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Grady Wright

Grady Wright portrait

Office: MB 140-A
(208) 426-4674

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I received a Ph.D. in Applied Mathematics from the University of Colorado at Boulder in 2003 and then spent the next four years as a National Science Foundation (NSF) Postdoctoral Fellow at the University of Utah before joining the Department of Mathematics at Boise State. I have also worked as a software engineer in industry, and held visiting research positions at the National Center for Atmospheric Research and the Mathematical Institute at the University of Oxford. I currently serve as President of the SIAM Pacific Northwest (PNW) section.

My research interests are in computational and applied mathematics, with a specific focus the areas of approximation theory, radial basis functions (RBFs), spectral and high-order finite differences, partial differential equations (PDEs) on surfaces, coupled bulk-surface biomechanical problems, iterative methods for linear systems, scientific computing, and numerical software development.

Computational and applied mathematics at Boise State University

Selected products

  • K. P. Drake and G. B. Wright. A stable algorithm for divergence-free radial basis functions in the flat limit. J. Comput. Phys., 417 (2020).
  • K. P. Drake and G. B. Wright. A Fast and Accurate Algorithm for Spherical Harmonic Analysis on HEALPix Grids with Applications to the Cosmic Microwave Background Radiation. J. Comput. Phys., 416 (2020).
  • V. Shankar and G. B. Wright. Mesh-free semi-Lagrangian methods for transport on a sphere using radial basis functions. J. Comput. Phys., 366 (2018), 170-190.
  • H. Wilber, A. Townsend, and G. B. Wright. Computing with functions in spherical and polar geometries II. The disk. SIAM. J. Sci. Comput., 39-4 (2017), C238-C262.
  • E. J. Fuselier and G. B. Wright. A radial basis function method for computing Helmholtz-Hodge decompositions. IMA J. Numer. Anal., 37-2 (2017), 774-797.

Publications on Google Scholar

Selected courses taught

  • MATH 566 – Numerical Linear Algebra
  • MATH 497/597 – Special topics: Matrix Methods in Data Science
  • MATH 567 – Numerical Methods for Differential Equations
  • MATH 365 – Introduction to Computational Mathematics
  • MATH 426/526 – Complex variables