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Ségolène Martin



I am working as a postdoctoral researcher at TU Berlin, in the image analysis team headed by Prof. Gabriel Steidl. My current research is at the intersection of optimization, optimal transport and generative learning, with applications to image processing and computer vision.


Prior to that, I was a PhD student in applied mathematics at CentraleSupélec, Université Paris-Saclay, in the CVN / OPIS Inria team. My PhD was under the supervision of Prof. Jean-Christophe Pesquet (CVN, CentraleSupélec) and Prof. Ismail Ben Ayed (ILLS, ETS Montréal). I defended my thesis Variational methods for large scale data problems in imaging on January 26th, 2024.


Research interests


  • Large-scale continuous optimization
    Majoration-Minimization & proximal algorithms, Expectation-Maximization, convergence theory
  • Inverse problems for image processing
    Image restoration and reconstruction, PSF estimation
  • Image classification
    Clustering and few-shot optimization-based methods, unbalanced few-shot, transductive learning
  • Text-vision models
    Zero and few-shot classification with CLIP

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


Journal articles

  • 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, Submitted, in hal-04296247, 2023.
  • 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, Y. Huang, F. Shakeri, J.-C. Pesquet, I. Ben Ayed Transductive zero-shot and few-shot CLIP, Accepted at the 2024 IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 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, Accepted at the 2024 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 29th European Signal Processing Conference (EUSIPCO), 2021.

Talks

  • October 2024 - Unbalanced few-shot classification, DATAIA workshop on Mathematical foundations of artifical 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 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