My long-term goal is to build robots that continually use the knowledge gained during their lifespan to improve their performance and learn new tasks with human-like efficiency. As such, I work at the intersection of machine learning, robotics, and computer vision to develop intelligent robot systems with theoretically-founded lifelong learning capabilities. I am interested in a broad range of topics including Bayesian deep learning, conformal prediction, out-of-distribution detection and generalization, and continual learning. Recently, I have also been working on using Optimal Transport theory to solve machine learning problems.
Nov 23, 2021
I have recieved the 2021 G. N. Patterson Student Award, awarded to the most outstanding Ph.D. candidate to graduate from UTIAS during the year of 2021.
Mar 2, 2021
1 paper accepted to CVPR 2021 (Oral Presentation).
Jan 13, 2021
1 paper accepted to ICLR 2021.