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).
Updates
- (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]
- Improving Limited Angle CT Reconstruction with a Robust GAN Prior
- (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.
