Jean Feydy's home page


  • Anisotropic power diagrams for polycrystal modelling: efficient generation of curved grains via optimal transport,
    Maciej Buze, Jean Feydy, Steven M. Roper, Karo Sedighiani, David P. Bourne, links: Paper, Code.
  • An optimal transport model for dynamical shapes, collective motion and cellular aggregates,
    Antoine Diez, Jean Feydy, links: Paper, Videos, Code.
  • Approximation and structured prediction with sparse Wasserstein barycenters,
    Minh-Hieu Do, Jean Feydy, Olga Mula, links: Paper, Code.
  • Sinkhorn divergences for unbalanced Optimal Transport,
    Thibault Séjourné, Jean Feydy, François-Xavier Vialard, Alain Trouvé, Gabriel Peyré, links: Paper, Code.

PhD thesis

Journal Papers

  • Collective proposal distributions for nonlinear MCMC samplers: mean-field theory and fast implementation,
    Grégoire Clarté, Antoine Diez, Jean Feydy,
    Electronic Journal of Statistics, 2022, links: Paper, Code.
  • Kernel operations on the GPU, with autodiff, without memory overflows,
    Benjamin Charlier*, Jean Feydy*, Joan Glaunès*, François-David Collin, Ghislain Durif,
    Journal of Machine Learning Research, 2021, links: Abstract, Paper, Code.

Conference Papers

  • DiffMaSIF: Surface-based Protein-Protein Docking with Diffusion Models,
    Freyr Sverrisson, Mehmet Akdel, Dylan Abramson, Jean Feydy, Alexander Goncearenco, Yusuf Adeshina, Daniel Kovtun, Céline Marquet, Xuejin Zhang, David Baugher, Zachary Wayne Carpenter, Luca Naef, Michael Bronstein, Bruno Correia,
    MLSB 2023 (NeurIPS workshop), links: Paper, Workshop.
  • Physics-informed deep neural network for rigid-body protein docking,
    Freyr Sverrisson, Jean Feydy, Joshua Southern, Michael M Bronstein, Bruno Correia,
    MLDD 2022 (ICLR workshop, spotlight presentation), links: Paper.
  • Accurate point cloud registration with robust optimal transport,
    Zhengyang Shen*, Jean Feydy*, Peirong Liu, Ariel Hernán Curiale, Ruben San José Estépar, Raúl San José Estépar, Marc Niethammer,
    NeurIPS 2021, links: Paper, Latex, Code.
  • Fast end-to-end learning on protein surfaces,
    Freyr Sverrisson*, Jean Feydy*, Bruno Correia, Michael Bronstein,
    CVPR 2021, links: Paper, Poster, Slides, Video, Code.
  • Fast geometric learning with symbolic matrices,
    Jean Feydy*, Joan Glaunès*, Benjamin Charlier*, Michael Bronstein,
    NeurIPS 2020 (spotlight presentation), links: Paper, Slides + Latex, Poster + Latex, Links and Videos, Website, Code.
  • Fast and scalable optimal transport for brain tractograms,
    Jean Feydy*, Pierre Roussillon*, Alain Trouvé, Pietro Gori,
    MICCAI 2019, links: Paper, Poster + PowerPoint, Website, Code.
  • Interpolating between optimal transport and MMD using Sinkhorn divergences,
    Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, Gabriel Peyré,
    AiStats 2019, links: Paper, Slides, Poster + Latex, Website, Code.
  • Global divergences between measures: from Hausdorff distance to optimal transport,
    Jean Feydy, Alain Trouvé,
    ShapeMI 2018 (MICCAI workshop, oral presentation), links: Paper, Slides + Latex, Code.
  • Optimal transport for diffeomorphic registration,
    Jean Feydy, Benjamin Charlier, François-Xavier Vialard, Gabriel Peyré,
    MICCAI 2017 (oral presentation), links: Hal, Arxiv, Code, Slides (+videos), Poster, Latex.
  • Distortion minimizing geodesic subspaces in shape spaces and computational anatomy,
    Benjamin Charlier, Jean Feydy, David W. Jacobs and Alain Trouvé,
    VipImage 2017.

