Stanislas Chambon
Chief Science Officer at Reliev
Nantes, Pays de la Loire, France
Summary
👤
Senior🎓
Top SchoolStanislas Chambon is a Chief Science Officer and research-driven machine learning leader with 11 years of experience translating cutting-edge research into deployed clinical AI, most recently leading science at Reliev after five years developing deep learning for breast cancer screening at Therapixel. He holds a PhD in machine learning from Université Paris-Saclay and combined applied mathematics and operations research training from École Polytechnique and TUM, bringing rigorous theory to production problems. His work spans EEG and sleep micro-event detection, transfer learning, and medical imaging, with a practical bent for production-ready algorithms and robustness. An active open-source contributor, he strengthened core optimal transport and domain adaptation code in the well-known POT Python library, reducing errors and improving reuse. Colleagues describe him as someone who bridges academia and product teams, able to shepherd complex models from prototype to regulatory-minded deployment. Early leadership experience in the French Gendarmerie hints at a pragmatic, disciplined approach to team management and operational rigor.
11 years of coding experience
10 years of employment as a software developer
Applied mathematics, Applied mathematics at École PolytechniqueMaster of Science (M.Sc.) Mathematics in Operations Research, Master of Science (M.Sc.) Mathematics in Operations Research at Technical University of MunichDoctor of Philosophy - PhD Machine learning, Doctor of Philosophy - PhD Machine learning at Université Paris-SaclayClasse préparatoire filière Physique-Chimie, Classe préparatoire filière Physique-Chimie at Lycée Carnot (Dijon)
English, Spanish, German, Italian
Github Skills (9)
machine-learning10
adaptation10
python10
numpy9
scipy8
algorithms8
data-structures8
algorithm8
data-structure8
Programming languages (2)
Github contributions (5)
PythonOT/POT
Jul 2017 - Sep 2017
POT : Python Optimal Transport
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:65 commits, 3 PRs, 47 comments in 1 month
Contributions summary:Stanislas focused on refactoring and improving existing domain adaptation code within the project. Their work involved fixing errors and removing error messages in Python files. They also contributed to core functionalities by working on the optimal transport algorithms and the methods to be used in these applications.
pythonot-mapping-estimationnumbanumerical-optimizationpattern-recognition
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.