I work on the theoretical foundations of machine learning and stochastic optimization. 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.