Hi! I am a machine learning scientist with the Machine Intelligence Group at Lawrence Livermore National Laboratory (LLNL).
My CV (pdf, updated July 2022).
(11/22) arXiv preprint DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
TLDR: A specially conditioned diffusion model does remarkably well, and produces useful uncertainties on the challenging, limited-angle CT reconstruction problem.
(10/30) arXiv preprint: On-the-fly Object Detection with CLIP guidance
TLDR: A completely automated system that leverages StyleGAN to identify “object-like” semantic neurons, and labels them using CLIP for an end-to-end object detector built “on-the-fly”.
(9/14) Single Model Uncertainty Estimation via Stochastic Data Centering
accepted as a spotlight at NeurIPS 2022! 🎷 [paper]
TLDR: Simple, easy-to-implement, accurate epistemic uncertainty estimates via a trivial data centering operation
(9/2) OOD Detection using Neural Network Anchoring,
accepted for publication to ACML 2022! 🏖️ [paper] [code]
TLDR: Novel hetero-scedastic (varying per sample) temperature scaling strategy that improves OOD detection
(8/16) Improving Diversity with Adversarially Learned Transformations for Domain Generalization,
accepted for publication to WACV 2023! 🏖️ [paper] [code].
TLDR: Improves domain generalization performance by expanding the space of learnable augmentations.
(7/11) arXiv preprint: Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection
TLDR: Studies the relative importance of inliers vs outlier exposure for effective OOD detection