Publications

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

Research interests

My research interests include

Journal Papers

26. 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.
Submitted, 2019. code.

25. Randomization and reweighted ell_1-minimization for A-optimal design of linear inverse problems.

E. Herman, A. Alexanderian, A. K. Saibaba.
arXiv preprint, 2019.
arXiv.

24. Randomized algorithms for low-rank tensor decompositions in the Tucker format.

R. Minster, A. K. Saibaba, M.E. Kilmer.
arXiv preprint, 2019.
arXiv.

23. Randomized Discrete Empirical Interpolation Method for nonlinear model reduction.

A. K. Saibaba.
arXiv preprint, 2019.
arXiv.

22. Efficient marginalization-based MCMC Methods for hierarchical Bayesian inverse problems.

A. K. Saibaba, J. Bardsley, D.A. Brown, A. Alexanderian.
Under Revision, arXiv preprint, 2018.
arXiv.

21. Uncertainty Quantification in Large-scale Bayesian linear inverse problems using Krylov subspace methods.

A. K. Saibaba, J. Chung, K. Petroske.
arXiv preprint, 2018.
arXiv.

20. 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.

19. 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.

18. 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.

17. 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.

15. 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.

14. 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.

13. Efficient generalized Golub-Kahan based methods for dynamic inverse problems.

J. Chung, A. K. Saibaba, M. Brown, E. Westman.
Inverse Problems, 2018.
website, arXiv.

12. Randomized Matrix-free Trace and Log-Determinant Estimators.

A.K. Saibaba, A. Alexanderian, I.C.F. Ipsen.
Numerische Mathematik, 2017.
website, arXiv.

11. 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.

10. 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.

9. 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.

8. 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.

7. Fast algorithms for hyperspectral Diffuse Optical Tomography.

A.K. Saibaba, M. Kilmer, E.L. Miller, S. Fantini.
SIAM Journal on Scientific Computing, 2015.
website.

6. 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.

5. 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.

4. 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.

3. 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.

2. 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.

1. 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.

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

3. 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.
Proceedings of IGARSS, Montreal, 2014.
website.

1. Dimensionality reduction in the Geostatistical approach for Hydraulic Tomography.

A.K. Saibaba, P.K. Kitanidis.
Proceedings of Computational Methods in Water Resources 2012, June 2012, Illinois.
website pdf.

Thesis

1. Fast algorithms for geostatistical approach and uncertainty quantification

PhD Thesis, Stanford University, 2013.
website

Software