Nicolas Courty

Nicolas Courty

Full Professor of Computer Science

Université Bretagne Sud


“One should always be a little improbable.”

Oscar Wilde

Nicolas Courty is Full Professor at University Bretagne Sud since 2018. He obtained his PhD degree in 2002 from INSA Rennes and his 'Habilitation à diriger des recherches' in. 2013, on the topic of computer graphics and animation (avatars, crowds), with a specialization in data-driven methods. He now leads the Obelix team in IRISA, dedicated to machine learning and its applications to Earth Observation. He is an experienced researcher in the domain of machine learning and AI. Among others, he has published several papers in top tier machine learning conferences (NeurIPS, ICLR, ICML, AISTATS, etc.), computer vision (IEEE TPAMI, ECCV, ACCV) and remote sensing (IEEE TGRS, ISPRS journal). From 2014, he has developed an expertise in the domain of optimal transport and related applications to machine learning. He is also one of the recipients of the U.V. Helava Award, awarded by the International Society for Photogrammetry and Remote Sensing (ISPRS), for the best paper in the ISPRS journal in years 2012--2015. Nicolas Courty is a member of the European ELLIS society. From 2020, he pilots an ANR Chair program on AI (OTTOPIA), on the topic of applied optimal transport for Remote Sensing.

Keywords: Optimal transport, Statistical learning, kernel methods, manifold and geometric approaches, optimal control

Recurrent collaborators


  • POT The python Optimal Transport Toolbox

On-going PhD Students

Recently defended PhD thesis:


I mostly teach computer and data science at Université Bretagne Sud. Among others, I give the following courses:

  • Deep Learning M2 AIDN
  • High performance computing and GPU programming M1 AIDN

I also teach in the Master Erasmus Mundus Copernicus CDE in the GeoData Science track.


We offer some positions at Obelix for master internships, PhD positions or post-docs. Consult the corresponding webpage. Also, if you are interested in doing research with me, and if you have an excellent track record but do not find a suitable announce, feel free me to contact me or use contact widget !

Recent Posts and news

Two papers to be published in TMLR journal

Two papers were recentmy accepted to the new journal Transation on Machine Learning Research Efficient Gradient Flows in Sliced-Wasserstein Space Arxiv Link Time Series Alignment with Global Invariances Arxiv Link

Two papers accepted at Neurips 2022 (with one oral) !

Two paper wills be featured in the Neurips 2022 program ! Congratulations to main authors (among others, Cédric Vincent-Cuaz, Huy Tran and Alexis Thual), and amazing collaborators ! Template based Graph Neural Network with Optimal Transport Distances Arxiv Link Aligning individual brains with Fused Unbalanced Gromov-Wasserstein Arxiv Link

One paper in ICLR 2022

One paper will be featured in the ICLR 2022 program on semi Relaxed Gromov-Wasserstein and its applications (work from Cédric Vincent-Cuaz) Semi-relaxed Gromov-Wasserstein divergence with applications on graphs Arxiv Link

Paper Accepted in IEEE Transactions on PAMI

Our paper on Wasserstein Adversarial Regularization was accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) Wasserstein Adversarial Regularization for learning with label noise Arxiv Link

2 new publications at ICML 2021

Two of our papers have made it to ICML 21 ! Unbalanced minibatch Optimal Transport; applications to Domain Adaptation Arxiv Link Online Graph Dictionary Learning" Arxiv Link

On-going Projects

ANR Chair in AI OTTOPIA (2021–2025)

OTTOPIA is a project dedicated to novel machine learning methods for solving Remote Sensing challenges, mostly based on applications of optimal transport to problems such as e.g. generalized domain adaptation, graph classification and prediction, or causality in remote sensing

CominLabs Dynalearn (2020–2024)

This project aims at contributing on the interactions between deep learning and physical models in a two-fold way: i) by exploring how dynamical formulation of learning process can help in understanding better learning deep neural architectures, as well as proposing new learning paradigms based on the regularization of the flows of information; ii) By leveraging on novel neural architectures and available data to devise new data-driven dynamical simulation models.

ANR PRC OATMIL (2017–2021)

The OATMIL project is funded by ANR. The goal is to bring innovative use of optimal transport in machine learning, and machine learning for the computation of optimal transport problems. I serve as coordinator for this project

Talks & Courses

Sliced Optimal Transport distances on Manifolds: Spherical and Hyperbolical cases
Optimal Transport for Graph-Signal Processing and Learning
Optimal Transport for Graph-Signal Processing and Learning
Sliced Optimal Transport distances on Manifolds
Introduction to Optimal Transport for Machine Learning

Recent Publications

Quickly discover relevant content by filtering publications.
(2022). Sliced-Wasserstein Gradient Flows. Transaction on Machine Learning Research.


(2022). Semi-relaxed Gromov-Wasserstein divergence for graphs classification. Colloque GRETSI 2022 - XXVIIIème Colloque Francophone de Traitement du Signal et des Images.


(2021). Subspace Detours Meet Gromov-Wasserstein. NeurIPS, workshop on Optimal Transport in Machine Learning.



  • (+33) 2 97 01 72 13
  • Campus de Tohannic, Vannes, Bretagne 56000
  • Enter ENSIBS Building and take the stairs to Office C103 on Floor 1