Benoit Hamelin
Researcher at Government of Canada
Drummondville, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolBenoit Hamelin is a researcher and machine learning engineer with 14 years of experience applying advanced computation to cybersecurity and imaging problems. Currently at the Government of Canada, he develops ML solutions for cyber defense, drawing on prior roles as a solution architect at Element AI and CTO/Chief Scientist at Arc4dia where he led large-scale distributed systems to detect advanced persistent threats. His academic background (PhD in medical imaging) underpins a track record of turning mathematically sophisticated ideas into fast, production-ready software—from accelerating CT reconstructions to building lightweight sensors and Cassandra-backed analytics. An active open-source contributor, he improved spectral initialization and robustness in the widely used UMAP library, modernizing code and tests for newer SciPy versions. He combines deep numerical analysis, systems engineering, and practical product delivery, and is known for making complex methods accessible to both engineers and end users. Based in Drummondville, Quebec, he blends research rigor with hands-on coding to solve adversarial, real-world problems.
14 years of coding experience
20 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Medical imaging, Doctor of Philosophy (Ph.D.), Medical imaging at École Polytechnique de MontréalMaster of Science (M.Sc.), Computer Science, Master of Science (M.Sc.), Computer Science at Université de SherbrookeCollège Saint-Bernard
French, English
Github Skills (7)
machine-learning10
python10
omap10
imap10
testing9
scipy9
pytest8
Programming languages (4)
Github contributions (5)
lmcinnes/umap
Jul 2023 - Nov 2023
Uniform Manifold Approximation and Projection
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 4 PRs, 12 comments in 4 months
Contributions summary:Benoit primarily contributed to improving the codebase related to spectral initialization and testing within the UMAP library. They refactored the spectral initialization routines, consolidating multiple functions into a single implementation. Furthermore, the user addressed issues related to deprecated SciPy functions, adapting the code to ensure compatibility with newer versions and refactored unit tests. They also fixed several bugs related to improper use of data structures, contributing to the stability of the project.
projectiondimensionality-reductionmachine-learningtopological-data-analysisapproximation
ElementAI/greensim
Jul 2018 - Nov 2018
General tools for building a discrete event simulation
Contributions:1 release, 24 PRs, 37 pushes in 4 months
discrete-event-simulationsimulationdiscretediscrete-event
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