I am a Computer Scientist with the Data Analysis Group at the Lawrence Livermore National Laboratory, a Federally Funded Research Institute. My research interests broadly include computer vision, machine learning, and high dimensional data analysis. I enjoy collaborating with scientists and researchers across several disciplines of science, and engineering. I also actively review for Vision and ML conferences, notably — CVPR, ICCV, AAAI, ICML, ECCV.
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Updates

• (Feb 2020) MimicGAN has been accepted for publication in IJCV’s special issue on GANs! [arXiv]
• (Feb 2020) Chairing a special session on Generative modeling for images & videos at Asilomar 2020.
• (Jan 2020) Organizing a mini-symposium at SIAM Mathematics of Data Science on machine learning under different kinds of constraints in May 2020. We have four exciting talks planned!
• (Jan 2020) Rate Invariant Autoencoding of Time-Series led by Kaushik and Suhas will appear at ICASSP 2020. [preprint]
• (Dec 2019) 2 new preprints are available:
  • MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking [arXiv]
    Where we make PGD style optimization more robust for encoding images accurately under unknown distribution shifts.
  • Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies [arXiv]
    Where we use CycleGAN style training for surrogate modeling in ICF with many favourable properties.
• (Dec 2019) Short papers presented at NeurIPS 2019 workshops:
  • Improving Limited Angle CT Reconstruction with a Robust GAN Prior
    Deep-Inverse Workshop [paper]
  • Extreme Few-view CT Reconstruction using Deep Inference
    Deep-Inverse Workshop [paper]
  • Exploring Physical Generative Models with Scientific Priors
    ML for Physical Sciences Workshop [paper]
  • Designing Deep Inverse Models for History Matching in Reservoir Simulations
    ML for Physical Sciences Workshop [paper]
  • Modeling Human Brain Connectomes using Structured Neural Networks
    Graph Representation Learning Workshop [paper]