More Information

About Me

I am a graduate student in the Mathematics, Vision, and Learning (MVA) Master’s program at ENS Paris-Saclay, and I am also pursuing an engineering degree at École des Ponts ParisTech.

My coursework at the MVA master’s is focused on the following topics:

• Generative Models: Three courses at MVA on generative models, plus one online from MIT (not enrolled). I highly recommend the “Génération de données en IA par transport et débruitage” by Stéphane Mallat at the Collège de France (or the MIT’s course if you prefeer english).
• Deep Learning and its best practices
• Computer Vision courses, delving into the latest research papers and implementing state-of-the-art models and common problems in computer vision.
• 3D: 3D computer vision, 3d geometry and geometric deep learning
• Inverse problems in computer vision: One specific about image denoising, another about numerical imagery and inverse problems. One course focused on restauration and synthesis of images
• Graph Machine Learning course, during which I learned mathematical tools for graphs and graph neural networks
• Representation Learning is one of the most interesting courses I had, although not directly connected to my research interests
• Bayesian Machine Learning is also an interesting course that gave me a global view of the current limitations of Bayesian approaches and the research landscape, as well as giving me a high-level overview of the main approaches to making decisions under uncertainty
• Learning for time series equipped me with the tools needed to tackle time series forecasting, extract meaningful patterns, enhance the data and apply change-point and anomaly detection

And here is my coursework at the ENPC:

• Spanish & English courses
• Optimization methods, including convex/non convex optimization, linear programming and dynamic programming
• Optimal Control, teached by Pf. Amaury Hayat
• Computer Vision course that introduced me classical computer vision techniques, teached by Vincent Lepetit
• Machine Learning theoretical and practical (JAX) course
• Stochastic Processes (MDPs, Martingales)
• Statistics in high dimensions
• Deep Learning, teached by Mathieu Aubry
• Data Science
• Advanced Algorithmics (C++)
• Scientific Computational Algorithms (Julia)
• Numerical Analysis / Calculus / Probabilities courses

But also …

• Economics
• Mechanics, teached by Luc Dormieux
• Geomechanics
• Statistical Physics
• Epistemology
• Sustainable Development
• Law

Open to Research Opportunities

I’m currently looking for a 6-month internship, starting October 2025, in Generative Models.

This will be the final internship of my studies, and it can potentially be continued as a PhD or a job.

You can find more information in my CV, explore my portfolio, or simply get in touch.