Zachary Ouellet
Directeur at CS Games
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolZachary Ouellet is a bilingual (FR/EN) aerospace engineer-turned-software leader based in Montreal, with 10 years of hands-on experience spanning C++, C#, Java, Python, JavaScript/TypeScript, Rust and modern front-end frameworks like React and Angular. Currently Directeur at CEGL and vice-president of the CS Games organizing committee, he blends technical leadership with event and education-focused roles that foster community and skills development. His background from ÉNA and Polytechnique Montréal gives him strong systems-thinking and mechanical design chops, complemented by practical experience in Unreal and Unity game development and frequent participation in Game Jams. Zachary has led cross-disciplinary teams in avionics and robotics projects (Exocet, research internships) and shipped production C++ work at CAE, demonstrating an ability to move between low-level systems and high-level product coordination. He’s equally comfortable prototyping interactive simulations as he is designing mechanical assemblies, which makes him a versatile bridge between software, hardware and education initiatives.
9 years of coding experience
1 year of employment as a software developer
DEC Génie aérospatial, DEC Génie aérospatial at ÉNA | École nationale d'aérotechniqueBaccalauréat Génie aérospatial, Baccalauréat Génie aérospatial at Polytechnique Montréal
Github Skills (10)
heterogeneous10
inversion10
waveform10
recurrent9
velocity7
deep-learning5
floating-point4
neural-network4
floating4
python3
Programming languages (2)
Github contributions (5)
gfabieno/SeisCL
May 2016 - Nov 2022
Software for viscoelastic full waveform inversion on large heterogeneous clusters
Contributions:1620 commits, 1 PR, 175 pushes in 6 years 6 months
clustersinversionwaveformheterogeneous
GeoCode-polymtl/Deep_1D_velocity
Oct 2019 - Oct 2022
Code for the article : Seismic velocity estimation: a deep recurrent neural-network approach
Contributions:1 release, 9 commits, 5 pushes in 3 years
deep-learningapproachrecurrent-neural-networkestimationneural-network
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