Fellow of IEEE
Nanyang Technological University, Singapore
Xudong Jiang received the B.Eng. and M.Eng. from the University of Electronic Science and Technology of China (UESTC), and the Ph.D. degree from Helmut Schmidt University, Hamburg, Germany. From 1986 to 1993, he was a Lecturer with UESTC, where he received two Science and Technology Awards from the Ministry for Electronic Industry of China. From 1998 to 2004, he was with the Institute for Infocomm Research, A-Star, Singapore, as a Lead Scientist and the Head of the Biometrics Laboratory, where he developed a system that achieved the most efficiency and the second most accuracy at the International Fingerprint Verification Competition in 2000. He joined Nanyang Technological University (NTU), Singapore, as a Faculty Member, in 2004, and served as the Director of the Centre for Information Security from 2005 to 2011. Currently, he is a professor in NTU. Dr Jiang holds 7 patents and has authored over 200 papers with 2 papers in Nature Communications, 20 papers in Pattern Recognition and over 40+ papers in the IEEE journals, including 6 papers in IEEE Transactions on Pattern Analysis and Machine Intelligence and 14 papers in IEEE Transactions on Image Processing. Four of his papers have been listed as the top 1% highly cited papers in the academic field of Engineering by Essential Science Indicators. He served as IFS TC Member of the IEEE Signal Processing Society from 2015 to 2017, Associate Editor for IEEE Signal Processing Letter from 2014 to 2018, Associate Editor for IEEE Transactions on Image Processing from 2016 to 2020 and the founding editorial board member for IET Biometrics form 2012 to 2019. Dr Jiang is currently an IEEE Fellow and serves as Senior Area Editor for IEEE Transactions on Image Processing and Editor-in-Chief for IET Biometrics. His current research interests include image processing, pattern recognition, computer vision, machine learning, and biometrics.
Fellow of IEEE
University of Macau, Macau SAR, China
Dr. Cheng-Zhong Xu, IEEE Fellow, is the Dean of Faculty of Science and Technology and the Interim Director of Institute of Collaborative Innovation, University of Macau, and a Chair Professor of Computer and Information Science. He was a professor of Wayne State University and the Director of Institute of Advanced Computing of Shenzhen Institutes of Advanced Technologies, Chinese Academy of Sciences before he joined UM in 2019. Dr. Xu is a Chief Scientist of Key Project on Smart City of MOST, China and the principal investigator of the Key Project on Autonomous Driving of FDCT, Macau SAR.
Dr. Xu’s main research interests lie in parallel and distributed computing and cloud computing, in particular, with an emphasis on resource management for system’s performance, reliability, availability, power efficiency, and security, and in big data and data-driven intelligence applications in smart city and self-driving vehicles. The systems of particular interest include distributed systems and the Internet, servers and cloud datacenters, scalable parallel computers, and wireless embedded devices and mobile edge systems. He published two research monographs and more than 400 peer-reviewed papers in journals and conference proceedings; his papers received over 14K citations with an H-index of 64. He was a Best Paper Nominee or Awardee of the 2021 ACM Symposium on Cloud Computing (SoCC’2021), 2013 IEEE High Performance Computer Architecture (HPCA), the 2013 ACM High Performance Distributed Computing (HPDC), IEEE Cluster’2016, ICPP’2005, GPC’2018, UIC’2018, AIMS’2019, IEEE Edge’2020. He also received more than 100 patents or PCT patents and spun off a business “Shenzhen Baidou Applied Technology” with dedication to location-based services and technologies. Dr. Xu received the most prestigious “President’s Awards for Excellence in Teaching” of Wayne State University in 2002.
He serves or served on a number of journal editorial boards, including IEEE Transactions on Computers (TC), IEEE Transactions on Cloud Computing (TCC), IEEE Transactions on Parallel and Distributed Systems (TPDS), Journal of Parallel and Distributed Computing (JPDC), Science China: Information Science and ZTE Communication. Dr. Xu has been the Chair of IEEE Technical Committee on Distributed Processing (TCDP) from 2015 to 2020. He obtained BSc and MSc degrees from Nanjing University in 1986 and 1989 respectively, and a PhD degree from the University of Hong Kong in 1993, all in Computer Science and Engineering.
More speakers to be announced.
