Portrait of Chris Pal

Chris Pal

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Deep Learning

Biography

Christopher Pal is a Canada CIFAR AI Chair, full professor at Polytechnique Montréal and adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also a Distinguished Scientist at ServiceNow Research.

Pal has been involved in AI and machine learning research for over twenty-five years and has published extensively on large-scale language modelling methods and generative modelling techniques. He has a PhD in computer science from the University of Waterloo.

Current Students

Research Intern - McGill University
Postdoctorate - HEC Montréal
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Collaborating researcher - McGill University
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Master's Research - Université de Montréal
PhD - Polytechnique Montréal
PhD - McGill University
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
PhD - Polytechnique Montréal
Master's Research - Université de Montréal
Co-supervisor :
Collaborating Alumni - Polytechnique Montréal
PhD - Polytechnique Montréal
Postdoctorate - McGill University
Co-supervisor :
Master's Research - Polytechnique Montréal
PhD - Université de Montréal
Co-supervisor :
Master's Research - Concordia University
Co-supervisor :
Collaborating researcher - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - École de technologie suprérieure
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - HEC Montréal
Principal supervisor :
PhD - Polytechnique Montréal
Principal supervisor :
PhD - McGill University
Principal supervisor :
PhD - Polytechnique Montréal

Publications

Rendering-Aware Reinforcement Learning for Vector Graphics Generation
Juan A. Rodriguez
Haotian Zhang
Abhay Puri
Aarash Feizi
Rishav Pramanik
Pascal Wichmann
Arnab Mondal
Mohammad Reza Samsami
Rabiul Awal
Perouz Taslakian
Spandana Gella
Sai Rajeswar
David Vazquez
Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (see more)uage models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.
Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion
Ge Ya Luo
Zhi Hao Luo
Anthony Gosselin
Alexia Jolicoeur-Martineau
Distilling semantically aware orders for autoregressive image generation
Rishav Pramanik
Antoine Poupon
Juan A. Rodriguez
Masih Aminbeidokhti
David Vazquez
Zhaozheng Yin
Distilling semantically aware orders for autoregressive image generation
Rishav Pramanik
Antoine Poupon
Juan A. Rodriguez
Masih Aminbeidokhti
David Vazquez
Zhaozheng Yin
StarVector: Generating Scalable Vector Graphics Code from Images and Text
Juan A. Rodriguez
Abhay Puri
Shubham Agarwal
Issam Hadj Laradji
Pau Rodriguez
Sai Rajeswar
David Vazquez
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
Amirhossein Abaskohi
Amrutha Varshini Ramesh
Shailesh Nanisetty
Chirag Goel
David Vazquez
Spandana Gella
Giuseppe Carenini
Issam Hadj Laradji
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
Amirhossein Abaskohi
Amrutha Varshini Ramesh
Shailesh Nanisetty
Chirag Goel
David Vazquez
Spandana Gella
Giuseppe Carenini
Issam Hadj Laradji
LitLLMs, LLMs for Literature Review: Are we there yet?
Shubham Agarwal
Gaurav Sahu
Abhay Puri
Issam Hadj Laradji
Krishnamurthy Dj Dvijotham
Jason Stanley
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Felix Heide
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Felix Heide
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Bechard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
David Vazquez
Spandana Gella
Sai Rajeswar
Perouz Taslakian
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Bechard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
David Vazquez
Spandana Gella
Sai Rajeswar
Perouz Taslakian