A Flexible Krylov Solver for Shifted Systems with Application to Oscillatory Hydraulic Tomography. - Arvind K. Saibaba; Tania Bakhos; Peter K. Kitanidis (SIAM J. Sci. Comput., 2013)

A fast Kalman filter for time-lapse electrical resistivity tomography. - Arvind K. Saibaba; Eric L. Miller 001; Peter K. Kitanidis (IGARSS, 2014)

A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers. - Tania Bakhos; Arvind K. Saibaba; Peter K. Kitanidis (J. Comput. Phys., 2015)

Fast Algorithms for Hyperspectral Diffuse Optical Tomography. - Arvind K. Saibaba; Misha Elena Kilmer; Eric L. Miller 001; Sergio Fantini (SIAM J. Sci. Comput., 2015)

Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen-Loève expansion. - Arvind K. Saibaba; Jonghyun Lee 005; Peter K. Kitanidis (Numer. Linear Algebra Appl., 2016)

HOID: Higher Order Interpolatory Decomposition for Tensors Based on Tucker Representation. - Arvind K. Saibaba (SIAM J. Matrix Anal. Appl., 2016)

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

Multipreconditioned Gmres for Shifted Systems. - Tania Bakhos; Peter K. Kitanidis; Scott Ladenheim; Arvind K. Saibaba; Daniel B. Szyld (SIAM J. Sci. Comput., 2017)

Generalized Hybrid Iterative Methods for Large-Scale Bayesian Inverse Problems. - Julianne Chung; Arvind K. Saibaba (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)

Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems. - D. Andrew Brown; Arvind K. Saibaba; Sarah Vallélian (SIAM/ASA J. Uncertain. Quantification, 2018)

A randomized tensor singular value decomposition based on the t-product. - Jiani Zhang 002; Arvind K. Saibaba; Misha E. Kilmer; Shuchin Aeron (Numer. Linear Algebra Appl., 2018)

The Discrete Empirical Interpolation Method: Canonical Structure and Formulation in Weighted Inner Product Spaces. - Zlatko Drmac; Arvind Krishna Saibaba (SIAM J. Matrix Anal. Appl., 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)

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)

Randomized Subspace Iteration: Analysis of Canonical Angles and Unitarily Invariant Norms. - Arvind K. Saibaba (SIAM J. Matrix Anal. Appl., 2019)

Efficient Randomized Algorithms for Subspace System Identification. - Rachel Minster; Arvind K. Saibaba; Jishnudeep Kar; Aranya Chakrabortty (CoRR, 2020)

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

Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems. - Arvind K. Saibaba; Julianne Chung; Katrina Petroske (Numer. Linear Algebra Appl., 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)

Randomized Discrete Empirical Interpolation Method for Nonlinear Model Reduction. - Arvind K. Saibaba (SIAM J. Sci. Comput., 2020)

Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format. - Rachel Minster; Arvind K. Saibaba; Misha E. Kilmer (SIAM J. Math. Data Sci., 2020)

Approximating monomials using Chebyshev polynomials. - Arvind K. Saibaba (CoRR, 2021)

Efficient edge-preserving methods for dynamic inverse problems. - Mirjeta Pasha; Arvind K. Saibaba; Silvia Gazzola; Malena I. Español; Eric de Sturler (CoRR, 2021)

Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions. - William Reese; Arvind K. Saibaba; Jonghyun Lee 005 (CoRR, 2021)

Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems. - Arvind K. Saibaba; Pranjal Prasad; Eric de Sturler; Eric L. Miller 001; Misha E. Kilmer (J. Comput. Phys., 2021)

Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis. - Arvind K. Saibaba; Joseph L. Hart; Bart G. van Bloemen Waanders (Numer. Linear Algebra Appl., 2021)

Efficient Algorithms for Eigensystem Realization Using Randomized SVD. - Rachel Minster; Arvind K. Saibaba; Jishnudeep Kar; Aranya Chakrabortty (SIAM J. Matrix Anal. Appl., 2021)

Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions. - Arvind K. Saibaba; Rachel Minster; Misha E. Kilmer (Adv. Comput. Math., 2022)

Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR). - Ege Ozsar; Misha Elena Kilmer; Eric L. Miller 001; Eric de Sturler; Arvind K. Saibaba (CoRR, 2022)

Robust Parameter Identifiability Analysis via Column Subset Selection. - Katherine J. Pearce; Ilse C. F. Ipsen; Mansoor A. Haider; Arvind K. Saibaba; Ralph C. Smith (CoRR, 2022)

Tensor-based flow reconstruction from optimally located sensor measurements. - Mohammad Farazmand; Arvind K. Saibaba (CoRR, 2022)

Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods. - Suman Majumder; Yawen Guan; Brian J. Reich; Arvind K. Saibaba (Stat. Comput., 2022)

Structured Matrix Approximations via Tensor Decompositions. - Misha E. Kilmer; Arvind K. Saibaba (SIAM J. Matrix Anal. Appl., 2022)

Randomized Reduced Basis Methods for Parameterized Fractional Elliptic PDEs. - Harbir Antil; Arvind K. Saibaba (CoRR, 2023)

Randomized Algorithms for Rounding in the Tensor-Train Format. - Hussam Al Daas; Grey Ballard; Paul Cazeaux; Eric Hallman; Agnieszka Miedlar; Mirjeta Pasha; Tim W. Reid; Arvind K. Saibaba (SIAM J. Sci. Comput., 2023)

Monte Carlo Methods for Estimating the Diagonal of a Real Symmetric Matrix. - Eric Hallman; Ilse C. F. Ipsen; Arvind K. Saibaba (SIAM J. Matrix Anal. Appl., 2023)

Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation. - Amit N. Subrahmanya; Vishwas Rao; Arvind K. Saibaba (CoRR, 2024)

Bayesian D-Optimal Experimental Designs via Column Subset Selection: The Power of Reweighted Sensors. - Srinivas Eswar; Vishwas Rao; Arvind K. Saibaba (CoRR, 2024)

Stable Rank and Intrinsic Dimension of Real and Complex Matrices. - Ilse C. F. Ipsen; Arvind K. Saibaba (CoRR, 2024)

Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation. - Julianne Chung; Scot M. Miller; Malena Sabate Landman; Arvind K. Saibaba (CoRR, 2024)

Efficient Algorithms for Bayesian Inverse Problems with Whittle-Matérn Priors. - Harbir Antil; Arvind K. Saibaba (SIAM J. Sci. Comput., 2024)

Hybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems. - Julianne Chung; Jiahua Jiang; Scot M. Miller; Arvind K. Saibaba (SIAM J. Sci. Comput., 2024)

Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems. - Khalil A Hall-Hooper; Arvind K. Saibaba; Julianne Chung; Scot M. Miller (Adv. Comput. Math., 2024)

Parametric Kernel Low-Rank Approximations Using Tensor Train Decomposition. - Abraham Khan; Arvind K. Saibaba (SIAM J. Matrix Anal. Appl., 2025)

Randomized Low-Rank Approximations beyond Gaussian Random Matrices. - Arvind K. Saibaba; Agnieszka Miedlar (SIAM J. Math. Data Sci., 2025)

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)

Structured Column Subset Selection for Bayesian Optimal Experimental Design. - Hugo Díaz; Arvind K. Saibaba; Srinivas Eswar; Vishwas Rao; Zichao Wendy Di (CoRR, 2025)

Improved Analysis of Khatri-Rao Random Projections and Applications. - Arvind K. Saibaba; Bhisham Dev Verma; Grey Ballard (CoRR, 2025)