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 list can be found in Google Scholar. 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.    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

2.    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

3.    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

4.    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

5.    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

6.    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

7.    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

8.    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

9.    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

10. 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

11.   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

12.   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

13. P. Hiort, J. Hugo, J. Zeinert, N. Muller, S. Kashyap, J. C. Rajapakse, F. Azhuaje, B. Y. Renard, and K. Baum, “DrDimont: explainable drug response prediction from differential analysis of multi-omics networks,” Bioinformatics, Vol. 38, Supplement 2, September 2022, Pages ii113-ii119, https://doi.org/10.1093/bioinformatics/btac477, IF = 6.931

14.   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 

15.   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.    ``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

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 Computer Engineering
School of Computer Science and Engineering
Nanyang Technological University
N4-2a11, 50 Nanyang Avenue
Singapore 639798
Email: asJagath@ntu.edu.sg, Jagath.Rajapakse@gmail.com
Tel: 67905802, Mobile: 93623027

Last updated on 07/06/2024