|Prof Rajapakse Jagath Chandana|
Division of Software & Information Systems
School of Computer Engineering
College of Engineering
- PhD State University of New York at Buffalo 1993
- MSc State University of New York at Buffalo 1989
- BSc(Eng)(Hons) University of Moratuwa 1985
|Professor Rajapakse is with the School of Computer Engineering and the Director of BioInformatics Research Center. He is presently a Visiting Professor to the Department of Biological Engineering, Massachusettes Institute of Technology (MIT). He received his First Class (Hons) Bachelor degree in Electronic and Telecommunication Engineering from the University of Moratuwa (Sri Lanka). He began his post-graduate studies under the Fulbright Scholarship at University of Buffalo (USA) where he received Master and Ph.D. degrees in Electrical and Computer Engineering. Before joining NTU in 1998, he was a Visiting Fellow at the National Institute of Mental Health (USA) and a Visiting Scientist at the Max-Planck-Institute of Brain and Cognitive Sciences (Germany).
His research interests are in the areas of neuroinformatics and bioinformatics. He has made fundamental contributions in his area of research and published over 210 top quality international conference and journal papers, which are widely cited by the research community. He serves as Associate Editor for IEEE Transactions on Medical Imaging, IEEE Transactions on Computational Biology and Bioinformatics, and IEEE Engineering in Medicine and Biology Magazine, and in editorial boards of several other journals. He is presently the Chair of IAPR Technical Committee on Pattern Recognition for Bioinformatics.
|Professor Rajapakse's areas of expertise are machine learning, brain imaging, and computational and systems biology.
Professor Rajapakse has pioneered several techniques for analysis of anatomical and functional MR images. His team was the first to develop techniques to model brain connectivity in an exploratory manner, using functional MR images. Presently, his team is investigating brain connectivity patterns underlying higher-order brain functions such as language and memory, and brain disease such as Parkinson's disease. He is also working on potential applications of brain connectivity and constrained independent component analysis (cICA) in Brain Computater Interface applications, especially in identifying different mental states and extracting features robust to inter- and intra-subject variations.
Professor Rajapakse is presently working on identifying key targets in biological pathways. His research is centered on identifying co-regulated genes, building gene regulary networks, fusion of protein-interactions, and identifying key molecules and core networks in pathways. His team also develops techniques to segment cells and nuclei, identify protein subcellular localizations, and model spatiotemporal changes of cell morphologies from cellular images obtained from electron microscopy and high content screening.
|Research Grant |
- Academic Research Fund Tier 2 (2010-)
- MOE Grant (2010-)
|Current Projects |
- Advanced computational image analysis (CSB-IUP4: Phase 2)
- Compaq - NTU Joint R&D
- Modeling , Validation, and Analysis of Gene Regulatory Networks including Delays
- Modeling, Validation, and Analysis of Gene Regulatory Networks including Delays
- Statistical Analysis of Data from Eden Study (Smart Subaward Agreement No. 017-Biosym & ID)
- To develop and implement three computing clusters at the Bioinformatics Research Centre (BIRC)
- JC Rajapakse, Y Wang, X Zheng, and J. Zhou. (2008). Probabilistic framework for brain connectivity from functional MR images. IEEE Transactions on Medical Imaging, 27(6), 825-833.
- J. C. Rajapakse and J. Zhou. (2007). Learning effective brain connectivity with dynamic Bayesian networks. NeuroImage, 37(3), 749-760.
- MN Nguyen and JC Rajapakse. (2006). Two-stage support vector regression approach for predicting accessible surface areas of amino acids. Proteins: Structure, Function, and Bioinformatics, 63(3), 542-550.
- JC Rajapakse and LS Ho. (2005). Markov encoding for detecting signals in genomic sequences. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 2(2), 131-142.
- W Lu and JC Rajapakse. (2005). Approach and applications of constrained ICA. IEEE Transactions on Neural Networks, 16(1), 203-212.