Christopher Pal
Distinguished Scientist
Low Data Learning
Leadership team
Chris Pal is an associate professor in the Department of computer and software engineering at the École Polytechnique of Montreal and a Distinguished Scientist at ServiceNow Research. Prior to arriving in Montreal, he was a professor in the Department of Computer Science at the University of Rochester. He has been a research scientist with the University of Massachusetts and has also been affiliated with the Interactive Visual Media Group and the Machine Learning and Applied Statistics groups at Microsoft Research. His research at Microsoft lead to three patents on image processing, computer vision and interactive multimedia. Chris earned his masters in Math and PhD from the University of Waterloo in Canada. His PhD research led to contributions applying probability models and optimization techniques to image, video and signal processing. Prior to his graduate studies Chris was with the multimedia research company Interval in Palo Alto, CA (Silicon Valley). As a result of his research at Interval he was awarded a patent on audio signal processing.
Interests
- Machine Learning
- Computer Vision
- Natural Language Processing
- Reinforcement Learning
- Deep Learning
Publications
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning.
Nan Rosemary Ke,
Aniket Didolkar,
Sarthak Mittal,
Anirudh Goyal,
Guillaume Lajoie,
Danilo Rezende,
Yoshua Bengio,
Christopher Pal,
Stefan Bauer ,
Michael C. Mozer. At
Conference on Neural Information Processing Systems (NeurIPS),
2021.
Predicting Infectiousness for Proactive Contact Tracing.
Yoshua Bengio,
Prateek Gupta,
Tegan Maharaj,
Nasim Rahaman,
Martin Weiss,
Tristan Deleu,
Eilif Benjamin Muller,
Meng Qu,
victor schmidt,
Pierre-luc St-charles,
hannah alsdurf,
Olexa Bilaniuk,
david buckeridge,
gaetan caron,
pierre luc carrier,
Joumana Ghosn,
satya ortiz gagne,
Christopher Pal,
Irina Rish,
Bernhard Schölkopf,
abhinav sharma,
Jian Tang,
andrew williams. At
International Conference on Learning Representations (ICLR),
2021.
SEVN: A Sidewalk Simulation Environment for Visual Navigation.
Martin Weiss,
Simon Chamorro,
Roger Girgis,
Margaux Luck,
Samira Ebrahimi Kahou,
Joseph P. Cohen,
Derek Nowrouzezahrai,
Doina Precup,
Florian Golemo,
Christopher Pal. At
Conference on Robot Learning (CoRL),
2019.
Deep Complex Networks.
Chiheb Trabelsi,
Olexa Bilaniuk,
Ying Zhang,
Dmitriy Serdyuk,
Sandeep Subramanian,
João Felipe Santos,
Soroush Mehri,
Negar Rostamzadeh,
Yoshua Bengio,
Christopher Pal. At
International Conference on Learning Representations (ICLR),
2018.