Laura is an applied mathematician in the Computation, Computers, Information, and Mathematics Center at Sandia National Laboratories. She is involved with the DAKOTA software project, and currently works in computational statistics. Laura's research interests include uncertainty and sensitivity analysis, optimization under uncertainty, and model calibration. Current research projects involve uncertainty quantification and calibration for engineering design problems. Laura has been investigating Bayesian methods to calibrate model parameters in a framework that explicitly accounts for model uncertainty as well as experimental uncertainty. She has also been working on epistemic uncertainty approaches, primarily Dempster-Shafer theory of evidence to propagate epistemic uncertainty in computational models.
Laura spent many years working in applied reliability analysis. She developed an optimization algorithm for reliability improvement that was incorporated in Sandia's Reliability Modeling and Analysis Program, WinR. She analyzed a variety of diagnostic and experimental systems. Laura was PI for a project involving vulnerability analysis of computer networks by generating attack graphs. She led a research effort developing prognostic algorithms for health monitoring and predictive maintenance applications, with a specific focus on the F-16 Accessory Drive Gearbox.
received her B.S. in Applied Mathematics from