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. (more details)
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My resume (pdf, updated Jan 2019).


  • (Nov 2019) I attended my first Dagstuhl Seminar in Germany, on Interpretable Machine Learning. Dagstuhl seminars promote collaborative, intimate discussions on emerging CS topics, it was a great learning experience!
  • (Oct 2019) Short papers accepted to 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]
  • (Aug 2019) Work led by Sam Jacobs on extremely scalable deep generative models in scientific machine learning has been accepted to IEEE Cluster 2019.
  • (Aug 2019) Work led by Shusen Liu on techniques to visualize extremely large datasets has been accepted to IEEE VIS 2019. (arXiv preprint)

I actively review for Vision and ML conferences, notably — CVPR ’19, ICCV ’19, AAAI ’20, CVPR ’20, ICML ’20, ECCV ’20.