Rate My Professor Karen Willcox

Willcox's research develops scalable computational methods for design of next-generation engineered systems, focusing on reduced-order modeling for physics-based approximations from data, multifidelity formulations for decision-making and uncertainty quantification, and scalable predictive digital twins. Her model reduction and multifidelity methods are widely applied and incorporated into codes used by Boeing for Blended-Wing-Body aircraft design, Lockheed Martin for aerostructural analysis, Air Force Research Laboratory for combustion simulations in rocket engine design, and NASA Goddard Space Flight Center for James Webb Space Telescope design. Key publications include "Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas" (2023), "Multi-output multilevel best linear unbiased estimators via semidefinite programming" (2023), "Operator inference for non-intrusive model reduction with nonlinear manifolds" (2022), "Reduced operator inference for nonlinear partial differential equations" (2022), and "A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale" (2022). Her honors include election to the National Academy of Engineering (2022), 2024 Theodore von Kármán Prize, 2024 JSCES Grand Prize, 2023 USACM J.T. Oden Medal, Member of the New Zealand Order of Merit (MNZM) (2017), and Fellow of SIAM, AIAA, and USACM. She is section editor of SIAM Journal on Scientific Computing.