Suyash P. Awate

Suyash P. Awate serves as the Asha and Keshav Bhide Chair Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Bombay, Mumbai, India. His research spans medical image computing, machine learning, image analysis, computer vision, and statistical modeling and inference. His work focuses on developing novel computational methods for medical image analysis, reconstruction, and quality enhancement.

Prof. Awate's research interests center on medical image computing, where he develops advanced machine learning techniques for medical image reconstruction, segmentation, and quality enhancement. His work addresses critical challenges in medical imaging such as low-dose PET/CT imaging, uncertainty quantification in segmentation, robustness to out-of-distribution data, and accelerated MRI/fMRI acquisition. He has pioneered methods combining variational inference, expectation maximization, and deep learning to improve medical image quality while reducing radiation exposure and scan times. His research has significant clinical applications in neuroimaging, tumor analysis, and brain function mapping.

His recent publications demonstrate a strong trend toward integrating deep learning with classical statistical methods, particularly focusing on uncertainty-aware models for medical image analysis. His work consistently addresses the challenge of robustness in medical imaging, developing methods that maintain performance even with degraded input data or distribution shifts. A significant portion of his research targets clinical applications, particularly in neuroimaging and oncology, with emphasis on practical implementation for real-world medical settings.

Prof. Awate actively mentors PhD, MTech, and BTech students at IIT Bombay. His current PhD students include Vatsala Sharma (awarded Microsoft Research India PhD Award), Jimut Bahan Pal (CMInDS Fellow, Prime Minister's Research Fellow), Tejomay Padole (TCS Research Fellow), Subhankar Nag, and Koustav Pal. He also guides MTech/MS students Soumya Mukherjee, Jay Gorakhiya, and Russel Abreo, along with BTech student Varshith Anumalasetty. He teaches several courses including CS 736: Medical Image Computing (inaugurated in 2014), CS 663: Fundamentals of Digital Image Processing, CS 215: Data Analysis and Interpretation, CS 740: Mathematics for Visual Computing, and CS 101: Computer Programming and Utilization.

His laboratory focuses on developing computational methods for medical image analysis, with particular emphasis on uncertainty quantification, robust deep learning for medical imaging, and accelerated acquisition techniques. The research group maintains strong collaborations with medical institutions to ensure clinical relevance of their computational methods.