Joel A. Tropp
Steele Family Professor of Applied and Computational Mathematics
B.A., B.S., University of Texas (Austin), 1999; M.S., 2001; Ph.D., 2004. Assistant Professor, Caltech, 2007-12; Professor, 2012-17; Steele Family Professor, 2017-.
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.
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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