open challenges and future directions toward scalable, embeddable, and privacy-preserving mmWave sensing systems. 

Prof. Yuanwei Liu

IEEE Fellow, AAIA Fellow, AIIA Fellow

The University of Hong Kong, China

Bio: Yuanwei Liu is a tenured full Professor in Department of Electrical and Electronic Engineering (EEE) at The University of Hong Kong (HKU), and also a visiting Professor in Queen Mary University of London. He is IEEE Fellow, AAIA Fellow, AIIA Fellow, web of Science Highly Cited Researcher (2021 to present), young member of the Hong Kong Academy of Engineering. His research interests include pinching antenna systems, next generation multiple access, integrated sensing and communications, reconfigurable intelligent surface, near-field communications and mobile edge generation. He is listed as one of 35 Innovators Under 35 China in 2022 by MIT Technology Review. He serves as an IEEE Communication Society Distinguished Lecturer, an IEEE Vehicular Technology Society Distinguished Lecturer, chair of IEEE Signal Processing and Computing for Communications (SPCC) Technical Committee, the academic Chair for the Next Generation Multiple Access Emerging Technology Initiative. He received IEEE ComSoc Outstanding Young Researcher Award for EMEA in 2020. He received the 2020 IEEE SPCC Technical Committee Early Achievement Award, IEEE Communication Theory Technical Committee (CTTC) 2021 Early Achievement Award. He received IEEE ComSoc Outstanding Nominee for Best Young Professionals Award in 2021. He received four IEEE best paper awards. He serves Co-Editor-in-Chief of IEEE ComSoc Technical Newsletter, Area Editor of IEEE TCOM/CL, Editor of IEEE COMST/TWC/TCCN /TVT/TNSE, (leading) guest editor of Proceedings of IEEE/IEEE JSAC/JSTSP etc., and the rapporteur of ETSI Industry Specification Group on RIS Industry Specification Group Work Item 6.

Speech Title: Pinching-Antenna Systems (PASS): From Wireless to Near-Wired Communications

Abstract: The Pinching-Antenna System (PASS) represents a revolutionary advancement in flexible antenna technology, which enhances wireless communication by establishing strong line-of-sight connectivity, minimizing free-space path loss, and enabling dynamic reconfiguration of antenna arrays. This talk will introduce the basic principles of PASS based on a prototype and explore how PASS transforms communication systems from a conventional wireless paradigm towards a near-wired paradigm. The talk will also cover the physics principles and signal modeling techniques behind PASS, the activation method of pinching antennas, and the transmission structures. Several case studies will be discussed to showcase PASS capabilities, including performance analysis, beamforming design, and the integration of deep learning. Finally, the talk will highlight key research challenges and outline promising opportunities for future exploration in this innovative technology.

Prof. Weijia Jia

IEEE Fellow

Beijing Normal University (BNU-Zhuhai), China

Bio: Prof. Weijia Jia is currently a Chair Professor at Beijing Normal-Hong Kong Baptist University and a Professor at Beijing Normal University (BNU), Zhuhai, Guangdong, China. He also serves as the Director of Joint BNU-BNBU Institute of Artificial Intelligence and Future Networking. Prior joining BNU/BNBU, he served as the Chair Professor and Deputy Director of the State Kay Laboratory of Internet of Things for Smart City at the University of Macau and Zhiyuan Chair Professor at the Shanghai Jiaotong University, PR China.  He received BSc/MSc from Center South University, China in 1982-1984 and PhD from Polytechnic Faculty of Mons (now with the University of Mons), Belgium in 1991-1993, respectively; all in computer science. For 1993-1995, he joined German National Research Center for Information Science (GMD) in Bonn (St. Augustine), Germany as a research fellow. From 1995-2013, he worked in City University of Hong Kong as an Assit./Assoc./full professor. 

His contributions have been recognized as the theory and algorithms of AI (NLP in particular) optimal network routing and deployment, intelligent edge computing, vertex cover, anycast and multicast protocols, sensors networking, and knowledge relation extractions. He has over 500 publications in the prestige international journals/conferences (e.g. IEEE/ACM Transactions/journals, Infocom, AAAI etc.) and research books and book chapters. His current H-index is 67 (Google scholar, citations 15000+). He received the 1st Prize of Scientific Research Awards from the Ministry of Education of China in 2017 and many provincial science and technology awards. He has guided the students to attain 30+ top prizes in various top international conferences and  AI competitions. Based on his research outcome and input into the system implementations, he received the best product awards from the International Science & Tech. Expo (Shenzhen, China) in the consecutive years of 2011 and 2012. He has served as area editor for various prestige international journals (e.g. IEEE Transactions on Parallel and Distributed Computing and Computer Communications), chair and PC member/keynote speaker for top international conferences. He has been recognized as Chinese National Expert and listed in 2020-2022 as Top 2% of life Scientists on Stanford List. He is the Fellow of IEEE and the Distinguished Member of China Computer Federation (CCF).

