Assistant Professor
CHEONG Siew Ann 

Division of Physics and Applied Physics,
School of Physical and Mathematical Sciences
Nanyang Technological University

Office: 6513 8084    
E-mail: cheongsa[at]ntu[dot]edu[dot]sg


Biographical Profile
Dr CHEONG Siew Ann obtained his B.Sc. in Physics with First Class Honours from the National University of Singapore in 1997. He then went on to pursue his graduate studies in Cornell University, USA, specializing in Theoretical Condensed Matter Physics. After receiving his PhD in 2006, Dr CHEONG did a short postdoctoral stint in Bioinformatics with the Cornell Theory Center, working on biological sequence analysis. He joined the Division of Physics and Applied Physics, in the School of Physical and Mathematical Sciences, Nanyang Technological University as an Assistant Professor in August 2007.
Research Interests
Dr CHEONG’s current research interests are in complex systems and computational physics. In the former, he aims to understand the underlying organizing principles behind different complex systems (the brain, the economy, the society, the stock market, …) from the large complex data sets that are produced by these systems, and also by building exploratory models that would shed light on how these systems became complex, and why do they become complex. In the latter, he and his students simulate complex systems, by developing novel algorithms to overcome the problems associated with hierarchies of length and time scales in complex systems.
Research Interest in the Neuroscience of Learning and Education
Dr CHEONG has started applying his data analytic methods to fMRI data, to see if brain responses can be understood in terms of complex but causal activation sequences. Recently, he and his student have successfully applied such complex network-based analysis to functional data on teaching practices in Singapore, and made interesting discoveries on how English and Mathematics are taught in Singapore, at the Primary 5 and Secondary 3 levels. He is also working with another student to develop a complex neural network model, to explore questions on brain development, learning, evolution, as well as modulation.