Marc-alexandre Côté
Principal Researcher at Microsoft
Montreal, Quebec, Canada
Summary
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Rockstar🎓
Top SchoolMarc-Alexandre Côté is a Principal Researcher at Microsoft with 14 years of experience blending deep learning research and production-grade software engineering. He holds a PhD in Computer Science from Université de Sherbrooke and has a track record of shipping core algorithmic contributions and performance-sensitive implementations in Python and Cython. His open-source work spans influential projects—from adding efficient array ops to Theano to optimizing medical-imaging libraries (dipy, nibabel) and improving RL research tooling (Microsoft’s TextWorld). Comfortable across backend, devops and test automation, he focuses on robustness, reproducibility and speed in scientific code. Based in Montreal, he brings academic rigor to practical engineering problems and often bridges research prototypes into maintainable infrastructure.
14 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Université de SherbrookeComputer Science, Computer Science at Cégep Saint-Jean-sur-Richelieu
French, English
Github Skills (26)
dependency-management10
unit-testing10
3d-graphics10
game-engine10
python10
numpy10
cython10
build-process10
vtk10
theano10
testing9
data-structure9
user-interface9
c119
devops9
Programming languages (10)
Github contributions (5)
microsoft/TextWorld
Aug 2018 - Nov 2022
TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:9 releases, 4 reviews, 381 commits in 4 years 3 months
Contributions summary:Marc-alexandre contributed to the project by addressing missing dependencies, refactoring module calls, and fixing various import issues. They also focused on maintaining the build process by restoring instructions for installing dependencies and including chromedriver. Moreover, the user improved the quality and reliability of the tests.
game-aigamesrl-agentsreinforcement-learningevaluation
nipy/nibabel
Feb 2014 - Feb 2022
Python package to access a cacophony of neuro-imaging file formats
Role in this project:Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:6 reviews, 189 commits, 15 PRs in 8 years 1 month
Contributions summary:Marc-alexandre's contributions centered on enhancing the codebase's handling of streamlines, specifically related to the application of transformations and the integration of properties. The user implemented support for coordinate transformation operations on streamlines, enabling them to function in different spaces. Additionally, the user addressed bug fixes in relation to TRK files, ensuring the correctness of streamline counts. The user also added unit tests for TCK file format, indicating a focus on testing the correctness of streamline handling.
pythontckimagingtrkstreamlines
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