 | Prof. Daqing Zhang (IEEE Fellow, Member of the Academia Europaea (MAE), CCF Distinguished Member)Peking University, China Daqing Zhang is a Chair Professor at Peking University and IP Paris. His research interests include ubiquitous computing, mobile computing, big data analytics and pervasive elderly care. He has published more than 400 technical papers in leading conferences and journals, with a citation of over 34800 and H-index of 96. He developed the OWL-based context model and Fresnel Zone-based wireless sensing theory, which are widely used by pervasive computing, mobile computing, wireless networks and service computing communities. He was the winner of the CCF TCPC Highest Science and Technology Award, the Ten Years CoMoRea Impact Paper Award at IEEE PerCom 2013, and the Ten Years Most Influential Paper Award at IEEE UIC 2019 and FCS 2023, the Best Paper Award Runner-up at ACM MobiCom 2022, the Distinguished Paper Award of IMWUT (UbiComp 2021), etc.. He is now in the editorial board of ACM IMWUT, ACM TOSN and CCF TPCI. Daqing Zhang is a Fellow of IEEE and Member of the Academy of Europe. Title: Preparing Every Home for Ageing-in-Place with Ubiquitous and Intelligent Wireless Sensing Abstract: With the ubiquitous deployment of Wi-Fi and 4G/5G infrastructure in each country, WiFi/4G/5G-based contactless sensing has become an ideal way for health and daily activity monitoring of elders in a non-intrusive manner, making every ordinary home suitable for Ageing-in-Place. In this work, I will introduce a series of WiFi/mmWave based vital sign and continuous daily activity monitoring systems for elders using home-owned WiFi/mmWave infrastructure. With the Wi-Fi sensing standard IEEE 802.11bf and 6G standard containing sensing capabilities rolling out soon, it’s expected that ubiquitous and intelligent wireless sensing will bring significant changes to sectors such as smart home, elderly care, health care and smart buildings. |
 | Prof. Weijia Jia (IEEE Fellow)Beijing Normal University, Institute of Artificial Intelligence and Future Networks, Zhuhai, China, China Professor Weijia Jia (Member of National Academy of Artificial Intelligence-NAAI, IEEE Fellow) is currently the Director of Institute of Artificial Intelligence and Future Networking, and the Director of Super Intelligent Computer Center, Beijing Normal University (BNU, Zhuhai); also a Chair Professor at BNBU(UIC), Zhuhai, Guangdong, China. He has served as the VP for Research at UIC in 6/2020-7/2024. Prior joining BNU, he served as the Deputy Director of State Kay Laboratory of Internet of Things for Smart City at the University of Macau and Zhiyuan Chair Professor at Shanghai Jiaotong University, PR China. His contributions have been recoganized for the research of edge AI, optimal network routing and deployment; vertex cover; anycast and multicast protocols; sensors networking; knowledge relation extractions; NLP and intelligent edge computing. He has over 800 publications in the prestige international journals/conferences and research books and book chapters with H-index 75. He has received the 2025 China Industry-University-Research Collaboration Innovation Figure Award, best product awards from the International Science & Tech. Expo (Shenzhen) in 2011/2012 and the 1st Prize of Scientific Research Awards from the Ministry of Education of China in 2017 (list 2), 1st Prize of Shanghai Science and Technology Award (2025, list 4) and top 2% World Scientists in Stanford-list (2020-2024) and many provincial science and tech awards. He has served as area editor for various prestige international journals, chair and PC member/keynote speaker for many top international conferences. He is the member of the National Academy of Artificial Intelligence (NAAI), Fellow of IEEE and the Distinguished Member of CCF. Title: Edge-Large Language Model Collaborative Computing and Applications Abstract: I first present how the Edge Computing works and the implemented works on Edge AI services and applications done in the AI Institute and Super Intelligent Computing Center at the Beijing Normal University (Zhuhai). Then we consider the 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 edges. To tackle with the challenges, I am happy to discuss a set of research issues of (1) Dynamic scheduling of container/image layers; (2) Collaborative work of edge servers and LLM for QoS for end-users under the resource constraints of edges; (3) Effective In-Context learning algorithm that balances the diversity and similarity of semantics for better services of users and (4) Novel system for edge 3D indoor scene recognition and understanding for the applications of embodied robot inspections. |
 | Prof. Zheng Yan (IEEE Fellow, AAIA Fellow, IET Fellow, AIIA Fellow)Xidian University, China Zheng Yan is currently a Huashan distinguished professor at the Xidian University, China. She is an External Member of Finnish Academy of Science and Letters, a Fellow of IEEE, IET, AAIA, and AIIA. She also worked as a visiting professor and a Finnish Academy Fellow at the Aalto University, Finland for over seven years. Her research interests are in cyber trust, security, privacy, and data analytics. She has led 30+ projects, sponsored by EU, Academy of Finland, NSFC, MOST, telecom industry, etc. At the helm of a research team with 70+ members, she has supervised 180+ post-doctoral researchers and graduates. She has authored 450+ publications, with 300+ first and corresponding authorships, featured prominently in top-tier venues. 18 of them are top 0.1% or 1% highly-cited ESI papers. She is the sole author of two books on trust management, utilized in teaching for a decade. She invented 220+ patents, among which 150+ patents (including 83 independent international inventions) have been adopted by industry, a few of them have been incorporated into international standards and widely used in practice with billions of users. She has delivered 50+ invited keynote speeches and talks at international conferences and world-leading companies. Her Google Scholar citation is over 20,000 with an H-index of 71. Title: AI-empowered Trust and Trustworthy AI Abstract: While Artificial Intelligence (AI) is contributing to the advancement of human society, it also presents us with new challenges. Its trustworthiness is worthy of in-depth exploration. This talk elucidates the aids of AI for trust and indicates the problems of AI’s trustworthiness, especially potential attacks on AI and the factors that impact AI trust. I will summarize various attacks suffered by AI in its life cycle. In particular, I will introduce recent research achievements of my team, including ChatGPT-aided trust evaluation, GNN-based robust and context-aware trust evaluation models, Large Language Model’s trust problems, a stealthy and practical audio backdoor attack with limited knowledge. Finally, several insights are proposed regarding AI trust management. |
 | Prof. Shiqian Wu (IEEE Senior Member)Wuhan University of Science and Technology, China Dr. Shiqian Wu is a Principal Scientist at the Institute of Advanced Displays and Imaging, Henan Academy of Sciences, China. He is also an adjunct professor at School of Electronic Information, Wuhan University of Science and Technology (WUST), China. Dr. Wu received his Ph.D. from Nanyang Technological University, Singapore, in 2001. Prior to joining WUST, he was an Assistant Professor, then Associate Professor at Huazhong University of Science and Technology from 1988 to 1997. From 2000 to 2014, he was a Research Fellow or Research Scientist with the Agency for Science, Technology and Research, Singapore. Prof Wu has co-authored two books and more than 350 scientific papers. He was recognized as a Most Cited Chinese Researcher (2014~2025, Elsevier). He was the recipient of the BEST 10% PAPER award in ICIP 2015 and the recipient of the Best Paper Finalist award in ICIEA 2020. His research interests include computer vision, image processing, pattern recognition, and artificial intelligence. Title: Knowledge-Embedded Machine Learning Abstract: Deduction and induction are two fundamental research methods. Machine learning, as a typical inductive solution, prevails in AI era. However, machine learning suffers from limited data, overfitting, overtraining etc., which results in poor generalization. In this talk, I provide several examples to show how knowledge could be embedded in machine learning. Experimental results demonstrate that such strategy can greatly improve performance and reduce sample numbers. |