Elvis Dohmatob
Associate Professor
Paris, Ile-de-France, Canada
Summary
👤
Senior🎓
Top SchoolElvis Dohmatob is an associate professor and research scientist with 14 years of experience at the intersection of mathematics, machine learning, optimization, and cognitive neuroscience, currently affiliated with Concordia University and Mila. He holds a PhD in Computer Science from Université Paris Sud and a Master’s in Cryptology, reflecting a rare blend of theoretical grounding and applied research. His industry experience includes research roles at Facebook AI and Criteo, where he translated ML research into robust engineering, while his open-source contributions to flagship projects like scikit-learn, nilearn, and nipy demonstrate practical impact on neuroimaging and ML tooling. Known for meticulous bug fixes, visualization improvements, and masking algorithm corrections, he brings both scientific rigor and production-quality coding to projects. Colleagues appreciate that he pairs deep mathematical intuition with hands-on software craftsmanship—often improving usability through subtle but high-impact API and plotting refinements.
14 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Université Paris Sud (Paris XI)Master 2, Cryptologie et Sécurité Informatique, Master 2, Cryptologie et Sécurité Informatique at Université Bordeaux I
English, French
Github Skills (23)
visualization10
python10
testing10
scikit10
machine-learning10
numpy10
neuroimaging10
scikit-learn10
visualizations10
scientific-computing10
data-analysis10
matplotlib9
debug9
scikit-image9
refactoring9
Programming languages (7)
Github contributions (5)
nilearn/nilearn
Feb 2014 - Aug 2017
Machine learning for NeuroImaging in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:856 commits, 30 PRs, 242 comments in 3 years 7 months
Contributions summary:Elvis primarily focused on bug fixes within the nilearn library, specifically addressing issues related to masking functions and tests. Their contributions centered on the implementation of corrections to the code base and its supporting test cases. They modified the core masking functions to resolve various code defects.
neuroimagingpythondecodingbrain-connectivitymachine-learning
scikit-learn/scikit-learn
Oct 2015 - Jun 2017
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:6 commits, 14 PRs, 80 comments in 1 year 7 months
Contributions summary:Elvis primarily contributed to bug fixes and code refactoring within the scikit-learn machine learning library. Their work includes resolving an issue with the `decision_function_shape` parameter in `SVC` and addressing a Python 2.7 compatibility issue in an example. They also refactored code in the `_RidgeGCV` class to reduce duplication. Additionally, the user added documentation and corrected the behavior of `RidgeClassifier`.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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