Dr. Hillary Fairbanks is a staff member in the Center for Applied Scientific Computing (CASC) at LLNL working in the Uncertainty Quantification and Optimization group. Her research interests include uncertainty quantification for large-scale applications, multilevel Monte Carlo, multi-fidelity methods, low-rank matrix approximations, Bayesian inference, and acceleration approaches for Markov chain Monte Carlo. In addition to her work on developing scalable multilevel sampling approaches for partial differential equations with random coefficients, Dr. Fairbanks leads the "Foundations for Next-Generation Decisions support" thrust of a DOE ASCR Applied Math Program project at LLNL. Dr. Fairbanks also works with LLNL programs where she performs uncertainty analysis and Bayesian model calibration for multiple engineering applications.
 
Dr. Fairbanks completed her postdoc at LLNL working with Dr. Panayot Vassilevski developing multilevel approaches for large-scale uncertainty quantification. In 2018 she completed her PhD in Applied Mathematics, with the dissertation "Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems", from University of Colorado Boulder, where she was advised by Prof. Alireza Doostan.
 

Fairbanks HR, Kalchev DZ, Lee CS, Vassilevski PS. Scalable Multilevel Monte Carlo Methods Exploiting Parallel Redistribution on Coarse Levels. arXiv preprint arXiv:2408.02241. 2024 Aug 5.

Reddy S, Fairbanks H. Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models. arXiv preprint arXiv:2405.11179. 2024 May 18.

Fairbanks HR, Villa U, Vassilevski PS. Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo. SIAM Journal on Scientific Computing. 2021;43(5):S293-316.

Fairbanks HR, Osborn S, Vassilevski PS. Estimating posterior quantity of interest expectations in a multilevel scalable framework. Numerical Linear Algebra with Applications. 2021 May;28(3):e2352.

Fairbanks HR, Jofre L, Geraci G, Iaccarino G, Doostan A. Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence. Journal of Computational Physics. 2020 Feb 1;402:108996.

Hampton J, Fairbanks HR, Narayan A, Doostan A. Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction. Journal of Computational Physics. 2018 Sep 1;368:315-32.

Fairbanks HR, Doostan A, Ketelsen C, Iaccarino G. A low-rank control variate for multilevel Monte Carlo simulation of high-dimensional uncertain systems. Journal of Computational Physics. 2017 Jul 15;341:121-39.