This page highlights a selection of my research and applied machine learning projects carried out during the Master MVA (Mathematics, Vision, Learning) at ENS Paris-Saclay, and subsequent independent work.
Each project blends rigorous mathematical modeling with practical implementations in deep learning, probabilistic reasoning, and optimal transport.
Together, they form a coherent research trajectory oriented toward generative modeling, learning theory, and robust machine intelligence.

Topics explored include:

• Optimal Transport & Schrödinger Bridges for generative dynamics
• Graph Neural Networks and latent diffusion models
• Audio and Signal Processing for end-to-end waveform modeling
• Probabilistic and Topological Learning for interpretability and structure inference
• Computational Statistics and theoretical optimization methods