Research Interests: Interior-point methods for linear, convex, nonconvex, semidefinite optimization, robust optimization, the parametric simplex method, and constraint matrix sparsification (e.g. fast Fourier optimization). Applications include high-contrast imaging (to design a NASA space telescope), finding new stable periodic solutions to the n-body problem, Machine Learning, portfolio selection, option pricing, and least-absolute-deviation statistics. Related side activities include algorithms to address grade inflation, quantifying climate change, and making Purple America maps.