Pierre-olivier Quirion
ARC HPC Specialist at Canadian Centre for Computational Genomics
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolPierre-Olivier Quirion is an ARC/HPC specialist and lead developer with 11+ years of hands-on experience building high-performance computing and cloud architectures for research domains including genomics, neuroimaging, astrophysics and CGI. With a PhD in astrophysics and a background managing MRI hardware/software, he blends deep numerical-simulation and signal-processing expertise (C/Fortran/Python) with practical Linux and distributed-systems skills. He currently supports computational genomics at McGill and Calcul Québec while contributing to open-source neuroimaging tooling such as nilearn, where he enhanced image resampling and plotting functionality. His career spans academia, government, and industry—designing pipelines for large optical systems and CCD data as well as clusters for animation rendering—so he’s as comfortable profiling MPI jobs as he is tuning multimedia production pipelines. Known for organizing national conferences and delivering international seminars, he pairs rigorous research instincts with effective communication and a pragmatic, solution-first engineering style.
11 years of coding experience
6 years of employment as a software developer
Ph.D. Astrophysics, Stellar physics, Ph.D. Astrophysics, Stellar physics at Université de MontréalLicence, Physics, Licence, Physics at Université Joseph Fourier (Grenoble I)
English, French
Stackoverflow
594reputation
171kreached
6answers
1question
Github Skills (21)
python10
image-processing10
brain-imaging10
scikit10
machine-learning10
neuroimaging10
scikit-learn10
mri10
visualization9
numpy9
visualizations9
pyqt9
testing8
openstack6
dockerfile6
Programming languages (17)
Github contributions (5)
nilearn/nilearn
Nov 2017 - Nov 2017
Machine learning for NeuroImaging in Python
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 2 PRs, 2 comments in 1 day
Contributions summary:Pierre-olivier contributed to the `nilearn` library by adding new functionality to image resampling functions, specifically the `clip` option to handle image value ranges. They implemented tests to validate the new clip functionality. Additionally, the user added new views to the `plot_surf_*` functions and integrated example code to showcase the use of these new views.
neuroimagingpythondecodingbrain-connectivitymachine-learning
c3g/auspice
Apr 2020 - Jan 2022
Web app for visualizing pathogen evolution
Contributions:12 PRs, 48 pushes, 41 branches in 1 year 8 months
pathogenreactevolutionvisualizingvisualization
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