Alessandro Favero


Ciao 👋 I’m Alessandro, a PhD candidate in the physics program at EPFL. I am affiliated with the Physics of Complex Systems Lab and the Signal Processing Lab, where I am co-advised by Matthieu Wyart and Pascal Frossard. Currently, I am on leave at Amazon Science in the AWS AI Labs located in the Bay Area.

My research revolves around foundational questions in modern machine learning. In particular, I am interested in understanding how the geometric structure of natural data – including symmetries, locality, and compositionality – enables efficient learning in high dimensions. To achieve this, I employ different methods, ranging from studying analytical models to conducting large-scale numerical experiments.

Previously, I completed a joint Master’s degree in theoretical physics at Sorbonne Université, Politecnico di Torino, SISSA, and ICTP.


Jul 2023 This summer, I’m an applied scientist intern at Amazon Science in the AWS AI Labs located in the San Francisco Bay Area!
Mar 2023 I’m giving a talk in the Statistical Physics Meets Machine Learning session of the APS March Meeting in Las Vegas! After, I’m visiting MIT, NYU, and Simons Foundation.
Nov 2022 Our 2021 NeurIPS papers on locality and stability to diffeomorphisms have been published in JSTAT as part of the Special Issue on the Statistical Physics Aspects of Machine Learning and AI.
Jun 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 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.