Scholar
Ajit Rajwade
Google Scholar ID: CkTW7PwAAAAJ
Professor, Department of CSE, IIT Bombay
Citations & Impact
Citations
713
H-index
11
i10-index
14
Publications
20
Co-authors
20
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Promoted to Professor, Department of CSE, IIT Bombay, February 2025
- Awarded the Prof. S. P. Sukhatme Award for Excellence in Teaching at IIT Bombay (institute-wide, based on cumulative teaching scores over 10 years), August 2024
- Multiple papers accepted to top-tier venues, including:
- — October 2025: Paper accepted in Inverse Problems (IOP)
- — March 2025: Paper on unlabeled sensing accepted to Transactions on Machine Learning Research (TMLR)
- — December 2024: Two papers accepted to ICASSP 2025
- — December 2024: Paper on theoretical analysis of 2D unknown-view tomography accepted to Signal Processing (Elsevier)
- — July 2024: Paper accepted to ECCV 2024
- — August 2024: Preprint on robust non-adaptive group testing under errors in group membership specifications
- — February 2025: Preprint on fast LASSO debiasing and debiased confidence intervals
- Taught a short summer course on group testing (June/July 2025)
- Delivered invited tutorial on Graph Signal Processing and Graph Learning at JTG/IEEE, ITSoc Summer School, IIT Bombay (July 2025)
Background
- Professor in the Department of Computer Science and Engineering, IIT Bombay
- Affiliated with the Centre for Machine Intelligence and Data Science (CMInDS) and the Koita Centre for Digital Health at IIT Bombay
- Research interests broadly span AI/ML applications to inverse problems, including:
- — Theoretical analysis of neural networks for inverse problems such as image reconstruction
- — Image reconstruction algorithms: tomography, cryo-electron microscopy, MRI, diffusion MRI
- — Compressed sensing: theory, algorithms, and applications
- — Image/video compression and restoration (denoising, deblurring, removing camera shake, occlusions, blocking artifacts, reflections, etc.)
- — Group testing: theory, algorithms, and applications in data science
- — Graph learning and graph signal processing
- — Disease modeling and model fitting in epidemiology
- Interested in various areas of applied mathematics
