Rajat K. De
Also known as: Rajat De
Indian Statistical Institute, University of Calcutta, Jadavpur University
0000-0001-6080-1131
About
Rajat K. De is a Professor of the Indian Statistical Institute, Kolkata, India. He completed his Bachelor of Technology in Computer Science and Engineering, and Master of Computer Science and Engineering in the years 1991 and 1993, from Calcutta University and Jadavpur University, India, respectively. He obtained his Ph.D. degree from the Indian Statistical Institute, India, in 2000. Dr. De was a Distinguished Postdoctoral Fellow at the Whitaker Biomedical Engineering Institute, the Johns Hopkins University, USA, during 2002-2003. He was a Fulbright-Nehru Academic and Professional Excellence Fellow in 2017 and 2018 to work at the Department of Medicine, University of California, San Diego. He has more than 100 research articles published in international journals, conference proceedings and in edited books to his credit. His research interest includes machine learning, deep learning, computational biology, computational systems biology, and big data analytics.
Employment
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Indian Statistical Institute Professor
Education
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University of Calcutta Bachelor of Technology
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Indian Statistical Institute Ph. D.
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Jadavpur University Master of Computer Science and Engineering
Projects & Funding
Projects & funding information is unavailable.
Publications (30)
- ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation Save
- TiMePReSt: Time and memory efficient pipeline parallel DNN training with removed staleness Save
- Multi-Omics Integration for Identification of Prognostic Molecular Signatures for Survival Stratification in Lung Cancer Save
- G-NeuroDAVIS: A generative model for data visualization through a generalized embedding Save
- MultitaskBP: Reliable PPG-Based Blood Pressure Measurement Under Motion Artifact Corruption Save
- The Forward-Cooperation-Backward (FCB) learning in a multi-encoding uni-decoding neural network architecture Save
- Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference Save
- NeuroDAVIS-FS: Feature Selection Through Visualization Using NeuroDAVIS Save
- A novel method addressing NGS-based mappability bias for sensitive detection of DNA alterations Save
- NeuroDAVIS: A neural network model for data visualization Save