Speech Title: Edge AI and LLM Collaborative Applications

Abstract: Large Language Models (LLMs) are widely used across various domains, but deploying them in cloud data centers often leads to significant response delays and high costs, undermining Quality of Service (QoS) at the network edge. We first present how the Edge AI function layers are scheduled in the containers and then we consider the cases of collaborative work of edge servers and LLM for QoS of end-users. Although caching LLM request results at the edge using vector databases can greatly reduce response times and costs for similar requests, this approach has been overlooked in prior research. To address this, we propose a novel non-invasive RAG approach called Vector database-assisted Cloud-Edge collaborative LLM QoS optimization system that caches LLM request results at the edge using vector databases, thereby reducing response times for subsequent similar requests. Unlike methods that modify LLMs directly, our system leaves the LLM’s internal structure intact and is applicable to various LLMs. Building on the system, we formulate the QoS optimization problem as a Markov Decision Process and design an algorithm based on Multi-Agent Reinforcement Learning. Our algorithm employs a diffusion-based policy network to extract the LLM request features, determining whether to request the LLM in the cloud or retrieve results from the edge’s vector database. Implemented in a real edge system, our experimental results demonstrate that our system significantly enhances user satisfaction by simultaneously reducing delays and resource consumption for edge users of LLMs. We further explore the non-invasive RAG approach to the intelligent applications of real estate by introducing a system RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate market. We finally briefly introduce our new In-Context learning algorithm that balances the diversity and similarity of semantics for resource constraints of edges. 

Prof. Weifa Liang

IEEE Fellow

City University of Hong Kong, China

Bio: Weifa Liang is a professor in the Department of Computer Science at City University of Hong Kong. He received his Bachelor of Science degree from Wuhan University in 1984 and his Master of Engineering degree from the University of Science and Technology of China in 1989, followed by a Ph.D. in Computer Science from the Australian National University in 1998. Prior to joining City University of Hong Kong, he was a professor at the Australian National University. 

His research interests include wireless sensor networks, mobile edge computing (MEC), network function virtualization (NFV), the Internet of Things (IoT), the design and analysis of approximation and online algorithms, parallel and distributed algorithms, combinatorial optimization, and graph theory. He has consistently published papers in renowned journals (such as TON, TMC, TPDS, TC, TCOM, TWC, JPDC, and Theoretical Computer Science) and conferences (including INFOCOM, ICDCS, IPDPS, ICPP, and PerCom). He currently serves as an Associate Editor for IEEE Transactions on Communications.

Speech Title: AoI-Aware Inference Services in DT-Assisted Edge Computing  

Abstract: The Mobile Edge Computing (MEC) paradigm gives impetus to the vigorous advancement of the Internet of Things (IoT), through provisioning low-latency computing services at the network edge. The emerging digital twin (DT) technique has grown in the IoT community, and bridges the gap between physical objects and their digital representations in MEC networks, thereby enabling real-time data analysis, emulating system dynamics, predicting behaviours of physical objects, and helping humans for decision-making.

In this talk, we consider AoI-aware, delay-sensitive inference services in a DT-assisted MEC network by jointly optimizing the freshness of services measured by the Age of Information (AoI) and service response delays, and we focus on the following three AoI-aware inference service problems. The first one is AoI-aware, differential inference services in an MEC network through Digital Twin Network (DTN) slicing, where a DTN is a virtual network consisting of a set of inference models with their  source data from a group of DTs, and the inference models provide users with differential quality of services, for which the expected cost minimization problem is formulated that jointly places DT instances and inference model instances, and develop efficient algorithms for the placements. Further, an online algorithm with a provable competitive ratio for dynamic DTN slicing request admissions is also developed, without the knowledge of future request arrivals. The second one is to minimize the weighted sum of the cumulative freshness of query results and the total delay incurred by providing inference services, for which a performance-guaranteed approximation algorithm is devised through exploring nontrivial trade-offs between the two conflicting optimization objectives: model freshness and service delay. The last one is to maximize user service satisfaction on services by replica DT placements for each object. Two optimization