Keynote Speakers

Keynote Speakers (Alphabetize by Last Name)

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Short Bio: Gang Feng received the B.Eng and M.Eng. Degrees in Automatic Control from Nanjing Aeronautical Institute, China in 1982 and in 1984 respectively, and the Ph.D. degree in Electrical Engineering from the University of Melbourne, Australia in 1992. Professor Feng was a Lecturer in Royal Melbourne Institute of Technology, 1991 and a Senior Lecturer/Lecturer, University of New South Wales, 1992-1999. He has been with City University of Hong Kong (CityU) since 2000, where he is now a Chair Professor of Mechatronic Engineering. He has received Alexander von Humboldt fellowship, the IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the outstanding research award and President award of CityU, and several best conference paper awards. He is listed as a SCI highly cited researcher by Clarivate Analytics since 2016. His research interests include intelligent systems and control, networked control systems, and multi-agent systems and control. Professor Feng is a fellow of IEEE. He has been the Associate Editor of IEEE Trans. Automatic Control, IEEE Trans. on Fuzzy Systems, IEEE Trans. Systems, Man, & Cybernetics, Mechatronics, Journal of Systems Science & Complexity, Journal of Guidance, Navigation & Control, and Journal of Control Theory and Applications. He is also on the advisory board of Unmanned Systems. For more information could be viewed at homepage: https://www.cityu.edu.hk/bme/megfeng/
Abstract: This talk first gives a brief overview on artificial intelligence, fuzzy logic and fuzzy control, highlighting the myth on fuzzy logic and several fuzzy control methods. It then presents the key idea of model based fuzzy control including its motivation and advantages. Finally, some challenges in intelligent fuzzy control are revealed.

Prof. Dimitrios Georgakopoulos is currently the Director of the ARC Research Hub for Future Digital Manufacturing, the Director of Swinburne's key IoT Lab, and the University's Industry 4.0 Program program leader. Before that he served as Research Director (2008-2014) of CSIRO's ICT Centre and a Professor at RMIT University (2014-2016). At CSIRO he leds the Information Engineering Laboratory, which was the largest Computer Science research program in Australia. Prior to joining CSIRO, he held research and management positions in industrial laboratories in the USA, including Telcordia Technologies (where he helped found two of Telcordia's Research Centers in Austin, Texas, and Poznan, Poland); Microelectronics and Computer Corporation (MCC) in Austin; GTE (currently Verizon) Laboratories in Boston; and Bell Communications Research (Bellcore) in New Jersey. He is a CSIRO Adjunct Fellow since 2014. He has authored approximately 283 journal and conference articles that have received approximately 23,00 citations. According to the October 2022 update of Elsevier's science-wide author databases of standardized citation indicators, he is in the 2% of top-cited authors globally (top 1% if self-citations are excluded) in both career and 2022 rankings. For more information could be viewed at homepage: https://www.swinburne.edu.au/research/our-research/access-our-research/find-a-researcher-or-supervisor/researcher-profile/?id=dgeorgakopoulos

Dr. Yi Pan is currently a Chair Professor and the Dean of College of Computer Science and Control Engineering at Shenzhen Institue of Advanced Technology, Chinese Academy of Sciences, China and a Regents' Professor Emeritus at Georgia State University, USA. He served as Chair of Computer Science Department at Georgia State University from 2005 to 2020. He has also served as an Interim Associate Dean and Chair of Biology Department during 2013-2017. Dr. Pan joined Georgia State University in 2000, was promoted to full professor in 2004, named a Distinguished University Professor in 2013 and designated a Regents' Professor (the highest recognition given to a faculty member by the University System of Georgia) in 2015. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. Dr. Pan has published more than 450 papers including over 250 journal papers with more than 100 papers published in IEEE/ACM Transactions/Journals. In addition, he has edited/authored 43 books. His work has been cited more than 20000 times based on Google Scholar and his current h-index is 90. Dr. Pan is currently serving as Editor-in-Chief of Big Data Mining and Analytics (a top 3% journal), Associate Editor-in-Chief of Journal of Computer Science and Technology (JCST), and Chinese Journal of Electronics (CJE). Dr. Pan has served as an editor-in-chief or editorial board member for 20 journals including 7 IEEE Transactions. For more information could be viewed at homepage: https://www.siat.ac.cn/college/jsjkz/szdw/202205/t20220519_6450923.html.

