Professor Jagath Rajapakse

Jagath's phoeo

Jagath Rajapakse is Professor of Data Science at the College of Computing and Data Science at Nanyang Technological University (NTU), Singapore. He has BSc degree in Electronics and Telecommunication Engineering from University of Moratuwa (UM), Sri Lanka, and MS and PhD degrees in Electrical and Computer Engineering from University at Buffalo (UB), USA. He was Visiting Scientist to the Max-Planck Institute of Cognitive and Brain Sciences, Germany, and the National Institute of Mental Health, USA before joining NTU. He was Visiting Professor to the Department of Biological Engineering at Massachusetts Institute of Technology (MIT).

Professor Rajapakse’s research works are in the areas of explainable AI, generative AI, brain imaging, and computational and systems biology. He has published over 300 peer-reviewed research articles in high-impact journals and conferences, which are listed here. His current research works focus on developing computational techniques and tools for diagnosis and treatment of brain diseases by combining neuroimaging and multi-omics data; and for generating small molecule and peptide-based drugs for cancer. He is also looking into how imaging data can be integrated with multi-omics (genomics, proteomics, transcriptomics, and epigenomics) data for investigating molecular underpinnings of various diseases.

He serves as Editor for Engineering Applications in Artificial Intelligence journal (IF = 8.0) and served as Associate Editor for IEEE Transactions on medical imaging, IEEE Transaction on neural networks and learning systems, and IEEE Transactions on computational biology and bioinformatics. He was a Fulbright Scholar and elevated to IEEE Fellow in 2012 in recognition of his contributions to brain image analysis.

TEACHING

RECENT PUBLICATIONS

1.    A. Niwarthana and J. C. Rajapakse, “LABMA: Latent bottleneck attention-based architecture for integration of transcriptomics, proteomics, and MRI in neurodegeneration,” Computers in Biology and Medicine, Volume 210, 1 July 2026. IF = 6.3, DOI: https://doi.org/10.1016/j.compbiomed.2026.111740

2.    J. Xia, Y. H. Chan, D. Girish, C. Wang, and J. C. Rajapakse, “Learning brain structure-function interactions via modality-guided attention-based graph neural networks,” Information Fusion, 135, 104426, April 2026, IF = 15.5, DOI: https://doi.org/10.1016/j.inffus.2026.104426

3.    T. N. B. Sayuti, K. Mittal, T. L. Heng, J. C. Rajapakse, “Target-aware latent diffusion model for design of apoptosis-inducing anticancer peptides,” Computers in Biology and Medicine, Volume 199, December 2025, 111286, IF = 6.3

https://doi.org/10.1016/j.compbiomed.2025.111286

4.    D. Girish, Y. H. Chan, S. Gupta, J. Xia, and J. C. Rajapakse, “Encoding and decoding of brain dynamic connectivity for ADHD diagnosis,” IEEE Journal of Biomedical and Health Informatics, IF = 6.8, DOI: https://ieeexplore.ieee.org/abstract/document/11399873

5.    J. Xia, Y. H. Chan, D. Girish, Q. H. Chew, K. Sim, and J. C. Rajapakse, “Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning,” Communications Biology, 8: 1215, 13 Aug 2025, IF = 5.2 https://www.nature.com/articles/s42003-025-08637-0

6.    P. Lyu, X. Yu, J. Chi, H. Wu, C. Wu, and J. C. Rajapakse, “TwinsTNet: Broad-view twins transformer network for bimodal salient object detection,” IEEE Transactions on Image Processing, Vol 34, pp. 2796-2810, May 2025, IF = 10.8, DOI: 10.1109/TIP.2025.3564821

7.    J. Xia, Y. H. Chan, D. Girish, and J. C. Rajapakse, “Interpretable modality-specific and interactive graph convolutional neural networks on brain functional and structural connectome,” Medical Image Analysis, Vol. 102, 25 February 2025, 103509, IF= 10.7. DOI: https://doi.org/10.1016/j.media.2025.103509

8.    C. Wang, G. A. Kumar, and J. C. Rajapakse, “Drug discovery and mechanism prediction with explainable graph neural networks,” Scientific Reports, Vol. 15, Issue 1, pp. 179, January 2025, IF = 3.8, DOI: https://www.nature.com/articles/s41598-024-83090-3

9.    C. Wang, H. H. Ong, S. Chiba, Jagath C Rajapakse, “GLDM: Hit molecule generation with constrained graph latent diffusion model,” Briefings in Bioinformatics, 2024, Volume 25, Issue 3, May 2024, bbae142, https://doi.org/10.1093/bib/bbae142

10. J. C. Rajapakse, C. H. How, Y. H. Chan, L. C. P. Hao, A. Padhi, V. Adrakatti, I. Khan, and T. Lim, “Two-stage approach to intracranial hemorrhage segmentation from head CT images,” IEEE Access, 2024, IF: 3.9, DOI: https://doi.org/10.1109/ACCESS.2024.3393231

11. S. Goyal and J. C. Rajapakse, “Self-supervised learning for hotspot detection and isolation from thermal images,” Expert Systems with Applications, Vol 237, Part B, 1 March 2024, p. 12156, IF = 8.665, DOI: https://doi.org.remotexs.ntu.edu.sg/10.1016/j.eswa.2023.121566

12. Y. H. Chan, W. C. Yew, Q. H. Chew, K. Sim, and J.C. Rajapakse, “Elucidating salient site-specific functional features and site-invariant biomarkers in schizophrenia via deep neural networks,” Scientific Reports, 13, Article number: 21047, 2023, IF= 4.6, DOI: https://www.nature.com/articles/s41598-023-48548-w

