Olivier Dionne
Principal Developer - Digital Human(oid) Tech at NVIDIA
Quebec, Canada
Summary
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Rockstar🎓
Top SchoolOlivier Dionne is a Principal Developer specializing in digital humanoid technologies with a decade of experience bridging research and production at companies like NVIDIA, Unity, and Autodesk. He leads R&D on motion transfer, ML-driven character motion, and humanoid robotics, applying deep expertise in animation, rendering, and math-heavy systems. His contributions to Unity.Mathematics — including quaternion/Euler conversions and affine transform tests — reflect a strong focus on robust geometric and numerical tooling for real-time engines. A former lecturer and research associate with an M.A.Sc. in Software Engineering from Polytechnique Montréal, he combines academic rigor with hands-on product delivery. Based in Quebec, he is known for turning complex motion and interaction research into scalable software for industry-grade simulations and robots.
11 years of coding experience
18 years of employment as a software developer
M. A. Sc., Master of Applied Science in Software Engineering, M. A. Sc., Master of Applied Science in Software Engineering at Polytechnique Montréal
English, French
Github Skills (10)
unity3d10
mathematics10
csharp10
math10
linear-algebra10
dotnet-core10
testing10
quaternions9
affine-transformation9
test-automation8
Programming languages (4)
Github contributions (5)
Unity-Technologies/Unity.Mathematics
Nov 2021 - Feb 2022
The C# math library used in Unity providing vector types and math functions with a shader like syntax
Role in this project:
Back-end Developer
Back-end DeveloperContributions:17 reviews, 18 commits, 3 PRs in 2 months
Contributions summary:Olivier primarily contributed to the development of the C# math library used in Unity. Their work included implementing and testing quaternion to Euler conversion functions, adding missing conversion methods, and refactoring existing code for improved readability. They also wrote tests to validate the inverse and decomposition functionality of the affine transform. This indicates a focus on mathematical functions and their integration within the Unity environment.
csharpmath-libraryshader
oldio/pytorch
Aug 2024 - Sep 2024
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:6 pushes, 1 branch in 18 days
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