I work on the theoretical foundations of machine learning.
Currently, my research focuses on the role of symmetry and geometry in learning dynamics and model generalization. Using methods from statistical physics, I study gradient-based learning algorithms and investigate how they select specific predictors among many equivalent parameterizations of the same model.
I am interested in discussions and collaborations on theoretical machine learning, statistical physics, and geometric approaches to learning.