Academic Profile : Faculty

Prof Jagath Chandana Rajapakse.JPG picture
Prof Jagath Chandana Rajapakse
Professor, School of Computer Science and Engineering
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Jagath Rajapakse is Professor of Computer Science and Engineering at the 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. He was Visiting Professor to the Department of Biological Engineering at Massachusetts Institute of Technology (MIT).

He serves as Editor for Engineering Applications in Artificial Intelligence journal (IF = 7.802) 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 appointed IEEE Fellow in 2012 in recognition of his contributions to brain image analysis.
Professor Rajapakse’s research works are in the areas of data science, machine learning, brain imaging, and computational and systems biology. He has published over 300 peer-reviewed research articles in high-impact journals and conferences. His research articles have been cited over 14,000 times on Google Scholar. He was recently ranked among the top 2% scientists globally by Stanford Study. His current research focus on developing techniques and tools for diagnosis and treatment of brain diseases. He develops tools to detect and segment brain structures, lesions, and tumours from CT and MRI scans with deep learning technologies. He investigates the connectome from functional MRI and DTI scans for disease identification and biomarker discovery. He is also looking into how neuroimaging data can be integrated with multi-omics (genomics, proteomics, transcriptomics, and epigenomics) data for investigating neurological and psychiatric diseases.
 
  • Decoding the connectome by deep encoding on graphs
  • Deep generative approach for de novo cancer drug discovery with desired mechanism of action
  • Developing Unsupervised Machine Learning Techniques for Discovering Novel Ocular and Brain Imaging Biomarkers of Alzheimer’s Disease
  • Novel methods of network modelling and personalised machine learning to understand and predict individual dementia using heterogeneous multi-omics data
  • Novel MRI Toolbox to Classify and Predict Asian Mild Cognitive Impairment
  • Software Application for Thermal imaging analytics
Courses Taught
CE4042 Neural Networks and Deep Learning
CE7412 Computational & Systems Biology