Tropp, Joel A.

Advisor — 7 thesises

• McCoy, Michael Brian — A Geometric Analysis of Convex Demixing (2013, Dissertation (Ph.D.))
• Levin, Eitan — Any-Dimensional Data Science: Learning, Optimization, and Sampling (2026, Dissertation (Ph.D.))
• Chen, Yuhua Richard — Concentration Inequalities of Random Matrices and Solving Ptychography with a Convex Relaxation (2017, Dissertation (Ph.D.))
• Epperly, Ethan Nicholas — Make the Most of What You Have: Resource-Efficient Randomized Algorithms for Matrix Computations (2025, Dissertation (Ph.D.))
• Bruer, John Jacob — Recovering Structured Low-rank Operators Using Nuclear Norms (2017, Dissertation (Ph.D.))
• Moreno Ferreira, Elvira — Scalable Approximation through Structure: Spectral Methods for Polynomial Optimization and Adaptive Sampling in Kernel Quadrature (2026, Dissertation (Ph.D.))
• Gittens, Alex A. — Topics in Randomized Numerical Linear Algebra (2013, Dissertation (Ph.D.))