Biography: Yinyan Zhang is currently a Professor at Jinan University, Guangzhou, China. He received the B.E. degree in Automation from Sun Yat-sen University, Guanghzou, China, and the Ph.D. degree in Computing from The Hong Kong Polytechnic University, Hong Kong, China in 2016 and 2019, respectively. He worked as a Postdoctoral Research Fellow at Department of Computing in The Hong Kong Polytechnic University for a few months. Since Dec. 2019, he has been with the College of Cyber Security, Guangzhou, China. His research interests include dynamic neural networks, security and control of multi-agent systems, and robotics. He has published 32 papers in IEEE Transactions, such as IEEE TAC, IEEE TIE, IEEE TCST, IEEE TII, IEEE TNNLS, and IEEE TYCB, and 4 papers in Automatica. He is an Associate Editor of IEEE Transactions on Industrial Electronics and Neural Processing Letters. He is included in the World Top 2% Scientists ranking in 2021, 2022, 2023, and 2024.

Title: Dynamic Neural Networks for Multi-Agent Systems and Manipulator Control
Abstract: Dynamic neural networks originate from Hopfield neural networks named after John Joseph Hopfield, who has been awarded the 2024 Nobel Prize in physics for foundational discoveries and inventions that enable machine learning with artificial neural networks. Different from widely known forward neural networks, dynamic neural networks are often modeled with differential equations and rely on the core of control theory, i.e., feedback, and do not need offline training based on large amounts of data. In this talk, I shall discuss some works on dynamic neural networks for the distributed coordination and competition of multi-agent systems, especially distributed k-winners-take-all, which is useful for distributed task allocation in robotics. I shall also talk about some works on dynamic neural networks for manipulator control, especially kinematic control of redundant manipulators and visual servoing of manipulators.

Biography: Wadii Boulila received his B.Sc. degree (1st Class Honours with distinction) in computer science from the Aviation School of Borj El Amri in 2005, and his M.Sc. degree in computer science from the National School of Computer Science (ENSI) at the University of Manouba, Tunisia, in 2007. In 2012, he earned his Ph.D. in computer science jointly from ENSI and Télécom Bretagne, University of Rennes 1, France. He is currently an Professor of computer science at Prince Sultan University, Saudi Arabia, and a Senior Researcher with the RIOTU Laboratory at Prince Sultan University. Additionally, he is a Senior Researcher with the RIADI Laboratory at the University of Manouba and was previously a Senior Research Fellow with the ITI Department at the University of Rennes 1, France. Wadii has received several prestigious awards, including the Young Researcher in Computer Science Award in 2021 from Beit El-Hikma, the Best Researcher Award from the University of Manouba in 2021, the Most Cited Researcher Award at the University of Manouba in 2022, and the Research Excellence Award from Prince Sultan University. He has participated in numerous research and industrial projects, and his primary research interests include data science, computer vision, big data analytics, deep learning, cybersecurity, artificial intelligence, and uncertainty modeling. Wadii has served as chair, reviewer, and TPC member for many leading international conferences and journals. His work has gained global recognition, and he has been nominated as one of the top 2% of scientists in his field by Stanford University. Wadii Boulila is an IEEE Senior Member, ACM Member, AAAI Member, and Senior Fellow of the Higher Education Academy (SFHEA) in the U.K.

Ttile: Adaptive and Resource-Efficient AI for Next-Generation Applications
Abstract: This talk explores cutting-edge advancements in artificial intelligence (AI) that emphasize adaptability, efficiency, and scalability. It examines innovative techniques that enable AI systems to learn effectively with minimal resources, including limited data, labels, or computational power. By focusing on approaches such as meta-learning, self-supervised frameworks, and task-specific adaptation, this talk highlights how these methods are driving breakthroughs across diverse real-world domains, from healthcare to industry and beyond. The talk aims to inspire new perspectives on the design of AI systems that are both robust and resource-conscious, advancing the future of transformative applications.

Biography: Dr. Jiehan Zhou is a Professor and Director of International Education and Research Programs at the School of Computer Science at Shandong University of Science and Technology and a Docent in the Department of Electronics and Information Engineering at the University of Oulu, Finland. He has a rich academic background and professional experience, having obtained a Ph.D. in Mechanical Automation from Huazhong University of Science and Technology in 2000 and a Ph.D. in Computer Engineering from the University of Oulu, Finland in 2011. He has worked at renowned research institutes and universities such as Tsinghua University, VTT Technical Research Centre of Finland, INRIA in France, Luxembourg Institute of Science and Technology (LIST), University of Oulu, and University of Toronto, Canada with over 20 years of international research experience across three continents. He has held positions such as Senior Scientist, Team Leader, Laboratory Director, General Manager of an Industrial Internet company in Germany, and Principal Engineer at a Research Institute on Cloud Computing in Canada. He also serves as a visiting professor at several Chinese universities, including Huazhong University of Science and Technology. Professor Jiehan Zhou has conducted extensive research in intelligent manufacturing, system modeling and simulation, industrial internet, and industrial big data analysis, achieving remarkable accomplishments in both academic and industrial fields. He has undertaken and participated in numerous Chinese, Canadian, European Union projects, including the ERCIM Fellowship Programme, the EU FP6 Amigo Project, and the EU ITEA4-CAM4Home Project.

Ttile: Update Soon
Abstract: Update Soon

Biography: Ling-Feng Shi (M’18-SM’19) received the B. S. degree in electronic engineering from Jiliang University of China, Hangzhou, China, in 1995 and both the M. S. degree and Ph. D in electronic engineering from Xidian University, Xi’an, China, in 2003 and 2008 respectively. He is a professor as the School of Electronical Engineering, Xidian University, China. He is currently engaged in research on navigation and position, wireless communication, high-speed circuit design, power electronic system design, electronic system integrated design, and signal integrity analysis. He has authored more than 100 journal papers (including more than 20 in IEEE) and 20 international conference papers, 19 national authorized invention patents, 1 authorized utility model, 6 national invention patents applied, 2 invention patents transferred. He has 2 textbooks and 1 academic work in English. He has won 2 second prize of provincial and ministerial science and technology progress award and 1 Outstanding teacher award in Xidian University.

Ttile: Indoor Positioning Technology Based on Smartphones
Abstract: Discuss the use of smartphones for navigation and positioning of indoor personnel, robots, drones, etc. in response to indoor navigation and positioning issues. Using the carrier of smartphones, pedestrian trajectory calculation, WIFI, Bluetooth, artificial intelligence and cloud computing technologies are used for indoor positioning or navigation, exploring solutions for indoor intelligent navigation. The main content of the report starts from the background significance of indoor positioning and the characteristics of artificial intelligence, and discusses the indoor positioning methods and development directions of multiple carriers such as personnel, robots, and drones.