13. W. K. Soh, H. Y. Yuen, and J.C. Rajapakse, “HUT: Hybrid U-Net transformer for brain lesion and tumor segmentation,” Heylion, 9 (2023), e22412, IF = 4.0, DOI: https://doi.org/10.1016/j.heliyon.2023.e22412

14. Y. Zhang, X. He, Y. H. Chan, Q. Teng, and J. C. Rajapakse, “Multi-modal graph neural network for early diagnosis of Alzheimer’s disease from sMRI and PET scans,” Computers in Biology and Medicine, 2023, Vol. 164, p. 107328, IF = 7.7, DOI: 10.1016/j.compbiomed.2023.107328

15. S. Xiao, H. Lin, C. Wang, S. Wang, and J. C. Rajapakse, “Graph neural networks with multiple prior knowledge for multiomics data analysis,” IEEE Journal of Biomedical and Health Informatics, 2023, Vol 27(9), pp. 4591 – 4600, IF = 7.021, DOI: 10.1109/JBHI.2023.3284794

16.   C. Wang, L. W. Lue, R. Kaalia, P. Kumar, and J.C. Rajapakse, “Network-based integration of multi-omics data for clinical outcome prediction of neuroblastoma,” Scientific Reports, 2022, 12:15425, IF = 4.996, DOI: https://www.nature.com/articles/s41598-022-19019-5

17.   C. Wang, X. Lye, R. Kaalia, P. Kumar, and J. C. Rajapakse, “Deep learning and multi-omics approach to predict drug responses in cancer,” BMC Bioinformatics, 2021:22:632, IF = 3.169, https://doi.org/10.1186/s12859-022-04964-9, Sept 2022

18.   Y. H. Chan, C. Wang, W. K. Soh, and J. C. Rajapakse, “Combining neuroimaging and omics datasets for disease classification using graph neural networks,” Frontiers of Neuroscience, IF = 4.677, 23 May 2022, DOI: https://doi.org/10.3389/fnins.2022.866666 

19.   S. Gupta, M. Lim, and J. C. Rajapakse, “Decoding task-specific and task-general architectures of the brain,” Human Brain Mapping, 43: 2801 - 2816, IF = 4.554, February 2022, DOI: https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.25817

FUNDED PROJECTS

1.    ``De novo anti-cancer therapeutic agent design in equivariant 3D space with conditional diffusion models,” AcRF Tier-2 Grant, Ministry of Education (MOE), Singapore, 01/08/2025 – 31/07/2028, $816,192.00

2.    ``Elucidating brain structure-function interaction implication in brain diseases via explainable AI,” AcRF Tier-1 Grant, Ministry of Education (MOE), Singapore, RG15/25, 01/11/2024 – 30/10/2026, $185,000.00

3.    ``Deep generative approach for de novo cancer drug discovery with desired mechanism of action,” AcRF Tier-1 Grant, Ministry of Education (MOE), Singapore, 01/11/2023 – 31/10/2024, $100,000.00

4.    “Software application for thermal image analytics,” $130,000 grant from Wonder Engineering Technologies, Singapore, 04/04/2022 – 03/04/2024

2.    “Decoding the connectome by deep encoding on graphs,” AcRF Tier-2 Grant, Ministry of Education, Singapore, 02/02/2022 – 01/02/2025, $730,249.00

3.    “Developing unsupervised machine learning techniques for discovering novel ocular and brain imaging biomarkers of Alzheimer’s disease,” $169,000.00 grant from MSD International GMBH, 01/10/2021 – 31/09/2023

4.    “Machine learning for demand forecasting,” $263,000.00 grant from Becton Dickinson Holding Pte. Ltd., 30/04/2021 – 29/04/2023

5.    “Multilayer networks for identification of biomarkers and prediction of clinical variables from multi-omics data,” AcRF Tier-1 Grant, Ministry of Education, Singapore, 01/05/2020 – 31/07/2021, $99,998.75

6.    “Detection of customer emotions and behaviour from speech,” Singtel-NTU Cognitive and Artificial Intelligence Joint Lab grant (IAF-ICP-RES 1-1.1), AStar and Singtel joint grant, 01/06/2018 – 30/05/2020, $390,593.00

7.    “Study of Alzheimer’s disease heterogeneity and progression using latent grey-matter atrophy and white-matter impairment factors,” AcRF Tier-1 RG149/17 Grant, Ministry of Education, Singapore, 01/03/2018 – 28/02/2020, $99,912.48

8.    “Predicting missing and spurious links and labels of protein-interaction networks,” AcRF Tier-2 Grant MOE2016-T2-1-029, Ministy of Education, Singapore, 09/01/2017 – 08/07/2020, $ 579,536.00

RESEARCH TEAM

GRADUATE AND RESEARCH POSITIONS AND PROJECTS

Anyone interested in graduate student opportunities or research positions can email his/her CV/resume and interests to asjagath@ntu.edu.sg.

CONTACT DETAILS

Jagath C. Rajapakse, PhD, FIEEE
Professor of Data Science
College of Computing and Data Science
Nanyang Technological University
S3-B1c92, 50 Nanyang Avenue
Singapore 639798
Email: asJagath@ntu.edu.sg, Jagath.Rajapakse@gmail.com
Tel: 67905802, Mobile: 93623027

Last updated on 18/05/2026