Medical Papers

  • Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome
    Quentin Hennocq, Marjolaine Willems, Jeanne Amiel, Stéphanie Arpin, Tania Attie-Bitach, Thomas Bongibault, Thomas Bouygues, Valérie Cormier-Daire, Pierre Corre, Klaus Dieterich, Maxime Douillet, Jean Feydy, Eva Galliani, Fabienne Giuliano, Stanislas Lyonnet, Arnaud Picard, Thantrira Porntaveetus, Marlène Rio, Flavien Rouxel, Vorasuk Shotelersuk, Annick Toutain, Kevin Yauy, David Geneviève, Roman H. Khonsari and Nicolas Garcelon
    Nature Scientific Reports, 2024, links: Paper.
  • Artificial intelligence in diagnostic and interventional radiology: Where are we now?
    Tom Boeken, Jean Feydy, Augustin Lecler, Philippe Soyer, Antoine Feydy, Maxime Barat, Loïc Duron,
    Diagnostic and Interventional Imaging, 2022, links: Paper.
  • Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications,
    Maxime Lacroix, Theodore Aouad, Jean Feydy, David Biau, Frédérique Larousserie, Laure Fournier, Antoine Feydy,
    Diagnostic and Interventional Imaging, 2022, links: Paper.
  • Accelerating high-dimensional temporal modelling using Graphics Processing Units for pharmacovigilance signal detection on real-life data,
    Pierre Sabatier, Jean Feydy, Anne-Sophie Jannot,
    Challenges of Trustable AI and Added-Value on Health, 2022, links: Paper.

