Publications

Link to Google Scholar page and Research gate profile. For the bibtex entries, please see github.

Journal Papers

  1. Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods.
    S. Majumder, Y. Guan, B.J. Reich, A.K. Saibaba
    Submitted, 2020.
    arXiv. code.

  2. Randomized approaches to accelerate MCMC algorithms for Bayesian Inverse Problems.
    A. K. Saibaba, P. Pranjal, E. de Sturler, E. Miller, M. Kilmer
    Submitted, 2020.

  3. Monte Carlo Estimators for the Schatten p-norm of Symmetric Positive Semidefinite Matrices.
    E. Dudley, A. K. Saibaba, A. Alexanderian
    Submitted, 2020.
    arXiv.

  4. Efficient Randomized Algorithms for Subspace System Identification.
    R. Minster, A. K. Saibaba, J. Kar, A. Chakrabortty
    Submitted, 2020.
    arXiv. code.

  5. Randomized Algorithms for Generalized Singular Value Decomposition with Application to Sensitivity Analysis.
    A. K. Saibaba, J. Hart, B.v.B. Waanders
    To appear, Numerical Linear Algebra with Applications, 2021.
    arXiv.

  6. Geostatistical inverse modeling with very large datasets: an example from the OCO-2 satellite.
    S. Miller, A. K. Saibaba, M. E. Trudeau, M.E. Mountain, A.E. Andrews.
    Geoscientific Model Development, 2020.
    website code.

  7. Randomization and reweighted $\ell_1$-minimization for A-optimal design of linear inverse problems.
    E. Herman, A. Alexanderian, A. K. Saibaba.
    SIAM Journal on Scientific Computing, 2020.
    website. arXiv.

  8. Randomized Discrete Empirical Interpolation Method for nonlinear model reduction.
    A. K. Saibaba.
    SIAM Journal on Scientific Computing, 2020.
    website. arXiv. code.

  9. Randomized algorithms for low-rank tensor decompositions in the Tucker format.
    R. Minster, A. K. Saibaba, M.E. Kilmer.
    SIAM Journal on Mathematics of Data Science, 2019.
    website. arXiv. code

  10. Efficient marginalization-based MCMC Methods for hierarchical Bayesian inverse problems.
    A. K. Saibaba, J. Bardsley, D.A. Brown, A. Alexanderian.
    SIAM/ASA Journal On Uncertainty Quantification, 2019.
    website.

  11. Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems.
    A. K. Saibaba, J. Chung, K. Petroske.
    Numerical Linear Algebra with Applications, 2020.
    website, arXiv.

  12. Randomized Subspace Iteration: Analysis of canonical angles and unitarily invariant norms.
    A. K. Saibaba.
    SIAM Journal on Matrix Analysis and Applications, 2018.
    website, arXiv, code.

  13. Efficient D-optimal design of experiments for infinite-dimensional Bayesian linear inverse problems.
    A. Alexanderian, A. K. Saibaba.
    SIAM Journal on Scientific Computing, 2018.
    website, arxiv.

  14. Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems.
    A. Attia, A. Alexanderian, A. K. Saibaba
    Inverse Problems, 2018.
    website, arXiv.

  15. Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion.
    E.C. Chi, L. Hu, A.K. Saibaba, A.U.K. Rao.
    Journal of Computational and Graphical Statistics, 2018.
    website, arXiv, code.

  16. The Discrete Empirical Interpolation Method: Canonical Structure and Formulation in Weighted Inner Product Spaces.
    Z. Drmac, A.K. Saibaba.
    SIAM Journal on Matrix Analysis and Applications, 2018.
    website, arXiv.

  17. A Randomized Tensor Singular Value Decomposition based on the t-product.
    J. Zhang, A.K. Saibaba, M.E. Kilmer, S. Aeron.
    Numerical Linear Algebra with applications, 2018.
    website, arXiv.

  18. Low-Rank Independence Samplers for Hierarchical Bayesian Inverse Problems.
    D. A. Brown, A.K. Saibaba, S. Vall\'elian.
    SIAM/ASA Journal on Uncertainty Quantification, 2018.
    website, arXiv, code.

  19. Efficient generalized Golub-Kahan based methods for dynamic inverse problems.
    J. Chung, A. K. Saibaba, M. Brown, E. Westman.
    Inverse Problems, 2018.
    website, arXiv.

