Speakers 2024
Keynote Speaker Ⅰ
| Speech Title: To be updated | |
| Prof. Fuchun Sun IEEE Fellow, CAAI Fellow and CAA Fellow Tsinghua University, China |
Biography: Dr. Fuchun Sun is a full professor, and vice director in the academic tenure committee of department of computer science and technology, director of intelligent robot center of AI research academy, Tsinghua University. He also serves as Vice president of China artificial intelligence society and executive director of China automation society. His research interests include robotic perception and intelligent control. He has won the Champion of Autonomous Grasp Challenges in IROS2016 and IROS 2019. Dr. Sun is the recipient of the excellent Doctoral Dissertation Prize of China in 2000 by MOE of China and the Choon-Gang Academic Award by Korea in 2003, and was recognized as a Distinguished Young Scholar in 2006 by the Natural Science Foundation of China. He is elected as IEEE Fellow and CAAI Fellow in 2019, CAA Fellow in 2020.He served as an associated editor of IEEE Trans. on Neural Networks during 2006-2010, IEEE Trans. On Fuzzy Systems during 2011-2018, IEEE Trans. on Cognitive and Development since 2018 and IEEE Trans. on Systems, Man and Cybernetics: Systems since 2015. He was invited to make a plenary talk or keynote speech at the international summit ICRA 2021, IROS 2019, AAAI 2021, etc.
Abstract: To be updated
Keynote Speaker Ⅱ
| Speech Title: Magnetic Miniature Robots for Biomedicine: From Individual and Modular Designs to Microswarms | |
| Prof. Li Zhang IEEE Fellow The Chinese University of Hong Kong, China |
Biography: Li Zhang is a Professor in the Department of Mechanical and Automation Engineering (MAE) and a Professor by Courtesy in the Department of Surgery at The Chinese University of Hong Kong (CUHK). He is also a director of the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences (CAS) – CUHK Joint Laboratory of Robotics and Intelligent Systems. Dr. Zhang is elected as a Fellow of IEEE (FIEEE), Royal Society of Chemistry (FRSC), Asia-Pacific Artificial Intelligence Association (FAAIA), The Hong Kong Institution of Engineers (FHKIE), a member of the Hong Kong Young Academy of Sciences (YASHK), and an Outstanding Fellow of the Faculty of Engineering at CUHK. Dr. Zhang’s main research interests include small-scale robotics and their applications for translational biomedicine. He has authored or co-authored over 300 publications, including Nature Reviews Bioengineering, Science Robotics (3), Nature Machine Intelligence (3), Science Advances (10), Nature Communications (5), as the corresponding author. He currently serves as (or was) Senior Editor/Associate Editor/Editorial Board Member of ten international journals, such as IEEE T-RO, IEEE T-ASE, IEEE/ASME T-MECH, IEEE T-MRB, IEEE RA-L, Advanced Intelligent Systems (Wiley), International Journal of Extreme Manufacturing (IOP), Biomicrofluidics (AIP), Research (SPJ-AAAS), Med-X (Springer), and Bio-design and Manufacturing (Springer).
Abstract: Robotics at small scales has attracted considerable research attention both in its fundamental aspects and potential biomedical applications. As the characteristic dimensions of the robots or machines scaling down to the milli-/microscale or even smaller, they are ideally suited to navigating in tiny and tortuous lumens inside the human body which are hard-to-reach by regular medical devices. Although the materials, structural design, and functionalization of micro-/nanorobots have been studied extensively, several key challenges have not yet been adequately investigated for in vivo applications, such as adaptive locomotion in dynamic physiological environments, in vivo localization with clinical imaging modalities, the efficiency of therapeutic intervention, biosafety, and their autonomy for the intervention tasks. In this talk, I will first present our recent research progress on development of magnetic miniature robots, from individual and modular designs to the microswarms, for rapid endoluminal delivery. Then the key challenges and perspective of using magnetic miniature robots for localized therapy and clinically relevant applications with a focus on endoluminal procedures will be discussed.
Keynote Speaker Ⅲ
| Speech Title: Machine Learning for Brain-Computer Interfaces | |
| Prof. Dongrui Wu IEEE Fellow Huazhong University of Science and Technology, China |
Biography: Dongrui Wu (IEEE Fellow) is Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. Prof. Wu's research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (12000+ Google Scholar citations; h=57). He received the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation (CAA) Early Career Award in 2021, the Ministry of Education Young Scientist Award in 2022, and First Prize of the CAA Natural Science Award. His team won National Champion of the China Brain-Computer Interface Competition in two successive years (2021-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.
Abstract: A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Thus, sophisticated machine learning approaches are needed for accurate and reliable EEG-based BCIs. This talk will introduce the basic concepts of BCIs, review the latest progress, and describe several newly proposed machine learning approaches for BCIs.
Keynote Speaker Ⅳ
| Speech Title: A Systematic Approach to Wearable Roboticd Development | |
| Assoc. Prof. Haoyong Yu National University of Singapore, Singapore |
Biography: Dr. Yu Haoyong is an Associate Professor of the Department of Biomedical Engineering at the National University of Singapore. He received his Bachelor’s Degree and Master’s Degree from Shanghai Jiao Tong University and his PhD degree from Massachusetts institute of Technology (MIT). His current research interests include biomedical robotics and devices, rehabilitation engineering and assistive technology, service robots, human robot interaction, intelligent control and machine learning. Dr. Yu is the Associate Editor of IEEE Transactions on Automation Science and Engineering and IEEE/ASME Transactions on Mechatronics.
Abstract: With the rapid population aging, wearable Robotics, commonly known as Exoskeleton Robots, are believed to have great potential applications in many areas in both industry and Healthcare. However, the current market size of wearable robotics is still quite small compared with other robotics sectors due to the limitations in design, user experience, and high cost. At NUS Biorobotics Lab, we are developing a series of wearable robotics with wide range applications. We adopt a modular approach based a set of core technologies and components developed in the lab, which includes compliant actuation, cable mechanism, wearable sensors and learning based movement detection algorithms. We achieved better portability and better human robot interaction due to our compliant actuation and cable transmission and novel mechanism design. In this talk, we will give a brief introduction of some of our wearable robots and their potential applications.
More speakers are to be announced......