Segolene Martin homepage

Ségolène Martin

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I am a tenured researcher (Chargée de Recherche) at Inria Lyon in the OCKHAM team.
My current research is at the intersection of optimization and generative learning, with applications to image processing and computer vision.
Previously, I was a postdoctoral researcher at TU Berlin, in the team headed by Prof. Gabriele Steidl. I completed my PhD in applied mathematics at CentraleSupélec, Université Paris-Saclay, in the CVN / OPIS Inria team. I did my PhD under the supervision of Prof. Jean-Christophe Pesquet (CVN, CentraleSupélec) and Prof. Ismail Ben Ayed (ILLS, ETS Montréal).

Research interests

Large-scale continuous optimization
Majoration-Minimization, proximal algorithms, subspace acceleration, convergence theory
Inverse problems for imaging
Image restoration and reconstruction in the Bayesian framework
Generative learning
Flow-based models, GANs, VAEs
Image classification
Clustering and few-shot optimization-based methods, unbalanced few-shot, transductive learning, text-vision models

Education

PhD in Applied Mathematics, 2020-2024
CentraleSupélec, Université Paris-Saclay
Master in Mathematics, Vision and Learning (MVA), 2019-2020
École Normale Supérieure Paris-Saclay
Agrégation in Mathematics, 2018-2019
École Normale Supérieure Paris-Saclay
Bachelor's Degree in Fundamental Mathematics, 2016-2017
École Normale Supérieure Paris-Saclay

Publications

Publication preceeded with a * are the ones where I am a main author.

Submitted papers

*A. Clerc, S. Martin, N. Papadakis, G. Steidl, Reinterpreting EMML as Mirror Descent for Constrained Maximum Likelihood Estimation, arXiv preprint arXiv:2602.13063
A. Gagneux, S. Martin, R. Gribonval, M. Massias, The Generation Phases of Flow Matching: a Denoising Perspective , arXiv preprint arXiv:2510.24830.
C. Drygala, H. Gottschalk, T. Kruse, S. Martin, A. Muetze, Learning Brenier Potentials with Convex Generative Adversarial Neural Networks , arXiv preprint arXiv:2504.19779.

Journal articles

*S. Martin, J.-C. Pesquet, G. Steidl, I. Ben Ayed, Variable Bregman Majorization-Minimization Algorithm and its Application to Dirichlet Maximum Likelihood Estimation , Journal of Applied and Numerical Optimization.
* J. Ajdenbaum, E. Chouzenoux, S. Martin, C. Lefort, J.-C. Pesquet, A novel variational approach for multiphoton microscopy image restoration: from PSF estimation to 3D deconvolution, Inverse Problems, 2024.
J.-B. Fest, T. Heikkilä, I. Loris, S. Martin, L. Ratti, S. Rebegoldi, G. Sarnighausen, On a fixed- point continuation method for a convex optimization problem, Advanced Techniques in Optimization for Machine learning and Imaging (ATOMI), 2023.
*E. Chouzenoux, S. Martin, J.-C. Pesquet, A local MM subspace method for solving constrained variational problems in image recovery Journal of Mathematical Imaging and Vision, 2022.

Conference Proceedings

*S. Martin, A. Gagneux, P. Hagemann, G. Steidl, PnP-Flow: Plug-and-Play Image Restoration with Flow Matching., to appear in International Conference on Learning Representations (ICLR), 2025.
M. Adipoetra, S. Martin, Deep Generative Clustering with VAEs and Expectation-Maximization , to appear in International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) (oral), 2025.
*S. Martin, Y. Huang, F. Shakeri, J.-C. Pesquet, I. Ben Ayed, Transductive zero-shot and few-shot CLIP, IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) as a highlight (<10 % of papers), 2024.
*A. Sadraoui, S. Martin, E. Barbot, A. Laurent-Bellue, J.-C. Pesquet, C. Guettier, A transductive few-shot learning approach for classification of liver cancer histopathology images, IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
*S. Martin, M. Boudiaf, E. Chouzenoux, J.-C. Pesquet, I. Ben Ayed, Towards practical few-shot query sets : Transductive minimum description length inference, Neural Information Processing Systems (NeurIPS), 2022.
M. Kahanam, L. Le-Brusquet, S. Martin, J.-C. Pesquet, A Non-Convex Proximal Approach for Centroid-Based Classification, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.
*S. Martin, E. Chouzenoux, J.-C. Pesquet, A Penalized Subspace Strategy for Solving Large-Scale Constrained Optimization Problems, IEEE European Signal Processing Conference (EUSIPCO), 2021.

Thesis

* S. Martin, Variational methods for large scale data problems in imaging (26.01.2024)

Talks

October 2025 – From Flow Matching to Denoising... and back?, GDR IASIS: Generative Models – Diffusion, Flow Matching and Their Applications, ENS Lyon, France.
September 2025 – From Flow Matching to Denoising... and back?, Workshop on Blind Inverse Problems in Imaging: From Foundations to Applications, CIRM, Marseille, France.
June 2025 – Brenier Generative Adversarial Networks, Math+ Spotlight, online.
May 2025 – Plug-and-Play Image Restoration with Flow Matching, SMAI Conference, Bordeaux, France.
October 2024 – Plug-and-Play Image Restoration with Flow Matching, Seminar of the LIP – Machine Learning and Signal Processing, ENS Lyon, France.
October 2023 – Unbalanced Few-Shot Classification, DATAIA Workshop on Mathematical Foundations of Artificial Intelligence, Sorbonne Center for AI, Paris, France.
December 2022 – Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference, Seminar of the ILLS, Montreal, Canada.
July 2022 – Numerical Restoration of Multiphoton Images, Seminar of the XLIM, Limoges, France.
May 2022 – Penalized Methods for Solving Constrained Variational Problems in Image Recovery, Mini-Symposium: Variational Methods for Inverse Problems in Imaging – 10th International Conference on Inverse Problems Modeling and Simulation.
March 2022 – A Penalized Subspace Strategy for Solving Large-Scale Constrained Optimization Problems, Mini-Symposium: Novel Perspectives in Optimization and Machine Learning for Imaging – SIAM Conference on Imaging Science.

I am also involved in several teaching activities. I have been a teaching assistant for the following courses:

Refresher course on optimization, Master 2 MVA, ENS Paris-Saclay, 2020-2023 - course taught by J.-C. Pesquet (exercises, correction)
Optimization, Master 1, CentraleSupelec, 2023 - course taught by J.-C. Pesquet
Data analysis and statistics, Bachelor 1, IUT Orsay, 2022 - course taught by S. Marduel
Python, Bachelor 1, IUT Orsay, 2022 - course taught by S. Marduel
Signal processing, Bachelor 2, IUT Orsay, 2021 - course taught by F. Alberge
Data analysis and statistics, Bachelor 1, IUT Orsay, 2021 - course taught by T. Guilbaud
Python, Bachelor 1, IUT Orsay, 2021 - course taught by T. Guilbaud
Mathematics, Bachelor 1, IUT Orsay, 2020 - course taught by M. Lagron
Oral interrogations in mathematics in "Classes Préparatoires aux Grandes Ecoles", Lycée Saint Louis, Paris, 2019-2020