Nicolas Gensollen
Enseignant Chercheur at Aflokkat
Ajaccio, Corsica, France
Summary
🤩
Rockstar🎓
Top SchoolNicolas Gensollen is an enseignant-chercheur and machine learning engineer with 11 years of experience bridging academic research and production-ready scientific software. He holds a PhD in computer science and has applied statistical modeling and ML to neuroimaging, complex networks, and smart-grid problems across roles at INRIA, LIP6 and multiple postdoctoral positions. At INRIA he developed neuroimaging algorithms and contributed backend and ML improvements to the widely used Nilearn library, focusing on robust image processing, new data-type support and clear documentation. Now based in Ajaccio and leading research at Aflokkat, he combines teaching, research and software engineering to deliver reproducible tools for cognitive neuroscience. Colleagues note his rare mix of rigorous mathematical modeling with pragmatic software craftsmanship and a knack for surfacing subtle data-handling issues before they reach users.
11 years of coding experience
5 years of employment as a software developer
Baccalauréat, S, Baccalauréat, S at lycée richelieuPhD, Computer science, PhD, Computer science at Université Pierre et Marie Curie (Paris VI)Diplôme d'ingénieur, Ingénierie des télécommunications, Diplôme d'ingénieur, Ingénierie des télécommunications at Télécom SudParisClasses préparatoires MP, Mathématiques et Physique, Classes préparatoires MP, Mathématiques et Physique at Lycée Pasteur
German, French, English
Github Skills (11)
scikit-learn10
data-analysis10
machine-learning10
python10
neuroimaging10
scikit10
image-processing9
numpy9
documentation8
pytorch4
tensorflow4
Programming languages (8)
Github contributions (5)
nilearn/nilearn
Nov 2020 - Aug 2022
Machine learning for NeuroImaging in Python
Role in this project:
Backend Developer & ML Engineer
Backend Developer & ML EngineerContributions:4 releases, 884 reviews, 201 commits in 1 year 9 months
Contributions summary:Nicolas's commits primarily focused on enhancing the functionality of the Nilearn library, particularly in the areas of image processing and statistical analysis. Their work involved modifying existing code to address issues related to data handling and implementation of new features. Their contributions include adding support for specific data types and refactoring to improve the software's robustness. Furthermore, the user demonstrated a focus on documentation, ensuring accurate reporting of results and the inclusion of new features.
neuroimagingpythondecodingbrain-connectivitymachine-learning
NicolasGensollen/clinica
Feb 2022 - Apr 2025
Software platform for clinical neuroimaging studies
Contributions:1091 pushes, 533 branches, 3 tags in 3 years 1 month
neuroimagingpythonneuroscienceclinicalcomputational-neuroscience
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