Biography: Prof. Zhi Li is a distinguished member of China Computer Federation (CCF), standing committee member of its Technical Council on Software Engineering (TCSE), and a member of its Technical Council on Service Computing and Formal Methods, senior member of IEEE and ACM. He graduated with a BSc degree from Fudan University in 1991, an MSc degree from the University of York in 2004, and a PhD degree from The Open University in 2008. Prof. Li had spent over 10 years doing professional and technical work before he entered academia in 2001, with subsequent 9 years in the UK. His research interests are modeling, verifying, testing and validating Human-Cyber-Physical Systems (HCPSs) based on a problem-oriented approach, natural language processing for requirements engineering (NLP4RE), Artificial General Intelligence (AGI), and Human-Computer Interaction (HCI). His research has been sponsored by 3 grants from the National Natural Science Foundation of China, and 5 grants from Ministry of Education of China, Guangxi Natural Science Foundation, and Guangxi Scientific Research & Technological Development. He has published over 70 research papers. He has given 2 keynote speeches and over 10 invited talks in international conferences, and he is the leader and one of the main contributors to a suite of Computer-Aided Requirements Engineering (CARE) tools for Problem-Oriented Software Development (POSD).

Biography: Pavel Loskot joined the ZJU-UIUC Institute in January 2021 as an Associate Professor after being 14 years with Swansea University in the UK. He received his PhD degree in Wireless Communications from the University of Alberta in Canada, and the MSc and BSc degrees in Radioelectronics and Biomedical Electronics, respectively, from the Czech Technical University of Prague in the Czech Republic. In the past 25 years, he was involved in numerous collaborative research and development projects, and also held a number of paid consultancy contracts with industry. He is the Senior Member of the IEEE, Fellow of the Higher Education Academy in the UK, and the Recognized Research Supervisor of the UK Council for Graduate Education. His current research interests focus on mathematical and probabilistic modeling, statistical signal processing and classical machine learning for multi-sensor data.

Biography: Mohan Li is a faculty member at Cyberspace Institute of Advanced Technology, Guangzhou University, recruited under the university’s "Hundred Talents Program." She also serves as the Executive Deputy Director of the Guangzhou University-Surfilter Network Technology Industrial Control Network Security Situational Awareness Joint Laboratory. She works in the team of Academician Binxing Fang at Guangzhou University. She earned her bachelor's, master's, and doctoral degrees from Harbin Institute of Technology under the supervision of Professor Jianzhong Li. Her research focuses on AI security and intrusion detection. She has published in top conferences and journals such as NeurIPS, AAAI, and TIFS, covering neural backdoor detection, hallucination in large models, and privacy threats in machine learning. She is a two-time recipient of the Guangdong Computer Society Outstanding Paper Award and has authored or contributed to books on big data governance, recommender systems, and AI security.

Biography: Hao Jiang received the B.S. and M.S. degrees in electrical and information engineering from Nanjing University of Information Science and Technology, Nanjing, China, in 2012 and 2015, respectively, and the Ph.D. degree from Southeast University, Nanjing, in 2019. From 2017 to 2018, he was a visiting student with the Department of Electrical Engineering, Columbia University, New York, NY, USA. Since 2019, he has been an associate Professor with the School of Information Science and Engineering, Nanjing University of Information Science and Technology, Nanjing. His current research interests are in general area of wireless channel measurements and modeling, B5G wireless communication networks, signal processing, machine learning, and AI-driven technologies. Reconfigurable intelligent surface (RIS) is one of the potential key technologies for sixth generation (6G) communications, which has the characteristics of low cost, low complexity, and easy deployment. By applying control signals to adjustable elements on the electromagnetic unit, it has the ability to adjusting the wireless communication environments, which provides a new opportunity to improve the high energy-efficiency performance of wireless communication systems. This talk provides a comprehensive overview of channel modeling and characteristics analysis for RIS-assisted unmanned aerial vehicle (UAV) high energy-efficiency communications. Firstly, based on the research basis of the UAV communications technologies, we clarify the necessities of introducing the RIS into UAV communications. Then, we summarize the key technologies for channel modeling and characteristics analysis for RIS-assisted UAV communications. Finally, we point out some future research directions in RIS-assisted UAV channel modeling and characteristics analysis.