Topic varies by year. 2023-24: Random matrix theory. This class introduces some fundamental random matrix models with applications in computational mathematics, statistics, signal processing, algorithms, and other areas. The focus is on finite-dimensional examples and comparisons with ideal models. Specific topics may include the independent sum model, matrix concentration inequalities, geometric random matrix theory, classical ensembles and their limiting spectral properties, universality laws, and free probability. Lectures and homework will require the ability to understand and produce mathematical proofs. Not offered 2024-25.