About Me
I’m a PhD Student at Université Lyon 1 (Biometry and Evolutionary Biology Lab, UMR 5558). I have a background in applied mathematics, statistics, machine learning and computer vision.
In my PhD, I develop and implement new methods for the automated analysis of camera trap images, leveraging deep learning to support ecological research. I’m an active member of the DeepFaune project, where I contribute to model training, data curation, and software development.
Outside of research, I’m passionate about wildlife photography and naturalist activities.
Interests
- Computer Vision
- Ecology
- Medical Imaging
- Artificial Intelligence
Education
M.Sc. MVA (Mathematics, Vision, Learning)
ENS Paris-Saclay
Engineering Degree in Applied Mathematics
ENSTA Paris
CPGE MPSI - MP*
Lycée Pierre de Fermat, Toulouse
Experience
PhD Student
University Lyon 1 - LBBE- Create new deep learning techniques for automatic species classification in camera trap images: score calibration, behavior prediction and leveraging sequence context
- Contribute to the improvement and development of the DeepFaune software.
- PhD supervisors: S. Dray, V. Miele and S. Chamaillé-Jammes
Research Engineer
CNRS - CREATIS- Develop a predictive model of consciousness state and 6-month outcomes for coma patients using multiple medical imaging modalities.
- Utilized advanced AI methods, including transformers and self-supervised learning.
Research Internship
University Lyon 1 - CREATIS- Develop a model for segmentation and characterization of prostate cancer in multiparametric MRI using weak annotations (point-based labels).
- Investigated non-standard loss functions: modeling inter-class correlations and incorporating constraints on lesion size.
Education
M.Sc. MVA (Mathematics, Vision, Learning)
ENS Paris-SaclayEngineering Degree in Applied Mathematics
ENSTA ParisCPGE MPSI - MP*
Lycée Pierre de Fermat, Toulouse
Featured Publications
Recent Publications
(2025).
Zero-shot animal behaviour classification with vision-language foundation models.
Methods in Ecology and Evolution.
(2024).
Being confident in confidence scores: calibration in deep learning models for camera trap image sequences.
Remote Sens Ecol Conserv.
(2023).
The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images.
European Journal of Wildlife Research.
(2022).
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans.
Medical Image Analysis.
Recent Projects
Recent & Upcoming Talks
