Professor Jagath Rajapakse

 

Description: Macintosh HD:Users:jagath:Google Drive:My Info:photos:IMG_0180a.jpeg

 

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]ntu.edu.sg

Tel: +65 67905802

 

 

Jagath Rajapakse is Professor of Computer Engineering at the Nanyang Technological University, Singapore. He obtained his Ph.D. degree in electrical and computer engineering from the University of Buffalo, USA. He had been a Visiting Professor to Massachusetts Institute of Technology (MIT), USA, and Visiting Scientist to Max-Planck Institute of Cognitive and Brain Sciences, Germany, and to National Institute of Health, USA.

 

Professor Rajapakse’s  research works are in the areas of brain imaging, computational systems biology, and machine learning. He has published over 300 peer-reviewed research articles in high-impact journals and conferences. His research publications have received over 11,000 citations. He served as Associate Editor for IEEE Transactions on Medical Imaging, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Computational Biology and Bioinformatics. He is a Fellow of IEEE.

 

Professor Rajapakse’s current research interests are in deep learning. His on-going investigations include deep learning on small datasets and to learn biological networks, represent hierarchical processing of brain activation, and detect sentiments from speech signals.

 

TEACHING

CZ4042: Neural networks and deep learning

CE7412: Computational and systems biology

 

 

ONGOING RESEARCH PROJECTS

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

 

 

RECENT PUBLICATIONS

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, (in press).

2.    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

3.    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

4.    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

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

6.    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

7.    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

8.    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

9.    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

10. 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

11. 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

12. 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

13. 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

14. 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, 

15. 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

 

RESEARCH GRANTS

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

 

 

RESEARCH TEAM

Dr. Rama Kaalia, Research Fellow

Dr. Chamara Kasun, Research Fellow

Dr. Parvin Kumar, Wallenberg Research Fellow

Mr. Sukrit Gupta, Ph.D. student

Mr. Yi Hao Chan, Ph.D. student

Mr. Zhong Xioshi, Research Associate and Ph.D. student

Mr. Anh Chung Soo, Research Engineer and Ph.D. student

Mr. Haveesh Goli, Master’s student

 

Available research positions

 

PHD STUDENT AND VISITING RESEARCH POSITIONS

For PhD student and Visiting Scientist positions, kindly email your interests and CV to Prof Jagath Rajapakse (asjagath@ntu.edu.sg).

 

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Last updated on 15/02/2020