Joel A. Tropp
Steele Family Professor of Applied and Computational Mathematics; Graduate Option Representative for Computing and Mathematical Sciences
Research interests: mathematics of data science, machine learning, numerical linear algebra, optimization, random matrix theory
Overview
Joel Tropp's research lies at the interface of applied mathematics, electrical engineering, computer science, and statistics. His work focuses on developing practical, rigorously justified algorithms for solving core computational problems in linear algebra, numerical analysis, and optimization. He also develops user-friendly theoretical tools for high-dimensional probability and matrix analysis. Some of his best known contributions include matching pursuit algorithms, randomized SVD algorithms, matrix concentration inequalities, and statistical phase transitions.
Related News
Read more newsPublications
- Kireeva, Anastasia;Tropp, Joel A. (2024) Randomized matrix computations: themes and variations
- Chen, Chi-Fang;Dalzell, Alexander M. et al. (2024) Sparse Random Hamiltonians Are Quantumly EasyPhysical Review X
- Tropp, Joel A. (2022) ACM 217: Probability in High Dimensions
- Tropp, Joel A. (2022) ACM 204: Matrix Analysis
- Tropp, Joel A. (2022) ACM 204: Lectures on Convex Geometry
- Tropp, Joel A. (2022) Randomized block Krylov methods for approximating extreme eigenvaluesNumerische Mathematik
- Nakatasukasa, Yuji;Tropp, Joel A. (2021) Fast & accurate randomized algorithms for linear systems and eigenvalue problems
- Ding, Lijun;Yurtsever, Alp et al. (2021) An Optimal-Storage Approach to Semidefinite Programming Using Approximate ComplementaritySIAM Journal of Optimization
- Levis, Aviad;Lee, Daeyoung et al. (2021) Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements
- Huang, De;Niles-Weed, Jonathan et al. (2021) Matrix Concentration for ProductsFoundations of Computational Mathematics
Related Courses
2023-24
CMS/ACM 117 – Probability Theory and Computational Mathematics
ACM 217 – Advanced Topics in Probability
2022-23
CMS/ACM 117 – Probability Theory and Stochastic Processes
ACM/IDS 204 – Topics in Linear Algebra and Convexity
ACM 206 – Topics in Computational Mathematics
ACM 217 – Advanced Topics in Probability
2021-22
CMS/ACM 117 – Probability Theory and Stochastic Processes
ACM/IDS 204 – Topics in Linear Algebra and Convexity
2020-21
CMS/ACM 117 – Probability Theory and Stochastic Processes
ACM 217 – Advanced Topics in Stochastic Analysis