Professor Jagath Rajapakse


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Jagath C Rajapakse, PhD, FIEEE

Professor of Computer Engineering

School of Computer Science and Engineering

Nanyang Technological University

N4-2a11, 50 Nanyang Avenue

Singapore 639798

Email: asjagath[at]

Tel: +65 67905802



*      Research Positions

Jagath Rajapakse is Professor of Computer Engineering at the Nanyang Technological University (NTU), Singapore. He has a bachelor’s degree in Electronics and Telecommunication Engineering and master’s and Ph.D. degrees in Electrical and Computer Engineering. He had been Visiting Professor to the Department of Biological Engineering at the Massachusetts Institute of Technology (MIT), USA, and Visiting Scientist to the Max-Planck Institute of Cognitive and Brain Sciences, Germany, and the National Institute of Mental Health, USA.


Professor Rajapakse’s  research works are in the areas of brain imaging, computational and systems biology, and machine and deep learning. He has published over 300 peer-reviewed research articles in high-impact journals and conferences. His research articles have been cited over 13,000 times. He serves as an Editor for Engineering Applications in Artificial Intelligence journal (Elsevier). He was an Associate Editor for IEEE Transactions on Medical Imaging, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Computational Biology and Bioinformatics.


Professor Rajapakse’s research are in the areas of deep learning, brain imaging, and computational and systems biology. He now develops innovative deep learning techniques for  analysis of small datasets , multi-omics data, and brain connectome though imaging, and detecting one’s emotions from speech.


He was recently ranked among the top 2% scientists globally by a Stanford Study. He is a Fellow of IEEE (2012 - ) and was a Fulbright Scholar (1987 – 1989).



CZ4042: Neural networks and deep learning

CE7412: Computational and systems biology




1.    Investigation functional and structural networks of the brain through whole imaging scans (MRI, fMRI, and DTI) and their manifestations in brain disease

2.    Study of cancer and mental diseases from molecular sub-networks inferred from multi-omics data

3.    Detecting anomalies and emotions from video and audio by using deep learning techniques




1.    S. Gupta, Y. H. Chen, and J. C. Rajapakse, “Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot,” Neurocomputing, January, 2021, DOI: 10.1016/j.neucom.2020.04.152

2.    X. Zhong and J. C. Rajapakse, “Graph embeddings on gene ontology annotations for protein-protein interaction prediction,” BMC Bioinformatics, 21 (Supplement 16), 516, December 2020, DOI: 10.1186/s12859-020-03816-8

3.    S. Gupta and J. C. Rajapakse, “Iterative consensus spectral clustering improves detection of subject and group level brain functional modules,” Scientific Reports, 10: 7590, May 2020, DOI:10.1038/s41598-020-63552-0, IF = 4.011

4.    S. Gupta, J. C. Rajapakse, and R. E. Welsch, “Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer’s disease and autism spectrum disorder,” NeuroImage: Clinical, Volume 25, 102186, Jan 2020, DOI: 10.1016/j.nicl.2020.102186, IF = 3.943

5.    L.-C. Tranchevent, F. Azuaje, and J. C. Rajapakse, “A deep neural network approach to predicting clinical outcomes of neuroblastoma patients,” BMC Medical Genomics, 12 (Suppl 8), 178, Dec 2019, DOI: 10.1186/s12920-019-0628-y, IF = 3.317

6.    R. Kaalia and J.C. Rajapakse, “Refining modules to determine functionally significant clusters in molecular networks,” BMC Genomics, 20 (Supplement 9): 901, Dec 2019, DOI: 10.1186/s12864-019-6294-9, IF = 3.730

7.    X. Zhong, R. Kaalia, and J. C. Rajapakse, “GO2Vec: transforming GO terms and proteins to vector representations using graph embeddings” BMC Genomics, 20 (Suppl 9): 918, Dec 2019, DOI: 10.1186/s12864-019-6272-2, IF = 3.730

8.    S. Gupta, Y. H. Chen, and J. C. Rajapakse “Decoding brain functional connectivity implicated in AD and MCI,” MICCAI 2019, LNCS 11766, pp. 781–789, 2019, DOI: 10.1007/978-3-030-32248-9_87

