Rachel Ward (mathematician)
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Princeton University (PhD)
Rachel Ward | |
|---|---|
 Ward at Oberwolfach Research Institute for Mathematics in 2014 | |
| Born | Rachel Angela Ward |
| Education | University of Texas at Austin (BS) Princeton University (PhD) |
| Scientific career | |
| Institutions | University of Texas at Austin |
| Thesis | Freedom through Imperfection: Exploiting the flexibility offered by redundancy in signal processing |
| Academic advisors | Ingrid Daubechies |
Rachel Angela Ward is an American applied mathematician at the University of Texas at Austin. She is known for work on machine learning, optimization, and signal processing. At the University of Texas, she is W. A. "Tex" Moncrief Distinguished Professor in Computational Engineering and Sciences—Data Science, and professor of mathematics.[1]
Ward received her BS in mathematics from the University of Texas at Austin in 2005.[2] She earned her PhD in applied and computational mathematics from Princeton University in 2009. Her doctoral advisor was Ingrid Daubechies.[3]
Career
Ward was an instructor at the Courant Institute from 2009-2011[4] and then joined the faculty at the University of Texas at Austin in 2011.[4] In 2018, she was a Visiting Research Scientist at Facebook Artificial Intelligence Research[4] and in 2019 she was a Von Neumann Fellow at the Institute for Advanced Study.[5] She serves on the Scientific Advisory Board for the Institute for Computational and Experimental Research in Mathematics (ICERM).[6]
Research
Ward is known for her work across a wide variety of applied math areas including image processing, compressed sensing, and stochastic and adaptive gradient descent.[7] Ward also worked on a project funded by the Department of Defense, with faculty from UT Austin's College of Natural Sciences and Cockrell School, to develop unmanned aerial vehicles.[8]
