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Rachel Ward is the W.A. 'Tex' Moncrief Distinguished Professor in Computational Engineering Sciences specializing in Data Science at the University of Texas at Austin. Her research centers around mathematics applied to data, particularly in areas such as signal and image processing, dynamical systems, and mathematical biology. Ward's expertise includes sparse approximation, stochastic optimization, and numerical linear algebra. She previously served as a visiting research scientist at Facebook AI Research and was a Courant Instructor at New York University. Her significant contributions to mathematics have been recognized through various awards including the NSF CAREER Award and the Alfred P. Sloan Fellowship. Ward holds a Ph.D. in computational and applied mathematics from Princeton University where she completed her doctoral work in 2009. In her academic pursuits, she focuses on topics such as applied harmonic analysis and numerical analysis, and has published extensively in prestigious journals. Through her work, she aims to advance the understanding of complex mathematical concepts and their applications in computational science and artificial intelligence.
The University of Texas at Austin • Austin, TX
Distinguished Professor in Computational Engineering Sciences - Data Science.
Facebook AI Research •
Conducted research in artificial intelligence.
Courant Institute, NYU • New York, NY
Instructor and researcher in mathematical sciences.
General requirements for the Graduate School at UT Austin apply to all programs unless otherwise specified.