| Dec 15, 2025 | Presented our work on the lon-run behaviour of SGD on non-convex landscapes to the Inria Argo team in Paris (slides). |
| Dec 10, 2025 | Delivered an invited seminar on stochastic optimization in deep learning at Morgan Stanley Machine Learning Research, New York (slides). |
| Oct 14, 2025 | Wrapped up my internship at Morgan Stanley ML Research, New York, where I investigated in-context learning capabilities of LLMs, see our preprint. |
| May 31, 2025 | Completed my PhD internship at Apple Machine Learning Research in Paris, working on uncertainty quantification methods for Large Language Models in Marco Cuturi’s team, see our prepint. |
| Apr 20, 2025 | Our paper “The global convergence time of stochastic gradient descent in non-convex landscapes” was accepted at ICML 2025! Preprint available. |
| Jan 15, 2025 | Released our paper “How does the pretraining distribution shape in-context learning?” - joint work with Ali Hasan from our Morgan Stanley collaboration. Available on arXiv. |
| Dec 15, 2024 | Presented “Invariant measures and SGD asymptotics” at the Séminaire de Probabilités et Statistiques of Nice University. Slides available. |
| Oct 25, 2024 | Released “skwdro: a library for Wasserstein distributionally robust machine learning” - collaborative work with Florian Vincent and my advisors. Available on arXiv. |
| Oct 22, 2024 | Gave a seminar on “Large deviation theory for SGD” at the Séminaire de Statistique of the LPSM lab in Paris. Slides here. |
| Jun 10, 2024 | Excited to share that our paper “What is the long-run distribution of stochastic gradient descent? A large deviations analysis” was accepted at ICML 2024! Paper on arXiv. |
| May 20, 2024 | Presented our deterministic analysis of mirror methods at the SMAI MODE Conference in Marseille. Slides available. |
| Apr 15, 2024 | Our work “The rate of convergence of Bregman proximal methods” was accepted for publication in SIAM Journal on Optimization. Preprint on arXiv. |