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:
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]
- Improving Limited Angle CT Reconstruction with a Robust GAN Prior
