I am currently a PhD candidate at LAMSADE, a joint research lab of Université Paris-Dauphine and Université PSL, specializing in Machine Learning. My initial research was centered around Adversarial Attacks generated using Invertible Neural Networks. Recently, I have expanded my research interests to delve into the expressivity of generative models.
My current work involves both theoretical and experimental exploration of the learning abilities of discriminator-based generative models, specifically examining diversity and sample quality. I have successfully developed GANs and Normalizing Flows, striking a balance between Recall and Precision, tailored to user-specific requirements
Download my resumé or my academic resumé.
PhD in Artificial Intelligence, 2023
Université Paris-Dauphine
Multidisciplinary Master's Year in Quantitative Economics, 2020
Université Paris-Dauphine
MVA - MEng in Computer Science, 2019
École Normale Supérieure Paris-Saclay
Multidisciplinary Master's Year in Fundamental Physics, 2018
Université Paris-Sud
BSc in Electrical Engineering, 2017
École Normale Supérieure Paris-Saclay
Course | Code | Level | Teaching | Year |
---|---|---|---|---|
Hands-On IA | EM | We Are | Lectures | 2024 |
Introduction to Deep Learning | DL | E.M. | Lectures | 2023-2024 |
Course | Code | Level | Teaching | Year |
---|---|---|---|---|
Deep Learning Project | DLP | M.S. | Lectures | 2022-2023 |
Mathematics for Data Science | MSD | M.S. | Lectures | 2020-2023 |
Projet IA | IA | E.M. | Lectures | 2022-2023 |
Artificial Intelligence | IA | M.S. | Seminars | 2021 |
Introduction to Normalizing Flow | A | M.S. | Lectures | 2021 |
Information System Engineering | ISI1 | B.S. | Lectures/Seminars | 2020 |