Invited Speakers

Fanxin Kong, Syracuse University, USA

Speech Title: Attack-Resilient Cyber-Physical Systems

Dr. Fanxin Kong is an assistant professor in the department of electrical engineering and computer science at Syracuse University. Dr. Kong's research interests spread security, real-time, energy-efficiency, and intelligence aspects for Cyber- Physical Systems (CPS) and Internet of Things (IoT). His current research focuses on real-time attack detection and recovery for autonomous CPS with applications such as autonomous vehicles and unmanned arial vehicles. His research techniques include machine learning, optimization, formal methods, and game theory. He has published over 50 research articles at highly reputable venues including top-tier conferences such as RTSS, RTAS, ICCPS, EMSOFT, IoTDI, DAC, INFOCOM, various IEEE/ACM Transactions, and three monographs at FnTs in EES and FnTs in EDA with now publishers.

Kun Cao, Jinan University, China
曹坤, 暨南大学

Speech Title: HELCFL: High-Efficiency and Low-Cost Federated Learning in Heterogeneous Mobile-Edge Computing

Kun Cao received the Ph.D. degree in Computer Science from East China Normal University, Shanghai, China, in 2020. He is currently an Associate Professor with the College of Information Science and Technology, Jinan University, Guangzhou, China. His current research interests are in the areas of Internet of things, edge/fog/cloud computing, and cyber-physical systems. He has published 20+ refereed papers in these areas, most of which are published in premium conferences and journals, including IEEE TCAD, IEEE TII, IEEE TAES, IEEE TSUSC, IEEE TPDS, IEEE COMST, ACM CSUR, etc. He was a recipient of the Best Paper Award from the 13th IEEE International Conference on Internet of Things, in 2020. He was ranked the World's Top 2% Scientists by Stanford University and Elsevier's Mendeley Data and Scopus citation database on August 1, 2021 (ranked #19 in China Mainland in the subfield of Computer Hardware & Architecture). Since 2020, he has been serving as an Associate Editor for the Journal of Circuits, Systems, and Computers.

Cheng Dai, Sichuan University, China
代成, 四川大学

Speech Title: Lightweight Skeleton Behavior Recognition Deep Model for Smart Embedded Monitoring Systems by Using Knowledge Distillation

Cheng Dai is an associate professor in the school of Computer Science, Sichuan University. He received the Ph.D. degree from University of Electronic Science and Technology of China, 2021. During his Ph.D, He received CSC Scholarship program, and serves as visiting Ph.D student in Mcmaster University from December 2019 to December 2020. He has published more than 10 papers in top journals (e.g., IEEE Network, IEEE Transactions on Industrial Informatics, IEEE Internet of Things Journal, IEEE Transactions on Vehicular Technology, Applied Soft Computing), and received Best Paper Award from HPCC-2020. He has always served as a reviewer for several prestigious Journals, such as IEEE IoT, IEEE TII, IEEE TSVT, IEEE TSP. He actively participates in International academic exchange, and he takes an active part in academic symposia. He received 2021 outstanding PhD Dissertation Award. His interests primarily focus on the AI and Embedded Systems with an emphasis on energy-efficient accelerators, and deep model compression.

Liying Li, Nanjing University of Science and Technology, China
李丽颖, 南京理工大学

Speech Title: Data Availability Optimization for Cyber-Physical Systems

Liying Li received her Ph.D. degree from the Department of Computer Science and Technology, East China Normal University, Shanghai, China, in 2022. She is now an Assitant Professor in the School of Computer Science and Engineering, Nanjing University of Science and Technology, China. She has published more than 20 works in top journals and conferences, including IEEE TC, IEEE TCAD, IEEE TPDS, IEEE TASE, DATE, etc. She has served as a TPC member of multiple conferences including HPCC and CPSCom. Her current research interests are in the areas of cyber-physical systems, IoT systems, and distributed artificial intelligence.