Ph.D. Yale University; 2000
|(65)65166210||Click here to Email|
Financial Engineer, Fannie Mae, Washington D.C., U.S.A., 2000-2001. Research Assistant, Center for Systems Science, Yale University, 1995-2000
Serve as the chairman of the IEEE Singapore Control Systems Chapter since 2010. Serve as the Vice chairman of the IEEE Singapore Control Systems Chapter, 2008-2009. Serve as the chairman for the 1st, 2nd and 3rd IEEE Singapore Control Systems Chapter Graduate Student Workshop in Control and Automation, in 2005, 2006 and 2007. Serve as the Finance Chair of the inaugural 2007 IEEE Multi-conference on Systems and Control (IEEE MSC2007). Serve as the Committee member of the IEEE Singapore Control Systems Chapter, 2006-2007. Serve as the Secretary of the IEEE Singapore Control Systems Chapter 2003-2005. Serve in the organizing committees for ICCA05, ICCA07, ICCA09, ICCA10 and ICCA11. Serve in the organizing committees for CIRAS 2003 and CIRAS 2005. Serve in the international program committees for ISNN2006, ISNN2007, ISNN2008, LSMS2007, and CIS-RAM 2008.
The mind, not space, is science's final frontier, which stimulates both scientists and engineers to discover how the human brain works and how to make machines really intelligent just like human beings that can learn from experience and improve their performances. The long term goal of my research programme is to investigate the fundamental issues related to the three most important aspects of the theme of intelligent systems and machines: pattern recognition, control and learning. Rather than limiting my interest to only one aspect, I am keeping my mind open to all possible interesting fundamental issues relevant to these three essential features. Although significant progress has been made in understanding the various fundamental issues in building intelligent systems and machines, there are plenty of open questions for us to investigate in the future. For instance, we have just shifted the paradigm for pattern recognition from the statistical approach to biological approach since the statistical approach has proven to be almost hopeless for pattern recognition problems where the training examples are scarce or the within-class variance is large while even the toddlers or the monkeys can learn to recognize different objects from very few examples with large variances. We are currently taking on the challenging problem of how to incorporate the various fundamental features of the human vision system together into one vision machine synergistically. From the very beginning, the primary motivation for utilizing multiple models, switching and tuning for adaptive control is to deal with time-varying environment. However, due to mathematical tractability, stability analysis has been confined to time-invariant systems with unknown parameters while very few analytical results have been reported on how to identify and control the system in rapidly changing environment except some heuristic ideas. The first question we would like to address along this line is how to locate the models in a rapidly time-varying environment. Most recently, we have discovered a general framework for identification of discrete-time time-varying systems. Extensive simulation studies have shown that our algorithm can indeed provide accurate estimates of the plant parameters even in noisy cases. Once the identification problem is solved satisfactorily, we will be ready to attack the challenging problem of adaptive control of time-varying systems using multiple models, which will surely make major impact once accomplished. We have just embarked on the adventure in exploring the feasibility of applying feedback control to regulate gene activities. The central tool for the gene engineering community is deleting or inserting the genes in existing networks. In contrast to this main stream, we are taking a different approach by investigating the feasibility and efficiency in utilizing feedback mechanisms to turn genes on and off without changing their presence. The motivation of this approach is based upon the biological fact that the highly specialization of different cells can be traced to the different activation levels of the various genes inside the cells despite the fact that all the cells share the same genes within the same living beings. Currently, we are trying hard to conduct the experimental work to validate this approach.
Adaptive Control using Multiple Models (PI) NUS ARF, S$37,290, 01/04/2002 to 31/03/2006 ---- --- completed Pattern Recognition: A Biological Approach (PI) NUS ARF, S$146,250, 01/02/2006 to 28/02/2009---- --- completed Experimentation, Modeling and Control of Calcium Dynamics in Human Vascular Endothelial Cells (PI) NUS ARF, S$154,800, 01/02/2008 to 31/03/2011 ---- --- completed Coordination and Control of Multi-Robot Systems: Hybrid System Approaches (PI) Temasek Labs, S$200,000, 01/08/2009 to 31/07/2013 ongoing STARFISH (Small Teams of Autonomous Robtic Fish) Robust Positioning and Localization (Co- PI) NUS ARF, S$122,000, 01/04/2006 to 30/09/2008 ---- --- completed Social Robots: Breathing Life into Machines (Collaborator) MDA, S$1,597,800, 2007 to 2011 ---- --- completed
1. W. Gu, C. Xiang, Y. V. Venkatesh, D. Huang and H. Lin, ˇ°Facial Expression Recognition using Radial Encoded Local Gabor Features and Classifier Synthesis,ˇ± Pattern Recognition, vol. 45, no. 1, pp. 80-91, 2012. 2. C.Y. Lai, C. Xiang and T.H. Lee, "Data-based Identification and Control of Nonlinear Systems via Piecewise Affine Approximation,ˇ± IEEE Trans. on Neural Networks, vol. 22, no. 12, pp.2189¨C2200, 2011. 3. K.R. Qin and C. Xiang, "Hysteresis Modeling for Calcium mediated Ciliary Beat Frequency in Airway Epithelial Cells," Mathematical Biosciences, vol. 229, no. 1 , pp. 101-108, 2011. 4. Z. Huang, C. Xiang, H. Lin and TH Lee, "Necessary and Sufficient Conditions for Regional Stabilizability of Generic Switched Linear Systems with a Pair of Planar Subsystems," Int. J. Control, vol. 83, no. 4 pp. 694-715, 2010. 5. KR Qin, C Xiang, Z. Xu, LL Cao, S S Ge and ZL Jiang, "Dynamic Modeling for Shear Stress Induced ATP Release from Vascular Endothelial Cells," Biomechanics and Modeling in Mechanobiology, vol. 7, no.5, pp. 345-353, 2008. 6. E. J. Teoh, KC Tan and C Xiang, "Estimating the number of hidden neurons in a feedforward network using the Singular Value Decomposition," IEEE Transactions on Neural Networks, vol. 17, no. 6, pp.1623-1629, 2006 7. C. Xiang and D. Huang, "Feature Extraction Using Recursive Cluster-Based Linear Discriminant with Application to Face Recognition," IEEE Trans. on Image Processing,vol. 15, no.12, pp.3824-3832, 2006. 8. C. Xiang, X.A. Fan and T.H. Lee,"Face Recognition Using Recursive Fisher Linear Discriminant," IEEE Trans. on Image Processing, vol. 15, no. 8, pp. 2097-2105, 2006. 9. C. Xiang, S. Q. Ding and T.H. Lee, "Geometrical Interpretation and Architecture Selection of MLP," IEEE Transactions on Neural Networks, vol. 16, no. 1, pp. 84-96, 2005. 10. KS Narendra and C Xiang, "Adaptive Control of Discrete-time Systems Using Multiple Models," IEEE Transactions on Automatic Control, AC-45, no. 9, pp. 1669-1686, 2000.
Yale University Fellowship, 1994-1995. Granted for outstanding academic achievement and performance. Invited Speaker at the Fourteenth Yale Workshop on Adaptive and Learning Systems, Yale University, June 2-4, 2008. The Yale Workshop on Adaptive and Learning Systems is a prestigious workshop, whose speakers are by invitation only from the organizer. Placed on Honors List for teaching, Faculty of Engineering, NUS, 2009. Placed on Commendation List for teaching, Faculty of Engineering, NUS, 2011.
EE3304 Digital Control Systems, S2 EE5103/ME5403 Computer Control Systems, S1 EE5904/ME5404 Neural Networks, S2