Louis Fortier-dubois
Co-Founder & Computer Scientist at Université Laval
Lévis, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolLouis Fortier-dubois is a co-founder and computer scientist with seven years of experience building AI-driven products and high-performance software from startup R&D to production. Trained at Université Laval with a master's in cryptography and now pursuing a doctorate in machine learning, he combines deep theoretical knowledge with practical engineering—evident in his work optimizing linear algebra and initialization routines for the Rust-based deep learning framework Burn. He co-founded Empego to bring AI decision support into community pharmacies and now leads technical efforts at Tracel Technologies, bridging research and scalable back-end systems. His background includes full‑stack web development, teaching core CS courses, and organizing competitive programming events, showing both leadership and hands-on coding. A recipient of multiple academic awards, he brings a rare blend of cryptography, ML, and HPC expertise paired with startup grit. Outside work he balances an active research agenda with family life as a proud husband and father.
7 years of coding experience
1 year of employment as a software developer
Doctorat, Artificial Intelligence, Non-complété, Doctorat, Artificial Intelligence, Non-complété at Université Laval
Github Skills (7)
matrix-multiplication10
rust10
deep-learning10
linear-algebra10
compile9
jit9
autodiff9
Programming languages (2)
Github contributions (5)
tracel-ai/burn
Jun 2023 - Jun 2026
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:346 reviews, 275 PRs, 443 pushes in 3 years 1 month
Contributions summary:Louis primarily contributed to implementing new initializer features and improvements to existing modules within the deep learning framework, Burn. Their work involved adding Xavier Glorot initialization methods and Kaiming initialization methods, as well as refactoring the initializer to be more flexible. They also made changes to matrix multiplication, including optimizations for performance with tiled and vectorization, specifically for CPU and GPU operations. Additional changes to support the Candle backend for more linear algebra operations were also made.
deep-learningtensorautodiffmachine-learningrust
tracel-ai/cubek
Dec 2025 - Jul 2026
CubeK: high-performance multi-platform kernels in CubeCL
Contributions:5 reviews, 27 PRs, 237 pushes in 7 months
cudagpuhpcrocmvulkan
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