I’m a research scientist at Deep Render, working in image and video compression. Previously I was a post-doc at McGill University, in machine learning. My PhD was in applied math, where I was supervised by Adam Oberman. My research interests lie at the intersection of deep learning, machine learning, and applied math.


Jul 6, 2020 Started as a research scientist at Deep Render
Jun 11, 2020 New preprint on learning Continuous Normalizing Flows as gradients of potential functions, using Optimal Transport & duality
Jun 1, 2020 "How to train your neural ODE: ..." was accepted to ICML 2020
Apr 23, 2020 Gave a talk at UCLA during the IPAM workshop on PDEs and Inverse Problems in Machine Learning
Jan 6, 2020 "A principled approach for generating adversarial images under non-smooth dissimilarity metrics" was accepted to AISTATS 2020