Currently seeking an internship in generative modeling for biology and/or biomedical discovery for my Master's thesis (April–October 2026).

I'm a Master's student at the intersection of computational biology and machine learning, specializing in generative models for biological systems.

Currently following the MVA Master program at ENS Paris-Saclay & Paris-Cité University where I'm focusing on the mathematics behind ML and reinforcing my knowledge and skills to prepare for a future PhD in AI for Biology and/or Biomedical discovery.

Here are the classes I took for the first semester (Fall 2025):

• Optimal Transport by Gabriel Peyré & Julie Delon
• Computational Statistics by Stéphanie Allassonière
• Geometric Data Analysis by Jean Feydy
• Introduction to Probabilistic Graphical Models and deep generative models by Pierre Latouche & Pierre-Alexandre Mattei
• ALTEGRAD (NLP and Graphs) by Michalis Vazirgiannis

Spring 2026 courses I will take:

• Stochastic Calculus for Generative Modeling by Alain Durmus
• Algorithms and Learning for Protein Science by Frédéric Cazals
• Reinforcement Learning by D. Basu, E. Kaufmann & O. Maillard
• Graphs in Machine Learning by Michal Valko
• Generative Models for Imaging by B. Galerne & A. Leclaire
• Representation Learning for Computer Vision and Medical Imaging by Pietro Gori & Loïc Le Folgoc

I previously completed an MSc in Artificial Intelligence and Computational Biology at Paris-Saclay University and AgroParisTech.

I am constantly trying to improve my data science and programming skills which makes me eager to contribute to impactful and ground-breaking projects involving AI and computational biology.

I am fascinated by these research fields (lot of work ahead to truly get there though 🤓):

• Single-cell & spatial biology: Latent diffusion models (e.g scLDM, SquiDiff), perturbation prediction (e.g, STATE), generative models for cellular dynamics
• Foundation models for biology: Protein language models (e.g, BoltzGen), multi-omic integration, virtual cell systems
• Geometric & generative ML: Optimal transport (e.g, GWOT, GrALe), diffusion models, representation learning for biological data
• AI for precision medicine: Multi-modal integration for patient stratification and biomarker discovery

• Studied Hyperspherical Variational Auto-Encoders -> Our repo
• Studied scVAE -> Our repo
• Studied Cross-Modality Matching with GWOT

• Got accepted into ENS Paris-Saclay's MVA coursework (really excited)
• Laureate of the PR[AI]RIE Institute Excellence Scholarship (huge support throughout the year)
• Published in the Journal Artificial Intelligence in the Life Sciences : A machine learning framework for the prediction and analysis of bacterial antagonism in biofilms using morphological descriptors
• Got an internship at Dassault Systèmes, the topic ? Explainable clustering for clinical data
• Our main project of the year during IODAA got accepted for presentation at the 1st AI for Animal Science Conference at ETH Zurich (2025)
• With a few friends from AgroParisTech, we won the AI Methodology Award at the 2025 Owkin & Servier AI Hackathon for Glioblastoma Research
• Gave a presentation at the Micalis Institute on the work I've done on the biofilm antagonism prediction model