Center for Computing Research (CCR)

Center for Computing Research

Drew Philip Kouri

Drew Philip Kouri
Optimization & Uncertainty Quantification
Phone: 505/845-8127
Fax: 630/252-5986

Mailing address:
Sandia National Laboratories
P.O. Box 5800, MS 1320
Albuquerque, NM

Drew's research interests include: PDE-constrained optimization, algorithms for solving risk-averse and robust PDE-constrained optimization problems, adaptive sampling and quadrature methods for risk-averse optimization, general frameworks to handle inexactness and model adaptivity in optimization.  Drew is also a lead developer of the Rapid Optimization Library (ROL) which is a software package for matrix-free, derivative-based optimization.


Drew earned his B.S. and M.S. degrees in Mathematics from Case Western Reserve University in 2008, including a minor in Spanish.  In 2010, he earned his M.A. in Computational and Applied Mathematics from Rice University.  Under the supervision of M. Heinkenschloss, he earned his Ph.D. in Computational and Applied Mathematics from Rice University in 2012 with dissertation "An Approach for the Adaptive Solution of Optimization Problems Governed by Partial Differential Equations with Uncertain Coefficients."  After obtaining his Ph.D., Drew served as the J.H. Wilkinson Fellow at Argonne National Laboratory before joining Sandia National Laboratories in 2013.


Selected Publications & Presentations

  • Kouri, Drew Philip, Thomas M. Surowiec, "A primal-dual algorithm for risk minimization," Journal Article, Optimization Online, Accepted/Published November 2020.
  • Kouri, Drew Philip, Denis Ridzal, Raymond S. Tuminaro, "KKT Preconditioners for PDE-Constrained Optimization with the Helmholtz Equation," Journal Article, SIAM SISC, Submitted June 2020.
  • Kouri, Drew Philip, Thomas M. Surowiec, "Risk-averse optimal control of semilinear PDEs," Journal Article, ESAIM: Control, Optimisation and Calculus of Variations, Vol. 26, No. 53, Accepted/Published September 2020.

Awards & Recognition

  • Kouri, Drew Philip, Award, Best Paper of 2019, Optimization Letters, November 25, 2020.