Jean-Sébastien Giroux

MASc Student · Polytechnique & Mila, Montréal, Québec

I am a graduate student working at the intersection of artificial intelligence and neuroscience, a field known as Neuro-AI. My work spans two complementary areas: artificial intelligence, where I improve machine learning algorithms with deep learning, and neuroscience, where I apply these advances in neurostimulation to help people regain lost functionalities, for example being able to walk or see again. My research is done under the supervision of Professor Marco Bonizzato @ Polytechnique Montréal and Mila - Québec AI institute within sciNeurotech Lab.

Before joining this lab, I was an applied research scientist @ Environment and Climate Change Canada leading two projects: meteorological downscaling over subcontinental grid, and deep learning wind power forecasting for Quebec wind farms.

I am very interested in bringing my research to operational use, don't hesitate to contact me for research collaborations, AI contracting, or internship opportunities.

Affiliations

News

  • May 01, 2026 Joined Prof. Marco Bonizzato at SciNeurotech Lab for my MASc, working on LLM-guided priors for Bayesian optimization. Très fier de rester au Québec et de poursuivre mes études graduées au Mila. 🚀
  • Nov 14, 2025 Excited to share that my first journal paper has been published in Artificial Intelligence for the Earth Systems! 🎉

Research

Interests

Machine Learning Deep Learning Computer Vision Time Series Neuroscience
Meteorological-AI Ongoing

Deep Learning Wind Power Forecasting with Multimodal Sequence Models

Jean-Sébastien Giroux*, Thomas Forest*, Mathieu Prévost et al. · Work in Progress

Accurate short-term wind power forecasting is essential for grid stability and renewable energy integration. This project provides a comprehensive benchmark of deep learning architectures - Dense networks, Recurrent neural networks (LSTMs, GRUs), Transformers, and the recent Mamba state-space model - for next-day wind power forecasting at 1-hour temporal resolution over a wind farm in Québec.

We combine two complementary data sources: SCADA data (real-time sensor readings from the wind park) and NWP data (numerical weather prediction forecasts). Fusing both sources consistently outperforms models trained on either alone, establishing a strong benchmark for operational wind power forecasting.

📄 Paper (coming soon) 💻 Code (coming soon)
Meteorological-AI Published · Artificial Intelligence for the Earth Systems (AIES) 2025

Interpolation-Free Deep Learning for Meteorological Downscaling on Unaligned Grids across Multiple Domains with Application to Wind Power

Jean-Sébastien Giroux, Simon-Philippe Breton, and Julie Carreau. · AIES, 2025

As wind energy production accelerates, reliable wind forecasts become critical - but numerical weather prediction models are too computationally expensive to capture fine-scale wind behaviors. We develop a deep learning downscaling model based on a U-Net architecture that learns to map coarse probabilistic wind forecasts to high-resolution outputs, eliminating the need for conventional preprocessing steps. The model is extended to the full Canadian region via transfer learning, and results show that low-resolution wind speed alone is sufficient as input. Downscaled wind velocities show promising improvements in detecting wind power ramps - a key phenomenon for grid stability.

📄 Paper 💻 Code

Beyond the Lab

What drives me in life is curiosity, and the pursuit of excellence (Per aspera ad astra). Outside the lab, I love to explore new activities and experiences. One of my biggest passions is traveling to new places and immersing myself in local cultures.

Photos coming soon.