CV

Postdoctoral Researcher in Deep Learning

IDS (Télécom Paris) & MLIA (Sorbonne University)

Disentangled and Controllable Latent Representations for Computer Vision and Medical Imaging. Under the supervision of Pietro Gori and Dr. Alasdair Newson.

• Developping structured latent representations for medical imaging, focusing on disentangling healthy and pathological brain patterns in neuroimaging.
• Adapting contrastive learning to enhance interpretability in psychiatric disorder diagnostics.
• Investigating diffusion models, optimizing their latent space structure for improved synthesis and analysis of neuroimaging data.

PhD in Deep Learning

CEA LIST

Refining Machine Learning Evaluation: Statistical Insights into Model Performance and Fairness. Under the direction of Betrand Delezoide and Adrian Popescu.

• Developed and integrated statistical methodologies to improve the robustness and interpretabilityy of deep learning model evaluations in 3 fields
• Studied the impact of pre-training and architectures for Class Incremental Learning (CIL), identifying key factors affecting performance, with results published in WACV 2024.
• Investigated biases in Face Recognition systems, quantifying demographic disparities, with findings under review. Showed how using conditioned generation can reduce biases, accepted in WACV 2025.
• Developed novel information metrics to characterize difficult users for Recommender systems, and studied their impact on performance. Work under review.

Research Intern in Deep Learning

CEA LIST / Valeo

Self-supervised depth prediction from monocular images. Goal: perform depth prediction for autonomous driving. Under the supervision of Florian Chabot.

• Developed and integrated statistical methodologies to improve the robustness and interpretabilityy of deep learning model evaluations in 3 fields
• Crafted a Vision Transformer U-Net pipeline for Depth prediction.
• Adapted the model to a self-supervised setup.

Research Intern in Statistics

CREST (ENSAE Paris)

Investigated variable selection in high-dimensional data, using Sequential Monte-Carlo methods. Under the supervision of Nicolas Chopin.

PhD

Unversité Paris-Saclay and CEA List

Deep Learning

MSc

ENS Paris-Saclay

MVA (Mathematics, Vision, and Learning)

• Deep Learning (V. Lepetit)
• Object Recognition and Computer Vision (I. Laptev)
• Deep Learning in Practice (G. Charpiat)
• Image denoising : the human machine competition (J.M. Morel)
• Graphs in Machine Learning(M. Valko)
• Geometrical Deep Learning (J. Feydy)
• Geometric Approaches in Statistical Learning: The Example of Longitudinal Data (S. Durrleman)

MSc

ENSAE Paris

Statistics

• Statistics 1 & 2
• Deep Learning
• NLP
• Reinforcement Learning
• Optimization
• Time Series
• Bayesian Statistics
• Econometrics

Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

Develops fairness-aware analysis and face generation techniques to mitigate demographic biases in face verification models.

An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

Investigates the impact of initial training strategies on exemplar-free class-incremental learning, highlighting key factors affecting model adaptation over time.

Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis

arXiv preprint

Proposes a framework to measure user coherence in cross-domain recommendation, providing insights into user behavior consistency across different domains.

Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

Introduces Vis2Rec, a large-scale dataset for visit recommendation based on visual data, addressing challenges in recommendation systems with multimodal information.

NLP and Political Programs – Applied Statistics Project (ENSAE)

Used Natural Language Processing (NLP) techniques to analyze the representation of French political programs in the media, assessing their visibility and narrative influence.

• Topic Modeling with LDA
• Text Embeddings using Word2Vec and TF-IDF
• Advanced Techniques: LSTM and Transformers (BERT)