Selected publications

• K. Koval, A. Alexanderian, G. Stadler, Optimal experimental design under irreducible uncertainty for inverse problems governed by PDEs, arXiv, 2019

• S. Wahal, G. Biros, BIMC: The Bayesian Inverse Monte Carlo method for goal-oriented uncertainty quantification. Part I, arXiv, 2019

• C. Dwelle, J. Kim, K. Sargsyan, V. Ivanov, *Streamflow, stomata, and soil pits: Sources of inference for complex models with fast, robust uncertainty quantification, In: Advances in Water Resources, 125, 2019

• S. Kramer, A. Jones, A. Mostafa, et al. The third Sandia fracture challenge: predictions of ductile fracture in additively manufactured metal, Int J Fract 218, 5–61, 2019

• S. Lan, Adaptive dimension reduction to accelerate infinite-dimensional geometric Markov Chain Monte Carlo, Journal of Computational Physics, 392:71-95, 2019

• U. Villa, N. Petra, O. Ghattas, hIPPYlib: An extensible software framework for large-scale inverse problems; Part I: Deterministic inversion and linearized Bayesian inference, arXiv, 2019

• P. Chen, U. Villa, O. Ghattas, Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty, Journal of Computational Physics, 385:163--186, 2019

• M. Parno, D. O'Connor, M. Smith, High dimensional inference for the structural health monitoring of lock gates, arXiv, 2018

• B. Crestel, G. Stadler and O. Ghattas, A comparative study of structural similarity and regularization for joint inverse problems governed by PDEs, Inverse Problems, 35:024003, 2018

• A. Attia, A. Alexanderian, A. K. Saibaba, Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems, Inverse Problems, 34:095009, 2018

• R. Nicholson, N. Petra and Jari P Kaipio, Estimation of the Robin coefficient field in a Poisson problem with uncertain conductivity field, Inverse Problems, Volume 34, Number 11, 2018

• P. Chen, K. Wu, J. Chen, T. O'Leary-Roseberry, O. Ghattas, Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions, arXiv, 2018

• E. M. Constantinescu, N. Petra, J. Bessac, C. G. Petra, Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms, arXiv, 2018

• U. Villa, N. Petra, O. Ghattas, hIPPYlib: An extensible software framework for large-scale inverse problems, Journal of Open Source Software (JOSS), 3(30):940, 2018

• P. Chen, U. Villa, O. Ghattas, Taylor approximation for PDE-constrained optimization under uncertainty: Application to turbulent jet flow, Proceedings in Applied Mathematics and Mechanics - 89th GAMM Annual Meeting, 18:e201800466, 2018

• M. Parno, Y. Marzouk, Transport Map Accelerated Markov Chain Monte Carlo, SIAM/ASA J. Uncertainty Quantification, 6(2), 645–682, 2018

• P. Conrad, A. Davis, Y. Marzouk, N. S. Pillai, A. Smith, Parallel Local Approximation MCMC for Expensive Models, SIAM/ASA J. Uncertainty Quantification, 6(1), 339–373, 2018

• P. Chen, U. Villa, O. Ghattas, Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems, Computer Methods in Applied Mechanics and Engineering, 327:147-172, 2017

• M. Parno, T. Moselhy, Y. Marzouk, A Multiscale Strategy for Bayesian Inference Using Transport Maps, SIAM/ASA J. Uncertainty Quantification, 4(1), 1160–1190, 2016

• Y. Marzouk, T. Moselhy, M. Parno, A. Spantini, Sampling via Measure Transport: An Introduction, In: Ghanem R., Higdon D., Owhadi H. (eds) Handbook of Uncertainty Quantification. Springer, 2016

• P. Conrad, Y. Marzouk, N. S. Pillai, A. Smith, Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations, J. American Statistical Association, 111(516), 1591-1607, 2015

• P. Conrad, Y. Marzouk, Adaptive Smolyak Pseudospectral Approximations, SIAM J. Sci. Comput., 35(6), A2643–A2670, 2013

Selected Ph.D. thesis

• S. Fatehiboroujeni, Inverse Approaches for Identification of Constitutive Laws of Slender Structures Motivated by Application to Biological Filaments, University of California, Merced, 2018. Adviser S. Goyal

• A. Davis, Prediction under uncertainty: from models for marine-terminating glaciers to Bayesian computation, MIT, Boston, 2015. Adviser Y. Marzouk

• K. A. McCormack, Earthquakes, groundwater and surface deformation: exploring the poroelastic response to megathrust earthquakes, The University of Texas at Austin, 2018. Adviser M. Hesse

• B. Crestel, Advanced techniques for multi-source, multi-parameter, and multi-physics inverse problems, The University of Texas at Austin, 2017. Adviser O. Ghattas

• M. Parno, Transport maps for accelerated Bayesian computation, MIT, Boston, 2015. Adviser Y. Marzouk