Portrait of Maxime Gasse

Maxime Gasse

Associate Industry Member
Adjunct Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Senior Research Scientist, ServiceNow

Biography

I am a senior research scientist at ServiceNow in Montréal, where I do research at the intersection of causal inference and reinforcement learning. I am an adjunct professor at Polytechnique Montréal (courtesy appointment) and an associate industry member of Mila – Quebec Artificial Intelligence Institute.

I am fascinated by the question of AI: can we build machines that think? I humbly believe that our attempts at designing thinking machines can be a path towards a fundamental understanding of intelligence and of ourselves. Currently, I am interested in questioning if and how ideas from the field of causality can help in the design of autonomous learning agents. I am looking for motivated interns with strong technical skills and a background in reinforcement learning and/or causality.

Current Students

PhD - Polytechnique Montréal

Publications

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
David Vazquez
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (see more)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
Brice Rauby
Paul Xing
Jonathan Por'ee
Jean Provost
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Sai Rajeswar
Hector Palacios
David Vazquez
Alexandre Lacoste
Using Confounded Data in Latent Model-Based Reinforcement Learning
Damien GRASSET
Guillaume Gaudron
Pierre-Yves Oudeyer
Lookback for Learning to Branch
Prateek Gupta
Elias Boutros Khalil
Didier Chételat
M. Pawan Kumar
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Muller
Gonzalo Munoz
Ambros Gleixner
Felipe Serrano
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Muller
Gonzalo Munoz
Ambros Gleixner
Felipe Serrano