JHelioviewer: Visualizing Large Sets of Solar Images Using JPEG 2000. - Daniel Muller; Bernhard Fleck; George Dimitoglou; Benjamin W. Caplins; Desmond E. Amadigwe; Juan Pablo Garcia Ortiz; Benjamin Wamsler; Alen Alexanderian; V. Keith Hughitt; Jack Ireland (Comput. Sci. Eng., 2009)

Multiscale Stochastic Preconditioners in Non-intrusive Spectral Projection. - Alen Alexanderian; Olivier P. Le Maître; Habib N. Najm; Mohamed Iskandarani; Omar M. Knio (J. Sci. Comput., 2012)

Preconditioned Bayesian Regression for Stochastic Chemical Kinetics. - Alen Alexanderian; Francesco Rizzi; Muruhan Rathinam; Olivier P. Le Maître; Omar M. Knio (J. Sci. Comput., 2014)

A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized ℓ0-Sparsification. - Alen Alexanderian; Noemi Petra; Georg Stadler; Omar Ghattas (SIAM J. Sci. Comput., 2014)

A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems. - Alen Alexanderian; Noemi Petra; Georg Stadler; Omar Ghattas (SIAM J. Sci. Comput., 2016)

Stability of Nonlinear Convection-Diffusion-Reaction Systems in Discontinuous Galerkin Methods. - Craig Michoski; Alen Alexanderian; C. Paillet; Ethan J. Kubatko; Clint Dawson 001 (J. Sci. Comput., 2017)

Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations. - Alen Alexanderian; Noemi Petra; Georg Stadler; Omar Ghattas (SIAM/ASA J. Uncertain. Quantification, 2017)

Randomized matrix-free trace and log-determinant estimators. - Arvind K. Saibaba; Alen Alexanderian; Ilse C. F. Ipsen (Numerische Mathematik, 2017)

Efficient Computation of Sobol' Indices for Stochastic Models. - Joseph L. Hart; Alen Alexanderian; Pierre-Alain Gremaud (SIAM J. Sci. Comput., 2017)

Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems. - Ahmed Attia; Alen Alexanderian; Arvind K. Saibaba (CoRR, 2018)

Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems. - Alen Alexanderian; Arvind K. Saibaba (SIAM J. Sci. Comput., 2018)

Randomization and reweighted 𝓁1-minimization for A-optimal design of linear inverse problems. - Elizabeth Herman; Alen Alexanderian; Arvind K. Saibaba (CoRR, 2019)

Optimal experimental design under irreducible uncertainty for inverse problems governed by PDEs. - Karina Koval; Alen Alexanderian; Georg Stadler (CoRR, 2019)

Sensitivity-Driven Adaptive Construction of Reduced-space Surrogates. - Manav Vohra; Alen Alexanderian; Cosmin Safta; Sankaran Mahadevan (J. Sci. Comput., 2019)

Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems. - Arvind K. Saibaba; Johnathan M. Bardsley; D. Andrew Brown; Alen Alexanderian (SIAM/ASA J. Uncertain. Quantification, 2019)

Derivative-Based Global Sensitivity Analysis for Models with High-Dimensional Inputs and Functional Outputs. - Helen L. Cleaves; Alen Alexanderian; Hayley Guy; Ralph C. Smith; Meilin Yu (SIAM J. Sci. Comput., 2019)

Monte Carlo Estimators for the Schatten p-norm of Symmetric Positive Semidefinite Matrices. - Ethan Dudley; Arvind K. Saibaba; Alen Alexanderian (CoRR, 2020)

Structure exploiting methods for fast uncertainty quantification in multiphase flow through heterogeneous media. - Helen L. Cleaves; Alen Alexanderian; Bilal Saad (CoRR, 2020)

A Distributed Active Subspace Method for Scalable Surrogate Modeling of Function Valued Outputs. - Hayley Guy; Alen Alexanderian; Meilin Yu (J. Sci. Comput., 2020)

Variance-based sensitivity analysis for time-dependent processes. - Alen Alexanderian; Pierre-Alain Gremaud; Ralph C. Smith (Reliab. Eng. Syst. Saf., 2020)

Randomization and Reweighted ℓ1-Minimization for A-Optimal Design of Linear Inverse Problems. - Elizabeth Herman; Alen Alexanderian; Arvind K. Saibaba (SIAM J. Sci. Comput., 2020)

Global sensitivity analysis of rare event probabilities. - Michael Merritt; Alen Alexanderian; Pierre-Alain Gremaud (CoRR, 2021)

Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know. - Alen Alexanderian; Noemi Petra; Georg Stadler; Isaac Sunseri (SIAM/ASA J. Uncertain. Quantification, 2021)

Multiscale Global Sensitivity Analysis for Stochastic Chemical Systems. - Michael Merritt; Alen Alexanderian; Pierre-Alain Gremaud (Multiscale Model. Simul., 2021)

Extreme learning machines for variance-based global sensitivity analysis. - John Darges; Alen Alexanderian; Pierre-Alain Gremaud (CoRR, 2022)

Hyper-differential sensitivity analysis for nonlinear Bayesian inverse problems. - Isaac Sunseri; Alen Alexanderian; Joseph L. Hart; Bart G. van Bloemen Waanders (CoRR, 2022)

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty. - Alen Alexanderian; Ruanui Nicholson; Noemi Petra (CoRR, 2022)

Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems. - Abhijit Chowdhary; Shanyin Tong; Georg Stadler; Alen Alexanderian (CoRR, 2023)

A new perspective on parameter study of optimization problems. - Alen Alexanderian; Joseph L. Hart; Mason Stevens (Appl. Math. Lett., 2023)

Robust optimal design of large-scale Bayesian nonlinear inverse problems. - Abhijit Chowdhary; Ahmed Attia; Alen Alexanderian (CoRR, 2024)

Goal oriented optimal design of infinite-dimensional Bayesian inverse problems using quadratic approximations. - J. Nicholas Neuberger; Alen Alexanderian; Bart G. van Bloemen Waanders (CoRR, 2024)

Optimal sensor placement under model uncertainty in the weak-constraint 4D-Var framework. - Alen Alexanderian; Hugo Díaz; Vishwas Rao; Arvind K. Saibaba (CoRR, 2025)

A control-oriented approach to optimal sensor placement. - Madhusudan Madhavan; Alen Alexanderian; Arvind K. Saibaba; Bart G. van Bloemen Waanders; Rebekah D. White (CoRR, 2025)

Bridging the Gap Between Deterministic and Probabilistic Approaches to State Estimation. - Lev Kakasenko; Alen Alexanderian; Mohammad Farazmand; Arvind K. Saibaba (CoRR, 2025)