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 list can be found in 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.
1.
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
2.
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
3.
Conghao Wang, Hiok Hian Ong, Shunsuke 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
4.
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
5.
S. P. Singh, S.
Gupta, and J. C. Rajapakse, “Sparse deep neural network for encoding and
decoding the structural connectome,” IEEE Journal of Translational
Engineering in Health and Medicine, 2024, IF: 3.40, DOI: 10.1109/JTEHM.2024.3366504
6.
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
7.
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
8.
W. K. Soh and J.
C. Rajapakse, “Hybrid U-Net transformer for ischemic stroke segmentation
with MRI and CT datasets,” Frontiers in Neuroscience, Vol 17, 2023, IF =
5.152, DOI: https://doi.org/10.3389/fnins.2023.1298514
9.
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
10. X. Zhong, X. Yu, E. Cambria, and J. C. Rajapakse,
“Marshall-Olkin power-law distributions in length-frequency of entities,” Knowledge-based
Systems, Vol. 279, 4 November 2023, p. 110942, IF = 8.139, DOI: https://doi.org.remotexs.ntu.edu.sg/10.1016/j.knosys.2023.110942
11. 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
12. 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
13.
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
14. 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
15.
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
16. 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
1. ``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
2. “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
Anyone interested in graduate student opportunities or research positions can email his/her CV/resume and interests to asjagath@ntu.edu.sg.
Last updated on 07/06/2024