Joel A. Tropp
Steele Family Professor of Applied and Computational Mathematics; Graduate Option Representative for Computing and Mathematical Sciences
algorithms, numerical analysis, statistics, random matrix theory
Overview
Joel Tropp's work lies at the interface of applied mathematics, electrical engineering, computer science, and statistics. This research concerns the theoretical and computational aspects of data analysis, sparse modeling, randomized linear algebra, and random matrix theory.
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2022-23
CMS/ACM 117 – Probability Theory and Stochastic Processes
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