Nicolas Courty
Professeur Des Universités at Université de Bretagne Sud
Vannes, Brittany, France
Summary
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Rockstar🎓
Top SchoolNicolas Courty is a French academic and researcher with nine years of professional experience specializing in optimal transport, machine learning, and signal processing. He is a Professor at Université de Bretagne Sud, seconded part-time to Inria where he advances links between optimal transport theory and applied ML. With a PhD in Mathematics and Computer Science and an HDR, he combines deep theoretical training with practical software contributions, notably extending the popular Python Optimal Transport (POT) library with new algorithms (generalized conditional gradient, Gromov–Wasserstein) and domain-adaptation tools. His work spans teaching, research supervision, and hands-on development, including fixing low-level C++ wrappers and producing illustrative examples for different regularizations. Based in Vannes, Brittany, he brings a rare mix of rigorous mathematical insight and pragmatic engineering to bridge research and open-source impact.
9 years of coding experience
Doctor of Philosophy - PhD, Mathematics and Computer Science, Doctor of Philosophy - PhD, Mathematics and Computer Science at INSA Rennes - Institut National des Sciences Appliquées de RennesMaster's degree, Artificial Intelligence, Master's degree, Artificial Intelligence at Université de Rennes IHabilitation à diriger des recherches (HDR), Habilitation à diriger des recherches (HDR) at Université de Bretagne Sud
Github Skills (10)
numerical-optimization10
python10
numpy10
data-structure9
machine-learning9
algorithm9
data-structures9
algorithms9
c-language7
cprogramming-language7
Programming languages (3)
Github contributions (5)
PythonOT/POT
Oct 2016 - Nov 2021
POT : Python Optimal Transport
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:7 reviews, 103 commits, 16 PRs in 5 years 1 month
Contributions summary:Nicolas primarily focused on adding new functionality and correcting previous code to the Python Optimal Transport library. Their contributions included adding a generalized conditional gradient algorithm for regularized optimal transport problems, creating new examples demonstrating the use of various regularization methods (Frobenius, Entropic), and implementing domain adaptation methods with group lasso regularization, all within the ot and ot.da modules. The user also corrected a bug in the C++ code wrapper and implemented Gromov-Wasserstein distance calculations.
pythonot-mapping-estimationnumbanumerical-optimizationpattern-recognition
ncourty/academic-kickstart
Mar 2020 - Nov 2022
📝 Easily create a beautiful website using Academic, Hugo, and Netlify
Contributions:5 pushes in 2 years 8 months
hugojamstacknetlifyhugo-themestatic-site-generator
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