Arvind K. Saibaba

607 total citations
35 papers, 314 citations indexed

About

Arvind K. Saibaba is a scholar working on Computational Mechanics, Computational Theory and Mathematics and Statistical and Nonlinear Physics. According to data from OpenAlex, Arvind K. Saibaba has authored 35 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computational Mechanics, 8 papers in Computational Theory and Mathematics and 7 papers in Statistical and Nonlinear Physics. Recurrent topics in Arvind K. Saibaba's work include Sparse and Compressive Sensing Techniques (7 papers), Probabilistic and Robust Engineering Design (6 papers) and Gaussian Processes and Bayesian Inference (6 papers). Arvind K. Saibaba is often cited by papers focused on Sparse and Compressive Sensing Techniques (7 papers), Probabilistic and Robust Engineering Design (6 papers) and Gaussian Processes and Bayesian Inference (6 papers). Arvind K. Saibaba collaborates with scholars based in United States, United Kingdom and Austria. Arvind K. Saibaba's co-authors include Peter K. Kitanidis, Misha E. Kilmer, Jonghyun Lee, Alen Alexanderian, Shuchin Aeron, Scot M. Miller, Julianne Chung, Mohammad Farazmand, A. E. Andrews and Sivaram Ambikasaran and has published in prestigious journals such as Journal of Fluid Mechanics, Water Resources Research and Journal of Computational Physics.

In The Last Decade

Arvind K. Saibaba

30 papers receiving 294 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Arvind K. Saibaba United States 11 82 68 62 60 48 35 314
Tiangang Cui Australia 13 35 0.4× 75 1.1× 213 3.4× 14 0.2× 19 0.4× 36 634
Sylvain Maire France 10 36 0.4× 74 1.1× 18 0.3× 14 0.2× 9 0.2× 28 251
Alexander Litvinenko Germany 10 57 0.7× 50 0.7× 44 0.7× 50 0.8× 11 0.2× 30 367
Claudia Schillings Germany 15 93 1.1× 132 1.9× 102 1.6× 3 0.1× 17 0.4× 30 495
Harbir Antil United States 13 104 1.3× 77 1.1× 18 0.3× 3 0.1× 14 0.3× 61 422
V. Akçelik United States 8 66 0.8× 39 0.6× 72 1.2× 4 0.1× 13 0.3× 12 595
Mark A. Lukas Australia 10 126 1.5× 40 0.6× 50 0.8× 3 0.1× 18 0.4× 25 533
Radu Alexandru Todor Switzerland 8 259 3.2× 282 4.1× 19 0.3× 27 0.5× 10 0.2× 12 770
Luisa D’Amore Italy 14 71 0.9× 67 1.0× 57 0.9× 27 0.6× 56 472
Carlos Ortiz Marrero United States 5 70 0.9× 56 0.8× 274 4.4× 2 0.0× 15 0.3× 11 596

Countries citing papers authored by Arvind K. Saibaba

Since Specialization
Citations

This map shows the geographic impact of Arvind K. Saibaba's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Arvind K. Saibaba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arvind K. Saibaba more than expected).

Fields of papers citing papers by Arvind K. Saibaba

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arvind K. Saibaba. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Arvind K. Saibaba. The network helps show where Arvind K. Saibaba may publish in the future.

Co-authorship network of co-authors of Arvind K. Saibaba

This figure shows the co-authorship network connecting the top 25 collaborators of Arvind K. Saibaba. A scholar is included among the top collaborators of Arvind K. Saibaba based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Arvind K. Saibaba. Arvind K. Saibaba is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Farazmand, Mohammad, et al.. (2026). Bridging the gap between deterministic and probabilistic approaches to state estimation. Physica D Nonlinear Phenomena. 490. 135150–135150.
2.
Kilmer, Misha E., et al.. (2025). Parametric level-sets enhanced to improve reconstruction (PaLEnTIR). Inverse Problems. 41(2). 25004–25004.
3.
Saibaba, Arvind K., et al.. (2025). Parametric Kernel Low-Rank Approximations Using Tensor Train Decomposition. SIAM Journal on Matrix Analysis and Applications. 46(2). 1006–1036.
4.
Farazmand, Mohammad & Arvind K. Saibaba. (2023). Tensor-based flow reconstruction from optimally located sensor measurements. Journal of Fluid Mechanics. 962. 10 indexed citations
5.
Chung, Julianne, et al.. (2023). Hybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems. SIAM Journal on Scientific Computing. 46(2). S97–S119. 6 indexed citations
6.
Ballard, Grey, et al.. (2023). Randomized Algorithms for Rounding in the Tensor-Train Format. SIAM Journal on Scientific Computing. 45(1). A74–A95. 13 indexed citations
7.
Saibaba, Arvind K., et al.. (2023). A computational framework for edge-preserving regularization in dynamic inverse problems. ETNA - Electronic Transactions on Numerical Analysis. 58. 486–516. 10 indexed citations
8.
Chung, Julianne, et al.. (2022). Computationally efficient methods for large-scale atmospheric inverse modeling. Geoscientific model development. 15(14). 5547–5565. 7 indexed citations
9.
Kilmer, Misha E. & Arvind K. Saibaba. (2022). Structured Matrix Approximations via Tensor Decompositions. SIAM Journal on Matrix Analysis and Applications. 43(4). 1599–1626.
10.
Saibaba, Arvind K., et al.. (2021). Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems. Journal of Computational Physics. 440. 110391–110391. 2 indexed citations
11.
Saibaba, Arvind K., et al.. (2021). Monte Carlo estimators for the Schatten p-norm of symmetric positive semidefinite matrices. ETNA - Electronic Transactions on Numerical Analysis. 55. 213–241. 3 indexed citations
12.
Miller, Scot M., Arvind K. Saibaba, M. Trudeau, Marikate Mountain, & A. E. Andrews. (2020). Geostatistical inverse modeling with very large datasets: an example from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Geoscientific model development. 13(3). 1771–1785. 21 indexed citations
13.
Saibaba, Arvind K., et al.. (2020). Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems. Numerical Linear Algebra with Applications. 27(5). 10 indexed citations
14.
Saibaba, Arvind K., et al.. (2020). Efficient Randomized Algorithms for Subspace System Identification.. 1 indexed citations
15.
Miller, Scot M., Arvind K. Saibaba, M. Trudeau, Marikate Mountain, & A. E. Andrews. (2019). Geostatistical inverse modeling with very large datasets: an example from the OCO-2 satellite. 1 indexed citations
16.
Brown, D. Andrew, et al.. (2018). Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems. SIAM/ASA Journal on Uncertainty Quantification. 6(3). 1076–1100. 7 indexed citations
17.
Saibaba, Arvind K., et al.. (2015). A fast algorithm for parabolic PDE-based inverse problems based on Laplace transforms and flexible Krylov solvers. Journal of Computational Physics. 299. 940–954. 3 indexed citations
18.
Saibaba, Arvind K. & Peter K. Kitanidis. (2014). Uncertainty quantification in geostatistical approach to inverse problems. arXiv (Cornell University). 1 indexed citations
19.
Saibaba, Arvind K. & Peter K. Kitanidis. (2013). Randomized square-root free algorithms for generalized Hermitian eigenvalue problems. arXiv (Cornell University). 2 indexed citations
20.
Saibaba, Arvind K., et al.. (2012). Application of Hierarchical Matrices to Linear Inverse Problems in Geostatistics. Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles. 67(5). 857–875. 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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