I am a researcher at Polytechnique Montréal, Canada, an associate academic member of the Mila research institute, and a member of the Data Science for Real-Time Decision Making Canada Excellence Research Chair (CERC). I obtained my PhD in 2017 on the topic of structure learning of probabilistic graphical models (PGMs), and I have since been working on a variety of interdisciplinary topics, such as machine learning applied to medical imaging, or machine learning applied to combinatorial optimization.
The fundamental question that drives my research is, can machines think ? Today I am mostly interested in questioning if and how causality can help in the design of autonomous learning agents.
My broad scientific interests include:
- probabilistic graphical models and their theoretical properties (see my PhD Thesis)
- machine learning for combinatorial optimization (see our Ecole library)
- causality, in particular in the context of reinforcement learning (see our recent paper and my talk on causal RL)
I am proud to serve the scientific community, and I have been rewarded NeurIPS 2019 best reviewer (top 40%), ICML 2020 best reviewer (top 30%), NeurIPS 2020 top reviewer (top 10%), ICLR 2021 outstanding reviewer (top 10%), and ICML 2021 expert reviewer.