  20. Randomized Matrix-free Trace and Log-Determinant Estimators.
    A.K. Saibaba, A. Alexanderian, I.C.F. Ipsen.
    Numerische Mathematik, 2017.
    website, arXiv.

  21. Multipreconditioned GMRES for Shifted Systems.
    T. Bakhos, P.K. Kitanidis, S. Ladenheim, A.K. Saibaba, D. Szyld.
    SIAM Journal on Scientific Computing, 2017.
    website, arXiv.

  22. Generalized Hybrid Iterative methods for Large-Scale Bayesian Inverse Problems.
    J.M. Chung, A.K. Saibaba.
    SIAM Journal on Scientific Computing, 2017.
    website, arXiv, code.

  23. HOID: Higher Order Interpolatory Decomposition for tensors based on Tucker representation.
    A. K. Saibaba.
    SIAM Journal on Matrix Analysis and Applications, 2016.
    website, arXiv, code.

  24. Randomized square-root free algorithms for generalized Hermitian eigenvalue problems
    A.K. Saibaba, J. Lee, P.K. Kitanidis.
    Numerical Linear Algebra with Applications, 2015.
    website, code.

  25. Fast algorithms for hyperspectral Diffuse Optical Tomography.
    A.K. Saibaba, M. Kilmer, E.L. Miller, S. Fantini.
    SIAM Journal on Scientific Computing, 2015.
    website.

  26. Fast computation of uncertainty quantification measures in the geostatistical approach to inverse problems.
    A.K. Saibaba, P.K. Kitanidis.
    Advances in Water Resources, 2015.
    website, arXiv.

  27. A Fast Algorithm for Parabolic PDE-based Inverse Problems Based on Laplace Tranforms and Flexible Krylov Solvers.
    T. Bakhos, A.K. Saibaba, P.K. Kitanidis.
    Journal of Computational Physics, 2015.
    website, arXiv.

  28. Fast Kalman Filter using Hierarchical-matrices and low-rank perturbative approach.
    A.K. Saibaba, E.L. Miller and P.K. Kitanidis.
    Inverse Problems, 2015. Featured article on Inverse Problems website.
    website, arXiv.

  29. A Flexible Krylov Solver for Shifted Systems with Application to Oscillatory Hydraulic Tomography.
    A.K. Saibaba, T. Bakhos, P.K. Kitanidis.
    SIAM Journal on Scientific Computing, 2013.
    website, arXiv.

  30. Application of Hierarchical Matrices to Linear Inverse problems for Geostatistical Applications.
    A.K. Saibaba, S. Ambikasaran, J.Y. Li, P.K. Kitanidis and E.F.Darve.
    Invited paper, OGST Revue d'IFP Energies Nouvelles, 2012.
    website.

  31. Efficient methods for Large-Scale Linear Inversion for Geostatistical Applications.
    A.K. Saibaba, P.K. Kitanidis.
    Water Resources Research, 2012. Featured article (2012) and WRR Editors' Choice award (2013).
    website.

Expository work

  1. Approximating monomials using Chebyshev polynomials.
    A.K. Saibaba.
    arXiv.

Book Chapters

  1. Fast Algorithms for Bayesian Inverse Problems.
    S. Ambikasaran, A.K. Saibaba, E.F. Darve, and P.K. Kitanidis.
    IMA Computational Challenges in the Geosciences Vol. 156, 2013.
    website.

Conference Proceedings

  1. 3D parameter reconstruction in hyperspectral diffuse optical tomography
    A.K. Saibaba, N. Krishnamurthy, P.G. Anderson, J.M. Kainerstorfer, A. Sassaroli, E.L. Miller, S. Fantini, M.E. Kilmer.
    Proc. SPIE 9319, Optical Tomography and Spectroscopy of Tissue XI, 2015.
    website.

  2. A Fast Kalman Filter for time-lapse Electrical Resistivity Tomography.
    A.K. Saibaba, E.L. Miller, P.K. Kitanidis.\n Proceedings of IGARSS, Montreal, 2014.
    website.

  3. Dimensionality reduction in the Geostatistical approach for Hydraulic Tomography.
    A.K. Saibaba, P.K. Kitanidis.\n Proceedings of Computational Methods in Water Resources 2012, June 2012, Illinois.
    website.

Thesis

  1. Fast algorithms for geostatistical approach and uncertainty quantification.
    PhD Thesis, Stanford University, 2013.
    website

Software