Fellow of IEEE
Auckland University of Technology, New Zealand
Professor Nikola Kasabov is Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He is George Moore Chair Professor of data analytics at the University of Ulster UK and Professor at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology, New Zealand. Kasabov is the Immediate Past President of the Asia Pacific Neural Network Society (APNNS) and Past President of the International Neural Network Society (INNS). He is member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, Springer Series of Bio-and Neuro-systems and Springer journal Evolving Systems. He is Associate Editor of several journals, including Neural Networks, IEEE TrNN, Tr CDS, Information Sciences, Applied Soft Computing. Kasabov holds MSc and PhD from TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 650 publications. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Advisory Professor at Shanghai Jiao Tong University and CASIA Beijing, Visiting Professor at ETH/University of Zurich, Honorary Professor of Teesside University, UK;. Prof. Kasabov has received a number of awards, among them: Honorary Professor at the University of Auckland, NZ; Doctor Honoris Causa from Obuda University, Budapest; INNS Ada Lovelace Meritorious Service Award; NN Best Paper Award for 2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT Medal; Honorable Member of the Bulgarian, the Greek and the Scottish Societies for Computer Science. He has supervised to completion more than 50 PhD students. More information of Prof. Kasabov can be found from: https://academics.aut.ac.nz/nkasabov.
IEEE Life Fellow
Brain-Mind Institute and GENISAMA, USA
Prof. Juyang Weng received the BS degree from Fudan University, in 1982, M. Sc. and PhD degrees from the University of Illinois at Urbana-Champaign, in 1985 and 1989, respectively, all in computer science. He is a former faculty member of Department of Computer Science and Engineering, faculty member of the Cognitive Science Program, and faculty member of the Neuroscience Program at Michigan State University, East Lansing. He was a visiting professor at the Computer Science School of Fudan University, Nov. 2003 - March 2014, and did sabbatical research at MIT, at Media Lab Fall 1999 – Spring 2000; and at Department of Brain and Cognitive Science Fall 2006-Spring 2007 and taught BCS9.915/EECS6.887 Computational Cognitive and Neural Development during Spring 2007. Since the work of Cresceptron (ICCV 1993) the first “deep learning” neural networks for 3D world without post-selection misconduct, he expanded his research interests in biologically inspired systems to developmental learning, including perception, cognition, behaviors, motivation, machine thinking, and conscious learning models. He has published over 300 research articles on related subjects, including task muddiness, intelligence metrics, brain-mind architectures, emergent Turing machines, autonomous programing for general purposes (APFGP), Post-Selection flaws in “deep learning”, vision, audition, touch, attention, detection, recognition, autonomous navigation, and natural language understanding. He published with T. S. Huang and N. Ahuja a research monograph titled Motion and Structure from Image Sequences. He authored a book titled Natural and Artificial Intelligence: Computational Introduction to Computational Brain-Mind. Dr. Weng is an Editor-in-Chief of the International Journal of Humanoid Robotics, the Editor-in-Chief of the Brain-Mind Magazine, and an associate editor of the IEEE Transactions on Autonomous Mental Development (now Cognitive and Developmental Systems). With others’ support, he initiated the series of International Conference on Development and Learning (ICDL), the IEEE Transactions on Autonomous Mental Development, the Brain-Mind Institute, and the startup GENISAMA LLC. He was an associate editor of the IEEE Transactions on Pattern Recognition and Machine Intelligence and the IEEE Transactions on Image Processing.
Fellow of IEEE
Southern University of Science and Technology, China
Prof. Yuhui Shi is an expert in the field of computational intelligence and the developer of the brain storm optimization (BSO) algorithm. He is also a fellow of IEEE for his contributions to particle swarm optimization algorithms.
Prof. Yuhui Shi received his Ph.D. from Southeast University in 1992. After that, he did research in the United States, South Korea, Australia, and other places. He has published many ground-breaking papers with Russell Eberhart and James Kenney, the developer of the particle swarm optimization algorithm, and co-authored the books on Swarm Intelligence and Computational Intelligence: Concepts to Implementations.
Brain Storm Optimization Algorithms; Particle Swarm Optimization Algorithms; Swarm Intelligence; Evolutionary Computation, Machine Learning; Computational Intelligence; Artificial Intelligence; Data Science; Intelligent System