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 fortunate to be able to collaborate with scientists and researchers across several areas of science and engineering — for example high energy physics; epidemiology for COVID19; x-ray imaging; human connectome project; healthcare.
I spend a lot of my time thinking about modeling and understanding high dimensional, multi-modal, and inherently structured data. I serve as a reviewer for several top machine learning and computer vision publications (NeurIPS, CVPR, ICLR, ICCV, ICML, AAAI. etc).
I also lead the Open Data Initiative with the goal of releasing LLNL’s high impact scientific datasets for machine learning — so far, we have 9 datasets and counting!
My resume (pdf, updated Jul 2021). Contact:
Updates
- (July 2021) 🔥 📝 Albert’s paper on using INRs for Dynamic CT has been accepted to ICCV 2021! [arXiv preprint] [Code will be available soon]
- (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)
- (May 2021) “MARGIN” is (finally) published! Our paper on using graph signal processing as a general tool for interpreting DNNs is out in the Frontiers in Big Data journal.
- (Jan 2021) Co-organizing the 6th edition of DiffCVML as a workshop at CVPR 2021.
- (Jan 2021) ✨ Our paper on “Generative Patch Priors” won Best Paper Honorable Mention at WACV 2021! Thank you to the organizers and awards committee for recognizing our work! We have released the code on github. [LLNL’s press release] [coverage on Photonics.com]
