Keynote Speaker in USIC 2025

Dr. Jun Yang
Loughborough University, UK
IEEE / IET / AAIA Fellow

Jun Yang (Fellow, IEEE) received the B.Sc. degree in automation from the Department of Automatic Control, Northeastern University, Shenyang, China, in 2006, and the Ph.D. degree in control theory and control engineering from the School of Automation, Southeast University, Nanjing, China, in 2011. He has been with the Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, U.K., since 2020, as a Senior Lecturer. His research interests include disturbance observer, motion control, visual servoing, nonlinear control, and autonomous systems. Dr. Yang is a fellow of IET and AAIA. He serves as an Associate Editor or a Technical Editor for IEEE Transactions on Industrial Electronics, IEEE/ASME Transactions on Mechatronics, and IEEE Open Journal of Industrial Electronics Society. He was a recipient of the EPSRC New Investigator Award.

Assoc. Prof. Cheng Siong LEE
Monash University, Australia

Vincent CS Lee (PhD, FIEAust, SMIEEE) is an Associate Professor (top level), Department of Data Science and Artificial Intelligence, Faculty of IT, Monash University (Go8), Australia. His research spans across AI in Digital Health, Control, Signal and Information Processing, Financial Innovation Technology, UAV based Edge Computing, Multimodal Deep Active Learning and Reasoning, Educational Data Mining. Lee published together 200+ Q1 papers in IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Signal Processing, IEEE Selected Areas in Communications, European Journal of Operational Research, Expert Systems with Applications, Neurocomputing, IEEE IoT Journal, Journal of Educational Computing Research; and in CORE A/A* Peer-review International Conferences proceedings (AAAI, IJCAI, ICDM, ICML, ICWS, ICDE, PAKDD, CIKM, WWW, IEEE IC Signal Processing, IC-EDM). Lee also served as invited keynote speakers for IEEE and ACM Flagship conferences and General Chair, Co-chair Technical Programs and Co-chair & Keynote speaker for IC on Education Network & Information Technology, ICNEIT2024, Dalian, Program Co-chair APIT2025 HK, Technical Program Chair for IC AIDF2025 12-15 June 25 in Chengdu. Since 2020, he is serving Associate Editor for Journal of Intelligent Manufacturing (Springer, SCI Mago Journal Rank Best Quartile Q1); Editorial Board, Science Progress (a Q1 Journal) in “Computer and Information Science”.

Assoc. Prof. Chen Lyu
Nanyang Technological University, Singapore

Dr. LYU, Chen (often spelled as Chen Lv) is an Associate Professor at School of Mechanical and Aerospace Engineering, Nanyang Technological University. He also holds a joint appointment with School of Electrical and Electronic Engineering. He is Director of Automated Driving and Human-Machine System (AutoMan) Research Lab, Cluster Director in Future Mobility Solutions at ERI@N, the Thrust Lead in Smart Mobility and Delivery at Continental-NTU Corp Lab, and the Program Lead in Next Generation AMR, Schaeffler-NTU Joint Lab. He received his Ph.D. degree from Department of Automotive Engineering, Tsinghua University, China in 2016, with a joint PhD from EECS Dept., University of California, Berkeley, USA. Before joining NTU as a Nanyang Assistant Professor in June 2018, he was a Research Fellow at Advanced Vehicle Engineering Center, Cranfield University, UK during 2016-2018. His research focuses on autonomous driving, human-machine collaboration, robotics, and CPS, where he has contributed 4 books, over 100 papers, and 12 granted patents.
He serves as Associate Editor for many top tier journals, including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology, and IEEE Transactions on Intelligent Vehicles, etc. He is elected as IEEE VTS Motor Vehicles Committee Member, the only delegate from Asia. He has received many honors and awards, selectively: SAE Ralph R. Teetor Educational Award (2023), Nanyang Research Award (Young Investigator) 2022, the Machines Young Investigator Award (2021), winner of Waymo Open Dataset Challenge (2022 and 2021), winner of IEEE VTS Motor Vehicles Challenge (2022), EVS’34 Excellent Paper Award (2021), Automotive Innovation Best Paper Award (2020) and IEEE Intelligent Vehicle Symposium Best Paper Award (2018).

Speech Title: Human-like Autonomy for Smart Mobility and Robotic
Abstract: The long-term goal of artificial intelligence (AI) systems is to make them learn, think and act smartly like human beings. As a typical application of AI, autonomous vehicles (AVs) become one of the most potential and ultimate ambitions in the smart mobilities. They primarily designed to replace human drivers during driving in order to enhance the performance and avoid the possible fatalities. In the near future, AVs are believed to share public roads with human-driven vehicles, which requires AVs to be smart and able to behave like human drivers, being reasonable and predictable to other road users. However, due to their limited smartness, current AVs are still lack of robust situation understanding, interaction prediction and human-like decision-making abilities when interacting with others, particularly in complex and emergency situations. Therefore, human-machine hybrid intelligence, as well as human-machine collaboration, are of great importance to ensure the safety and further improve the smartness of mobility systems, during long-term development and large-scale deployment of AVs. In this talk, the recent studies in human-like autonomy and human-machine hybrid intelligence for future mobility will be presented. First, a data-driven prediction and decision-making framework for human-like autonomous driving will be introduced. Next, a novel human-machine collaboration framework with bi-directional performance augmentation ability developed for automated vehicles and robotics will be presented in detail.