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 generalization, and continual learning.

Currently, I am an IVADO Postdoctoral Research Fellow at Mila-Quebec AI Institute. I am part of the Montreal Robotics and Embodied AI Lab (REAL) working with Professor Liam Paull.

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Recent News

Jul 19, 2021

Our survey paper on probablistic object detection has been accepted to appear in the IEEE Transactions on Intelligent Transportation Systems. Code for surveyed methods is available here.

Jun 30, 2021

I began my new position as an  IVADO Postdoctoral Research Fellow at Mila-Quebec AI Institute.

Jun 17, 2021

I have successfully defended my Ph.D. thesis, which was accepted as is (no corrections required). Link to dissertation manuscript.

Jun 7, 2021

I have been selected to the Robotics Science and Systems pioneers cohort of 2021, a group of 30 leading senior PhD students and postdocs in the field. 

May 20, 2021

I have been acknowledged as an Outstanding Reviewer by CVPR 2021 organizers.

Mar 2, 2021

1 paper accepted to CVPR 2021 (Oral Presentation).

Jan 13, 2021

1 paper accepted to ICLR 2021.

Feb 1, 2020

1 paper accepted to ICRA 2020.

Nov 9, 2019

Presented my talk on how to evaluate predictive uncertainty estimates in deep object detectors at IROS 2019.


Research Highlights

Contact Me


Phone: +1(519) 5040469