Joel A. Tropp
Steele Family Professor of Applied and Computational Mathematics
Research interests: mathematics of data science, machine learning, numerical linear algebra, optimization, random matrix theory
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.