Dr Chai Quek is currently in the School of Computer Engineering since 1990. He received his Bachelor degree in Science and Ph.D. degrees from the Heriot-Watt University (Edinburgh). His research interests include Neurocognitive informatic, Biomedical Engineering and Computational Finance. He has done significant research work his research areas and published over 200 top quality international conference and journal papers. He has been often invited as a programme committee member or referee and reviewer for a number of premier conferences and journals, including IEEE TNN, TEvC etc. Dr. Quek is a senipr member of IEEE. He is also a member of the IEEE Technical Committee on Computational Finance and Economics. He has constantly and sucessfully groomed several high calibre research manpower who are awarded prestigious Singapore Millenium Foundation Scholar and Fellowships, Lee Kuan Yew Fellowship and A*Star Scholarhips.
His main interests includes the research and study of brain-inspired functional and computational models of memory structures that underlie the human reasoning process. In addition, advanced brain-inspired reasoning and cognition frameworks such as focus of attention, affect modeling, skill learning from novice to expert are actively investigated using the neurocognitive informatic approach. Fuzzy, Fuzzy neural, Neural, GA, GA-Neural, Rough set that maps formal fuzzy logical structures onto neural systems to perform fuzzy set derivation and Rule identification/reduction are investigated. The development of architectures that supports on-line and off-line learning fuzzy intelligent rule based systems is examined. The research issues cover fuzzy clustering, learning, modeling, fuzzy rule model, etc. These basic techniques are used to craft the brain-inspired memory learning systems. These memory learning structures are the building blocks of the functional neuro-cognitive brain and they can be broadly classified into huppocampal global semantic memories and neocortical like semantic association memories. In addition, research into CMAC and in-house developed MCMACs and Fuzzy MCMACs are actively pursued. They form a class of cerebellar like association memories that have excellent memory resolution and recall. The application areas are extensive as they rely heavily on the basic research into brain-inspired learning memory structures and brain-inspired cognitive architectures for emotion, cognition and perception modeling. The exciting application areas include computational finance ? arbitration, portfolio, trend analysis, bond and commodity trading; biomedical engineering ? diabetes modeling and control, ICU ventilator control; Affect modeling for edutainment, forensic tools, marketing research tools; Medical decision support tools ? thermograph analysis, pediatric leukemia and ovarian cancer analysis; Intelligent transportation analysis tools ? trend analysis and incident monitoring; as well as student affect modeling in Intelligent Tutoring System.
- Data Analytics and Complex Systems (IAF-ICP)
- Data Analytics and Complex Systems (RR)
- Integrated Multisensory System for Autonomous Maritime Vessels
- Javan Tan and C.Quek. (2010). A BCM-theory of meta-plasticity for online self-reorganizing fuzzy-associative learning. IEEE Transactions on Neural Networks, .
- S. D. Teddy, C. Quek, E. M-K. Lai and A. Cinar. (2010). PSECMAC Intelligent Insulin Schedule for Diabetic Blood Glucose Management Under Non Meal Announcement. IEEE Transactions on Neural Networks, 21(3), 361-380.
- W.L.Tung and C.Quek. (2010). eFSM – A Novel Online Neural-Fuzzy Semantic Memory Model. IEEE Transactions on Neural Networks, 21(1), 136-157.
- G.M.Goh, C.Quek & D.L.Maskell. (2010). EpiList II: Closing the Loop in the Development of Generic Cognitive Skills. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, .
- C. Ting and C.Quek. (2009). A Novel Blood Glucose Regulation using TSK0-FCMAC: A Fuzzy CMAC Based on the Zero-ordered TSK Fuzzy Inference Scheme. IEEE Transactions on Neural Networks, 20(5), 856-871.