Institut de Mathématiques de Bordeaux
 351 Cours de la Libération
 33405 TALENCE Cedex, France
Email : nicolas.papadakis a
Phone : +33 5 40 00 21 16
Office : 251

CNRS Researcher



  • July 2019: Introduction for general (french) audience to Image colorization, in Interstices.

  • June 2019: Our paper GraphXNET - Chest X-Ray Classification Under Extreme Minimal Supervision has been accepted to MICCAI 2019.

  • April 2019: Elsa Cazelles has been awarded the 2018 Jacques Neveu Prize for her PHD on "Statistical properties of barycenters in the Wasserstein space and fast algorithms for optimal transport of measures"

  • March 2019: Antoine Houdard has started his post-doc within the GOTMI project

  • August 2018: Beginning of the 9 months stay in Cambridge with a NoMADS secondment at Mathworks

  • May 2018: CNRS article on our work on Wasserstein Geodesic PCA (Actualités de l'INSMI)

Research interests

  • Inverse problems in Imaging sciences
    Introduction of new variational and discrete models for image/video processing and analysis.
    Keywords: Segmentation, superpixels, denoising, motion estimation, tracking, stereovision, 3D reconstruction, color transfer, colorization, inpainting.
    Applications: Satellite data, Football replay, Medical imaging, Movie post-production.
  • Numerical methods for Optimal Transportation
    Definition of generalized optimal transportation models for comparing and interpolating densities
    Keywords: Physical-based regularity constraints, approximations for fast computation, data analysis in Wasserstein space.
    Applications: Computational photography, Audio analysis, Neural Networks.
  • Image Assimilation in Geosciences
    Improving numerical prediction with the integration of structured image information in data assimilation systems.
    Keywords: Variational and sequential assimilation, observation errors.
    Applications: Meteorology and Oceanography
  • Machine Learning and Graph representation
    Algorithms for processing non structured data represented by graph.
    Keywords: Graph cuts, cheeger cuts, clustering, label propagation, semi-supervized learning, unsupervised learning.
    Applications: Hyperspectral and Medical data