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personal
portfolio
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projects
Autonomous Ship Navigation in Ice-Covered Waters
Published:
Diffusion Model Predictive Control (DMPC) for safe and efficient navigation in stochastic ice environments using real-world satellite and ocean current data.
Testing Real-Time Reinforcement Learning in Duckiematrix
Published:
Exploring the challenges and opportunities of real-time RL in simulated autonomous driving environments, focusing on how computation delays affect agent performance.
publications
Molecular interactions of tannic acid with proteins associated with SARS-CoV-2 infectivity
Published in International Journal of Molecular Sciences, 2022
Co-authored after an undergraduate internship in numerical biophysics, supervised by Normand Mousseau.
Mapping a Black Hole Accretion Flow with JWST/NIRSpec
Published in STScI Committee, 2024
Accepted Cycle 3 James Webb Space Telescope Research Proposal
A novel approach to understanding the link between supermassive black holes and host galaxies
Published in Astrophysical Journal, 2024
First-authored paper published as part of my M.Sc.
Probabilistic Interpolation of Sagittarius A*’s Multi-Wavelength Light Curves Using Machine Learning
Published in IAUS 397, 2025
Conference paper for IAUS 397: UniversAI
Enabling Reparameterized Policy Gradients for LLM Post-training
Published in NeurIPS 2025 Workshop (FLLMPT), 2025
Accepted at the NeurIPS 2025 Workshop on Foundations and Limits of Learning from Model Preferences and Trajectories (FLLMPT).
FALCON: Secure Agentic Coding through Counterexample-Grounded Feedback
Published in ICLR 2026 (under review), 2025
Under review at ICLR 2026.
MCP-AT: Reinforcement Learning for Coding Agents under Tool Poisoning
Published in ACL ARR 2026 (under review), 2025
Under review at ACL Rolling Review (ACL ARR 2026).
Application of the LSTM model for streamflow forecasting in the Great-Lakes region
Published in American Geophysical Union, 2025
Conference paper for American Geophysical Union 2025
Probabilistic Interpolation of Sagittarius A*’s Multi-Wavelength Light Curves Using Diffusion Models
Published in Astrophysical Journal, 2026
First-authored paper published as part of my M.Sc.
talks
Applying a new Bayesian statistical model to better understand supermassive black holes
Published:
Talk during the 90th ACFAS Congress. Link to the video here.
AI for understanding the supermassive black hole at the center of the Milky Way
Published:
Talk during the 2024 Digital Futures Conference at IVADO. Link to the video here.
Interpolating Sagittarius A* multi-wavelength data using machine learning
Published:
Talk during the 2024 AstroAI Workshop at Harvard CfA. Link to the video here.
Probabilistic Interpolation of Sagittarius A*’s Multi-Wavelength Light Curves Using Machine Learning
Published:
Talk during the 2025 PhD application round at Anton Pannekoek Institute of University of Amsterdam, in Amsterdam, Netherlands.
Probabilistic Interpolation of Sagittarius A*’s Multi-Wavelength Light Curves Using Machine Learning
Published:
Talk during the 2025 IAUS 397: UniversAI conference in Athens, Greece.
teaching
Assistant Astronomer
Telescope Assistant, Mont-Mégantic Observatory, 2022
Teaching new summer interns the roles of being an assistant astronomer at Mont-Mégantic Observatory.
Summer School Teacher
Teacher, Brébeuf Summer School, 2022
Teaching a class on Bayesian Inference and MCMC at the Brébeuf Summer School.
Teaching Assistant
Undergraduate Course, University of Montreal, Department of Physics, 2024
Teaching assistant for PHY 2441 Advanced Electromagnetics.
Teaching Assistant
Graduate Course, Université de Montréal, DIRO (Mila), 2026
Teaching assistant for IFT 6162 — Building Up RL: From Dynamics and Control to Learning, a graduate course taught by Professor Pierre-Luc Bacon that constructs reinforcement learning from first principles in dynamics, optimization, and control theory. The course covers controlled systems and state-space modeling, discrete-time optimal control, trajectory optimization and model predictive control, dynamic programming, approximate value methods, policy optimization and gradient estimation, and deep RL algorithms (DQN, DDPG, TD3, policy gradients).