The goal of my research is to advance the understanding of modern machine learning. I primarily focus on statistical aspects of deep learning and the interplay between data structure and generalization. In particular, I’m interested in elucidating what structures in real data allow for efficient learning in high-dimensions.
I enjoy working both studying analytical models and performing numerical experiments.
Before, I completed a joint Master’s degree in theoretical physics at Sorbonne Université, Politecnico di Torino, SISSA, and ICTP.
|Aug 2, 2022||A new preprint is out! We show how the multi-scale structure of deep CNNs reflects into the spectral structure of their NTK allowing them to adapt to the spatial scale of the task!|
|Jun 13, 2022||This summer, I’m attending the Machine Learning Theory Summer School at Princeton University and the Summer School on Statistical Physics and Machine Learning at Les Houches School of Physics|
|Apr 6, 2022||I’m giving a lightning talk on the locality prior of convolutional networks at the Workshop on the Theory of Overparameterized Machine Learning organised by Rice University|