• I presented our latest work on Stability analysis of learning-based reconstruction approaches at the 2021 Electronic Imaging meeting in January.  Talk Title: “Stability Analysis of Data and Image Domain Learning-based Reconstruction Approaches”.

Talk          Video

  • I have successfully defended my Ph.D. Dissertation titled “Data and Image Domain Deep Learning for Computational Imaging” in December 2020.

Presentation Slides          Ph.D. Thesis

  • Check out our latest work on #ComputationalImaging, introducing a new unified framework for Data and Image Priors Integration for Image Reconstruction (DIPIIRDeep-peer“) using Consensus Equilibrium. Impressive results on CT and MR. 


  • Our Deep-MAR paper has been amongst the 5 most popular articles since June. #IEEE #TCI #Xplore
    It applies Deep Learning in the sensor-data domain for metal artifact reduction (MAR). 
  • Our new work combining Data and Image domain learned Prior models in MBIR (DIPIIR framework) will be presented at the 2020 Electronic Imaging meeting in January.
  • Prof. Clem Karl presented our Data and Image domain Deep Learning work at the IMA special Workshop on Computational Imaging: “Data and Image Domain Deep Learning for Tomographic Computational Imaging”.
    Talk     Slides
  • Our new work on integrating Data and Image domain Deep Learning for Limited Angle CT Reconstruction using Consensus Equilibrium framework has been accepted for publication in proceedings of IEEE ICCV Workshop on Learning for Computational Imaging (Oral).
  • Our CT metal artifact reduction (MAR) approach using data domain Deep Learning has been accepted for publication in IEEE Transactions on Computational Imaging.
    IEEE Xplore     arXiv    Github    CodeOcean
  • Highlighted as Student Spotlight in January issue of ALERT newsletter.  Link