Mehdi Rahim
Research Scientist at Air Liquide
Orsay, Île-de-France, France
Summary
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Rockstar🎓
Top SchoolMehdi Rahim is a research scientist with 11 years of experience at the intersection of machine learning and neuroimaging, currently holding roles at Air Liquide, Groupe BPCE and as a postdoc at Inria. He earned a PhD in computer science with a focus on image processing and has a strong academic-to-industry trajectory including postdoctoral work at CEA and research positions at Aix-Marseille. Mehdi contributes to prominent open-source neuroimaging tools such as nilearn, improving visualization and testing quality for ML workflows on brain data. His work blends rigorous research methods with practical engineering—optimizing plotting parameters, fixing argument issues and slimming test assets to boost usability and reproducibility. Based in Orsay, France, he brings a rare mix of deep domain expertise in neuroimaging and hands-on software craftsmanship applied in both research labs and corporate R&D.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Image processing, Doctor of Philosophy (PhD), Computer Science, Image processing at Université d'Aix-MarseilleEngineer's degree, Computer Science, Engineer's degree, Computer Science at Université d'Oran Es-Sénia
Github Skills (10)
machine-learning10
python10
plot10
neuroimaging10
brain-imaging10
numpy10
fmi9
mri9
scikit8
scikit-learn8
Programming languages (5)
Github contributions (5)
nilearn/nilearn
Feb 2015 - Jul 2019
Machine learning for NeuroImaging in Python
Role in this project:
ML Engineer
ML EngineerContributions:32 commits, 15 PRs, 2 pushes in 4 years 6 months
Contributions summary:Mehdi primarily contributed to the `nilearn` repository, focusing on enhancements to image plotting functionalities. Their work included adding and modifying parameters such as `colorbar` and `resampling_interpolation` to improve visualization capabilities. They also addressed argument-related issues and reduced the size of images used in testing, demonstrating a focus on code quality and usability within the context of neuroimaging data analysis.
neuroimagingpythondecodingbrain-connectivitymachine-learning
mrahim/eprime_parser
Nov 2014 - Sep 2015
Contributions:28 commits, 8 pushes, 1 branch in 10 months
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