9.    K. Baum, J. C. Rajapakse, and F. Azuaje, “Analysis of correlation-based molecular networks from different omics data by fitting stochastic block models,” F1000Research, 8: 465, Aug 2019, DOI: 10.12688/f1000research.18705.2

10. W. Liu and J. C. Rajapakse, “Fusing gene expressions and transitive protein interactions for inference of gene regulatory networks,” BMC Systems Biology, 13(Suppl 2): 37, April 2019, DOI: 10.1186/s12918-019-0695-x, IF = 2.05

11. A. N. Barrett, C. Y. Fong, A. Subramanian, W. Liu, Y. Feng, M. Choolani, A. Biswas, J. C. Rajapakse, and A. Bongso, “Human Wharton’s jelly mesenchymal stem cells show unique expressions compared with bone marrow mesenchymal stem cells using single-cell RNA sequencing,” Stem Cells and Development, 28(3), Feb 2019, DOI: 10.1089/scd.2018.0132, IF = 3.315

12. R. Kaalia and J. C. Rajapakse, “Functional homogeneity and specificity of topological modules in human proteome,” BMC Bioinformatics, 19: 553, Feb 2019, DOI: 10.1186/s12859-018-2549-8, IF = 2.511

13. X. Sui and J. C. Rajapakse, “Profiling heterogeneity of Alzheimer’s disease using white matter impairment factors,” Neuroimage: Clinical, 20, pp. 1222 – 1232, Oct 2018, DOI: 10.1016/j.nicl.2018.10.026, IF = 4.348

14. W. Liu, J. Liu, and J. C. Rajapakse, “Gene ontology enrichment improves performances of functional similarity of genes,” Scientific Reports, 8: 12100, Aug 2018, DOI: 10.1038/s41598-018-30455-0 ,IF = 4.122

15. D. N. Wadduwage, J. Kay, V. R. Singh, O. Kiraly, M. R. Sukup-Jackson, J. C. Rajapakse, B. P. Engelward, and P. T. C. So, “Automated fluorescence intensity and gradient analysis enables detection of rare fluorescent mutant cells deep within the tissue of RaDR mice,” Scientific Reports, 8:12108, Aug 2018, DOI: 10.1038/s41598-018-30557-9, IF = 4.122, 

16. L.-C. Tranchevent, P. V. Nazarov, T. Kaoma, G. P. Schmartz, A. Muller, S.-Y. Kim, J. C. Rajapakse, and F. Azuaje, “Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach,” Biology Direct, 13:12, June 2018, DOI:10.1186/s13062-018-0214-9, IF = 2.649



1.    “Multilayer networks for identification of biomarkers and prediction of clinical variables from multi-omics data,” AcRF Tier-1 Grant, Ministry of Education (MOE), Singapore, 01/05/2020 – 31/07/2021, $99,998

2.    Predicting customer emotions from speech: Singtel-NTU Cognitive and Artificial Intelligence Joint Lab grant, 01/06/2018 – 30/05/2020, $390,593

3.    Study of Alzheimer’s disease heterogeneity and progression using latent grey-matter atrophy and white-matter impairment factors: AcRF Tier-1 Grant, Ministry of Education (MOE), Singapore, 01/03/2018 – 29/02/2020, $99,912

4.    Predicting missing and spurious links and labels of protein-interaction networks: AcRF Tier-2 Grant 2016-T2-1-029, Ministry of Education (MOE), Singapore, 09/01/2017 – 08/07/2020, $ 579,536.00




Dr. Parvin Kumar, Wallenberg Postdoctoral Research Fellow

Mr. Anh Chung Soo, Research Associate

Mr. Yi Hao Chan, Ph.D. student

Mr. Soh Wei Kwek, Ph.D. student

Ms. Charlene Ong Zhi Lin, Ph.D. student

Mr. Chung Suhwan, Ph.D. student

Mr. Chaudhary Nitesh Kumar, M.Eng. student

Mr. Wang Conghao, M.Eng. student




For PhD applicants and Visiting Scientist positions, kindly email your interests and CV to Professor Jagath Rajapakse (



Last updated on 15/02/2021