Topic varies by year. 2026-27: High-dimensional probability. This class treats probabilistic and geometric phenomena that occur in high-dimensional linear spaces. These insights have applications in machine learning, statistics, algorithms, numerical analysis, and related fields. Specific topics may include concentration inequalities, suprema of random processes, random matrix theory, phase transition phenomena, and universality laws. This course requires the ability to understand and produce mathematical proofs.