Stochastic optimization in deep learning Large-deviation analysis of SGD invariant measures and global convergence times. Internal mechanisms of large language models Understanding uncertainty estimates and in-context learning through targeted experiments. Wasserstein distributionally robust optimization Regularization schemes and generalization guarantees for Wasserstein DRO models. Last-iterate convergence of mirror methods Determining how Bregman geometry impacts last-iterate guarantees in variational inequalities. Expressive power of invariant and equivariant GNNs Precise characterization of what symmetry-aware graph networks can represent. Smooth game optimization for machine learning Unified analyses and accelerated methods for differentiable games.