Julie Mabon

jmabon.fr | GitHub

Research Assistant

Postdoc at the Biological Image Analysis unit at Institut Pasteur, working on a diversity of bio-image analysis projects. PhD in Signal and Image processing on image analysis combining modern deep learning approaches with stochastic geometry, applied to remote sensing.

Technical Skills

  • Languages: French (native), English (fluent), German, Spanish (notions)
  • Programming: Python (Pytorch, Napari, dask) (++), LaTex/TikZ/Beamer (++), C, Java
  • Dev Tools: Visual Studio Code, Git, cluster/HPC
  • Other Tools: Blender, Photoshop/Gimp, Illustrator/Inkscape

Experience

2025 –

Postdoc in Bioimage Analysis, Institut Pasteur, ProteoVir project, Paris, France

Working on neuron segmentation in fluorescence microscopy images within the Biological Image Analysis team directed by Jean-Christophe Olivo-Marin.

  • Other activities: Various Image analysis projects in collaboration with biologists. Supervision of interns. Image analysis course teaching.

2021 – 2024

PhD in Signal and Image processing, Inria d’Université Côte d’Azur, Sophia-Antipolis, France

Directed by Josiane Zerubia (AYANA team, Inria) and Mathias Ortner (Airbus Defense and Space).

Learning Stochastic geometry models and convolutional neural networks. Application to multiple object detection in aerospatial data sets.

  • Member (2021-2022) then Secretary (2022-2023) of the ADSTIC: association of PhD students in the SophiaTech campus, organizing social, sportive and scientific events, as well as acting as an intermediary with the doctoral school.

2019 (6 months)

Research internship, EMBL-EBI, Uhlmann group, Cambridge, UK

Improving detection and tracking of individual Mycobacteria smegamtis in time lapse microscopy images of growing colonies, using graphical models and convolutional neural networks.

2016 – 2019

Master’s degree in Engineering, Ecole Centrale de Lille, Lille, France

  • Common core: Aero/Hydrodynamics, Thermodynamics, Signal processing, Project management, Materials science
  • DAD (Decision & Data Analysis) specialization: Probability and statistics optimization, machine learning

2018 – 2019

Applied Mathematics, Université de Lille

Functional analysis, Stochastic process, Itô integrals, Statistical physics

Publications

Journal Paper

  • Learning Point Processes and Convolutional Neural Networks for object detection in satellite images, J. Mabon, M. Ortner and J. Zerubia, MDPI Remote Sensing, 2024

Conference Papers

Full list on HAL

  • Improving Gradient Flow Methods for Instance Segmentation of Crossing Objects, J. Mabon, J.-C. Olivo-Marin, IEEE ISBI, Apr 2026, London, UK, Proceedings to be published
  • Apprentissage contrastif de modèles de processus ponctuels pour la détection d’objets, J. Mabon, M. Ortner and J. Zerubia, GRETSI, Aug 2023, Grenoble, France