Rushil Anirudh

D93CCDC5-529F-4134-A5E8-909F0A40649D


Hi! 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 and Journals, notably — CVPR, NeurIPS, ICCV, ICML, AAAI, ICML, ECCV, IEEE TIP, Pattern Recognition, IEEE TMI.

My resume (pdf, updated Jan 2020). Contact:undefined

Updates

  • (March 2020) Our paper that proposes a new class of neural network surrogates for inertial confinement fusion (ICF) will appear in the Proc. of the National Academy of Sciences (PNAS)! [arXiv preprint] [github]
  • (Feb 2020) MimicGAN will appear in IJCV’s special issue on GANs! [published version] [arXiv]
  • (Feb 2020) Chairing a special session on Generative modeling for images & videos at Asilomar 2020.
  • Cancelled due to Covid-19 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) 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]