Maths and CS Talks

  • Computational optimal transport: recent speed-ups and applications.
    March 2024, PSDOL, Lagrange center, Paris: Pdf, Latex, Videos, Workshop.
  • Software bottlenecks for 3D AI.
    February 2024, X-IA #16, BPI France, Paris: Pdf, Pptx, Latex, Workshop.
  • Optimal transport with 3D shapes.
    December 2023, GT CalVa, Université Paris-Dauphine: Pdf, Latex, seminar.
    December 2023, G-Stats Seminar, Inria Sophia-Antipolis: Pdf, Latex, seminar.
    December 2023, SMAI-SIGMA day, Jussieu: Pdf, Latex, workshop.
  • Computational optimal transport: mature tools and open problems.
    November 2022, Measure-theoretic approaches and optimal transportation in statistics, Institut Henri Poincaré: Pdf, Latex, workshop.
    August 2022, Workshop on mathematical imaging and surface processing, Oberwolfach: Pdf, Latex, workshop.
    July 2022, Frontiers in Design Representation, University of Maryland: Pdf, Latex, Tutorial (HTML), Summer school.
    June 2022, Curves and Surfaces 2022, Arcachon: Pdf, Latex.
    June 2022, University of Göttingen: Pdf, Latex.
  • Fast libraries for geometric data analysis.
    May 2023, Healthcare AI grand round, Nvidia: Pdf, Latex.
    May 2023, Workshop on geometry/physics-informed neural networks, Thales: Pdf, Latex.
    February 2023, Machine Learning Coffee Seminar, Finnish Center for Artificial Intelligence: Pdf, Latex, Seminar.
    July 2022, online meeting with Nvidia: Pdf, Latex.
    May 2022, joint HeKA-Soda seminar, PariSanté Campus: Pdf, Latex.
  • Fast geometric libraries for vision and data sciences.
    April 2022, DataShape seminar, Inria Saclay: Pdf, Latex.
    December 2021, GRAPES software and industrial workshop, Inria Sophia: Conference, Pdf, Latex.
    December 2021, AI and healthcare seminar, Centre de Recherche des Cordeliers: Pdf, Latex.
    November 2021, JCJC développement, Inria Saclay: Conference, Pdf, Latex.
    November 2021, Robotic Perception team, Université de Picardie Jules Verne, Amiens: Pdf, Latex.
    October 2021, GdR MIA, Institut Henri Poincaré: Conference, Pdf, Latex.
  • Calcul géométrique rapide pour la vision et les sciences des données.
    September 2021, Orasis 2021, Lac de Saint-Ferréol: Conference, Pdf, Latex.
  • Fast geometric learning with symbolic matrices.
    January 2021, CogSys seminar, DTU Compute (Online): Pdf, Latex.
    December 2020, NeurIPS 2020 (Online): Spotlight presentation + Latex, Poster + Latex, Links and Videos.
  • Geometric data analysis, beyond convolutions.
    April 2021, Signal Processing Laboratory (LTS4, EPFL, Online): Pdf, Latex.
    March 2021, Image, Visual and Language Computing Seminar (UNC Chapel Hill, Online): Pdf, Latex.
    January 2021, Centre de Vision Numérique (CentraleSupélec - Inria Saclay, Online): Pdf, Latex.
    October 2020, Centre de Recherche des Cordeliers (Online) - in French: Pdf, Latex, Video.
    September 2020, University College London (Online) - with more applications: Pdf, Latex, Video, Workshop.
    July 2020, PhD defense (Online): Pdf, Latex.
    December 2019, King's College London: Pdf, Latex.
  • Geometric loss functions for shape analysis
    July 2020, SIAM Imaging Sciences 2020 (Online): Pdf, Video, Latex.
  • Sorting points in dimension D > 1.
    April 2021, Sea Ice Modeling and Data Assimilation (Dartmouth, Online): Pdf, Latex.
    February 2020, Twitter London: Pdf, Latex.
  • Discrete optimal transport: scaling up to 1M samples in 1s.
    June 2019, "People in optimal transportation and applications" workshop, Cortona: Pdf, Latex.
  • Robust matching of measures with Optimal Transport.
    February 2019, GTTI, ENS Cachan: Pdf, Latex.
    December 2018, BIRS center, Banff: Pdf, Latex.
    November 2018, Télécom ParisTech: Pdf, Latex.
  • Global divergences between measures, from Hausdorff distance to Optimal Transport.
    September 2018, ShapeMI workshop, MICCAI 2018 (Granada): Pdf, Latex + Code.
    July 2018, Curves and Surfaces 2018 (Arcachon): Pdf, Latex + Code.
  • Normalizing LDDMM metrics using autodiff, an introduction to KeOps.
    June 2018, SIAM Imaging Sciences 2018 (Bologna): Pdf, Latex + Code.
    November 2017, Isaac Newton Institute (Cambridge): Pdf, Latex.
  • Optimal transport for diffeomorphic registration: a global and robust data attachment term.
    September 2017, MICCAI 2017, Québec City: Pdf (+videos), Poster, Latex.
    June 2017, Asclepios Inria Team: Pdf, Latex.

Radiology Talks

Miscellaneous Talks

  • Présentation de l'équipe HeKA
    June 2023, Matinée Science à PariSanté Campus: Pdf, Pptx, Latex.
  • Tools to write and publish your code
    June 2023, HeKA team seminar, PariSanté Campus: Pdf, Latex.
  • Du calcul haute performance à l'hôpital ?
    May 2023, Séminaire Médecine et Mathématiques, Centre Borelli: Pdf, Latex.
    February 2023, Demi-heure de science, Inria Paris: Pdf, Latex.
  • Getting started with clusters.
    January 2023, Team building seminar of the HeKA team, Rouen: Pdf, Latex.
  • Retour sur le sondage auprès des utilisateurs des moyens de calcul Inria.
    June 2022, CEP Inria Paris: Pdf, Latex.
  • Geometric data analysis for healthcare.
    May 2021, application for the Inria CRCN position (Inria Paris, online): Application, Slides (in French), Video, Latex, Questions, CRCN-ISFP cheat sheet.
  • Automatic differentiation for applied mathematicians: presenting the PyTorch library.
    February 2018, at the ENS Ulm, ENS Cachan and Jussieu (Paris VI): Pdf, Latex + Code.
  • Riemannian geometry for computational anatomy: presenting the LDDMM framework.
    October 2017, ENS Department of Mathematics and Applications: Pdf, Full Latex.
    June 2017, MoKaPlan Inria Team: Pdf (+videos), Latex.