Rodrigue De Schaetzen
Graduate Researcher
Montreal, Quebec, Canada
Summary
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Senior🎓
Top SchoolRodrigue De Schaetzen is a PhD student and graduate researcher specializing in robotics and embodied AI, currently based at Mila and the Université de Montréal with eight years of applied research and teaching experience. He combines a strong foundations in systems and control (MEng/MASc) and computer science (BS) with hands-on work in sampling-based MPC, cloud offloading for SLAM, and efficient path-following for game and real-world vehicles. His internships and research roles—spanning EA, UBC, ZHAW, and Waterloo—demonstrate a pattern of translating theoretical controls and learning methods into scalable pipelines and performance gains (including a 70% reduction in stuck vehicles in a game setting). He also has a track record of improving developer and deployment workflows, from speeding robot-cloud registration from hours to minutes to building neural dynamics training pipelines. Known for bridging academic rigor with pragmatic engineering, he teaches graduate and undergraduate courses while pushing autonomy research toward real-world deployment.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science (Robotics and AI), Doctor of Philosophy - PhD Computer Science (Robotics and AI) at Université de MontréalBachelor of Science - BS Combined Honours Computer Science and Physics, Bachelor of Science - BS Combined Honours Computer Science and Physics at The University of British ColumbiaMaster of Applied Science Electrical and Computer Engineering (Systems and Control), Master of Applied Science Electrical and Computer Engineering (Systems and Control) at University of Waterloo
English, French, German
Github Skills (95)
octave10
marine9
matlab9
audio9
whenever9
annotations9
pymunk9
toolbox9
euler9
simulation9
pytorch9
grasp9
flux9
pygame9
computer-vision8
Programming languages (9)
Github contributions (5)
rdesc/autorally
May 2020 - Jan 2021
Software for the AutoRally platform
Contributions:92 commits, 6 PRs, 79 pushes in 7 months
infinibandopenflownetboxtizenrdma
Project-X-UBC/smoke-detection-net
Oct 2020 - Jan 2021
Pipeline to train a multi-label smoke detection network. Code submission for the ProjectX 2020 machine learning competition.
Contributions:84 commits, 4 PRs, 18 pushes in 2 months
detection-networkpipelinedeep-learningmachine-learningmulti-label
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