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Joel Tropp is the Steele Family Professor of Applied and Computational Mathematics at the California Institute of Technology. He has made significant contributions in applied mathematics, machine learning, data science, numerical algorithms, and random matrix theory. Some of his most notable work includes matching pursuit algorithms, randomized singular value decomposition algorithms, matrix concentration inequalities, and insights into statistical phase transitions. Tropp earned his Ph.D. in Computational Applied Mathematics in 2004, joining Caltech in 2007. His research has garnered him recognition as a Highly Cited Researcher from 2014 to 2018 and he is a co-founder of the SIAM Journal on Mathematics of Data Science, serving as co-chair for the inaugural SIAM Conference on Mathematics of Data Science in 2020. He has also been elected as a fellow of several esteemed organizations including SIAM, IEEE, IMS, and AMS. In 2025, he received the Richard P. Feynman Prize for Excellence in Teaching at Caltech and is an invited speaker for the 2026 International Congress of Mathematicians.
California Institute of Technology • Pasadena, CA
Professor in the field of Applied and Computational Mathematics.
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