Hi! I am a Computer Scientist with the Machine Intelligence Group at Lawrence Livermore National Laboratory (LLNL), a Federally Funded Research Institute. My research interests broadly include computer vision, machine learning, and high dimensional data analysis.
I’m currently work on : (a) Making ML models more resilient under controllable and uncontrollable distribution shifts (b) Solving ill-posed inverse problems with novel priors (c) Applications of ML in high energy physics, healthcare, and x-ray imaging etc. I also serve on the program committees for several ML and vision venues (NeurIPS, CVPR, ICLR, ICCV, ICML, AAAI).
At LLNL, I lead the Open Data Initiative with the goal of releasing LLNL’s high impact scientific datasets for machine learning — so far, we have 10 datasets and counting!
My resume (pdf, updated Oct 2021). Contact:
Updates
- (Oct 2021) Paper describing a new uncertainty quantification method (Δ-UQ) is up on arXiv
- (Oct 2021) 🔥 📝 Albert’s paper on using INRs for Dynamic CT has been accepted to ICCV 2021! [paper] [github]
- (July 2021) Serving as Area Chair for WACV 2022.
- (July 2021) 🔥 Qunwei & Bhavya’s paper that proposes a manifold regularized GAN is accepted for publication at SIAM Mathematics of Data Science (SIMODS)