Ron Sun is a cognitive scientist investigating the fundamental nature of the human mind, using various methodologies of cognitive science, and in particular computational modeling, as means of forging mechanistic, process-based theories of the mind (especially comprehensive computational theories such as cognitive architectures). He has played a leading role early on in developing hybrid neural-symbolic (neurosymbolic) systems for cognitive modeling, and he is currently known for his work on the Clarion cognitive architecture. He has published more than 150 technical papers in journals such as Psychological Review, Cognitive Science, Artificial Intelligence, and Neural Networks, as well as 12 books by MIT Press, Cambridge University Press, Oxford University Press, and so on. His recent books include: Anatomy of the Mind (Oxford University Press), Grounding Social Sciences in Cognitive Sciences (MIT Press), and Cambridge Handbook of Computational Cognitive Sciences (Cambridge University Press). For more information could be viewed at homepage: https://sites.google.com/site/drronsun/home
Abstract: Neurosymbolic models have had a history and date back to the 1990s when the first batch of such models emerged, given the shortcomings of purely neural or purely symbolic models (see, e.g., Sun & Bookman,1994). Since then, there have been many different ways of structuring such models, but the question remains: how should we best structure them? I argue that they should be best structured in a cognitively motivated/justified way. In this talk, I will discuss neurosymbolic models, dual-process theories, and computational cognitive architectures and their relevance to each other. I will provide some historical backgrounds and argue that dual-process psychological theories have significant implications for developing neurosymbolic models. Computational cognitive architectures can help disentangle complex issues concerning dual-process theories and thus help develop neurosymbolic models.

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 project leader in the Multi-scale Medical Robotics Center (MRC), InnoHK, at the Hong Kong Science Park. 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 (H-index: 82), including Science Robotics (3), Nature Machine Intelligence (3), Nature Materials, Nature Reviews Bioengineering, Science Advances (10), Nature Communications (5), as the corresponding author. 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. He is a visiting professor of Lee Kong Chian School of Medicine at NTU Singapore, and Senior Editor of IEEE T-ASE and IEEE T-RO. For more information could be viewed at homepage: https://www4.mae.cuhk.edu.hk/peoples/zhang-li/.
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.

Dr. Yinqiang Zheng received his Doctoral degree of engineering from the Department of Mechanical and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan, in 2013. He is currently a full professor in the Next Generation Artificial Intelligence Research Center, The University of Tokyo, Japan, leading the Optical Sensing and Camera System Laboratory (OSCARS Lab.). He has published a serial of research papers that bridge optical imaging and machine learning. In collaboration with Canon and Hitachi, he has contributed substantially to the development and commercialization of multiband photo-acoustic imaging system and microscopic fluorescent imaging system. He has served as area chair for CVPR, ICCV, MM, 3DV, ACCV, ISAIR, DICTA and MVA. For more information could be viewed at homepage: https://www.u-tokyo.ac.jp/focus/en/people/k0001_03600.html.
Abstract: Polarization and color, which represent two distinct aspects of light fluctuations, have recently garnered significant attention. This is partly due to the development of single-chip color polarization sensors, which have made acquiring polarization-color data easier than ever before, leading to a surge in vision applications such as 3D reconstruction, reflection removal, surface defect detection, color constancy, and transparent object detection and segmentation. This talk will discuss our recent research on signal enhancement of polarization-color images, robustified inverse rendering with polarization information, and the vulnerabilities of polarization-based vision algorithms, three fundamental topics that have not received sufficient attention in the computer vision community. Regarding image enhancement, we will demonstrate how to model the noise distribution of a polarization-color sensor and how to improve low-quality polarization-color images through supervised deep learning. As for inverse rendering, we will show how to use polarized projection to improve shape reconstruction and reflectance estimation. In terms of vulnerabilities, we will present a novel physical adversarial attack on 3D reconstruction and glass detection by utilizing locally controllable polarizing projection.