Ronald A. DeVore
Texas A&M University-College Station
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Primary Section: 32, Applied Mathematical Sciences Secondary Section: 11, Mathematics Membership Type:
Member
(elected 2017)
Photo Credit: Jim Lyle |
Biosketch
Ronald DeVore is a mathematician recognized for his work in applied mathematics, particularly those areas that interface numerical analysis, partial differential equations, data processing, machine learning and approximation of functions. DeVore was born and raised in Detroit, Michigan. He graduated from Eastern Michigan University in 1964 and then obtained a doctoral degree in mathematics from Ohio State University in 1967 under the supervision of Ranko Bojanic. From 1968 to 1977, DeVore was at Oakland University and in 1977 he became a professor at the University of South Carolina, Columbia, South Carolina, where he served as the Robert L. Sumwalt Professor from 1986-2005. From 1999-2005 he served as the director of the Industrial Mathematics Institute, of which he co-founded. It is now known as the Interdisciplinary Mathematics Institute. In 2005 DeVore retired from the University of South Carolina, as the Robert L. Sumwalt Distinguished Professor Emeritus. In 2008 he joined the faculty at Texas A&M University, as the Walter E. Koss Professor, and was named Distinguished Professor in 2010. DeVore is a member of the American Academy of Arts and Sciences, the National Academy of Sciences, and is a fellow of the American Mathematical Society.
Research Interests
Ronald DeVore is involved in a spectrum of applied areas of mathematics, however his core research is in approximation theory. He has applied methods of approximation to the development of numerical methods for partial differential equations, including a recent emphasis on families of parametric partial differential equations and related areas such as inverse problems and parameter estimation. DeVore has been a major contributor to signal and image processing including the areas of compressed sensing and data assimilation, and he was one of the original developers of wavelet compression methods. Two characteristics of his work are nonlinear methods and high dimensional problems. Additionally, he has been a major developer of adaptive methods for solving partial differential equations and greedy algorithms in signal processing.
