John W. Pearson

3.4k total citations
109 papers, 2.4k citations indexed

About

John W. Pearson is a scholar working on Computational Theory and Mathematics, Immunology and Computational Mechanics. According to data from OpenAlex, John W. Pearson has authored 109 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computational Theory and Mathematics, 24 papers in Immunology and 23 papers in Computational Mechanics. Recurrent topics in John W. Pearson's work include Matrix Theory and Algorithms (31 papers), Advanced Numerical Methods in Computational Mathematics (23 papers) and Electromagnetic Scattering and Analysis (12 papers). John W. Pearson is often cited by papers focused on Matrix Theory and Algorithms (31 papers), Advanced Numerical Methods in Computational Mathematics (23 papers) and Electromagnetic Scattering and Analysis (12 papers). John W. Pearson collaborates with scholars based in United States, United Kingdom and Germany. John W. Pearson's co-authors include Joseph S. Redding, Andrew J. Wathen, M. A. Chirigos, Martin Stoll, Dan L. Longo, Louis Lasagna, Ronald L. Hornung, Margaret Beckwith, S D Chaparas and Jennifer Pestana and has published in prestigious journals such as New England Journal of Medicine, The Lancet and JAMA.

In The Last Decade

John W. Pearson

102 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John W. Pearson United States 27 587 461 412 325 323 109 2.4k
Peter K. Moore United States 24 103 0.2× 298 0.6× 72 0.2× 243 0.7× 83 0.3× 73 2.5k
S. Sivaloganathan Canada 25 140 0.2× 276 0.6× 52 0.1× 147 0.5× 30 0.1× 81 1.9k
Shiro Ishikawa Japan 26 56 0.1× 357 0.8× 1.3k 3.2× 40 0.1× 243 0.8× 100 3.4k
Vittorio Rizzoli Italy 38 56 0.1× 1.5k 3.3× 77 0.2× 40 0.1× 451 1.4× 283 5.8k
Hien Tran United States 29 15 0.0× 745 1.6× 134 0.3× 271 0.8× 257 0.8× 134 3.6k
John Jeremy Rice United States 35 377 0.6× 2.1k 4.6× 134 0.3× 20 0.1× 96 0.3× 105 5.2k
Yang Dai United States 37 38 0.1× 2.0k 4.4× 109 0.3× 41 0.1× 703 2.2× 159 4.3k
Mark Fleming United States 26 19 0.0× 2.0k 4.4× 335 0.8× 1.5k 4.6× 859 2.7× 62 6.4k
Jonathan M. Weiss United States 41 48 0.1× 1.5k 3.3× 44 0.1× 1.0k 3.2× 2.3k 7.1× 129 6.2k
Tomás Alarcón Spain 29 31 0.1× 1.4k 3.1× 136 0.3× 66 0.2× 113 0.3× 144 3.4k

Countries citing papers authored by John W. Pearson

Since Specialization
Citations

This map shows the geographic impact of John W. Pearson'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 John W. Pearson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John W. Pearson more than expected).

Fields of papers citing papers by John W. Pearson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John W. Pearson. 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 John W. Pearson. The network helps show where John W. Pearson may publish in the future.

Co-authorship network of co-authors of John W. Pearson

This figure shows the co-authorship network connecting the top 25 collaborators of John W. Pearson. A scholar is included among the top collaborators of John W. Pearson 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 John W. Pearson. John W. Pearson 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.
Pearson, John W., et al.. (2025). A constrained optimisation framework for parameter identification of the SIRD model. Mathematical Biosciences. 380. 109379–109379. 1 indexed citations
2.
Pearson, John W., et al.. (2024). Saddle point preconditioners for weak-constraint 4D-Var. ETNA - Electronic Transactions on Numerical Analysis. 60. 197–220.
3.
Bergamaschi, Luca, et al.. (2024). Parallel-in-Time Solver for the All-at-Once Runge–Kutta Discretization. SIAM Journal on Matrix Analysis and Applications. 45(4). 1902–1928. 2 indexed citations
4.
Bergamaschi, Luca, Ángeles Martínez, John W. Pearson, & Andreas Potschka. (2024). Spectral analysis of block preconditioners for double saddle-point linear systems with application to PDE-constrained optimization. Computational Optimization and Applications. 91(2). 423–455.
5.
Pearson, John W. & Andreas Potschka. (2024). Double saddle‐point preconditioning for Krylov methods in the inexact sequential homotopy method. Numerical Linear Algebra with Applications. 31(4).
6.
Pearson, John W. & Andreas Potschka. (2023). On symmetric positive definite preconditioners for multiple saddle-point systems. IMA Journal of Numerical Analysis. 44(3). 1731–1750. 5 indexed citations
7.
Pearson, John W., et al.. (2022). Parameter-Robust Preconditioning for Oseen Iteration Applied to Stationary and Instationary Navier--Stokes Control. SIAM Journal on Scientific Computing. 44(3). B694–B722. 1 indexed citations
8.
Goddard, Benjamin D., et al.. (2022). Pseudospectral methods and iterative solvers for optimization problems from multiscale particle dynamics. BIT Numerical Mathematics. 62(4). 1703–1743. 5 indexed citations
9.
Gondzio, Jacek, et al.. (2022). General-purpose preconditioning for regularized interior point methods. Computational Optimization and Applications. 83(3). 727–757. 2 indexed citations
10.
Pearson, John W., et al.. (2020). Fast Solution Methods for Convex Quadratic Optimization of Fractional Differential Equations. SIAM Journal on Matrix Analysis and Applications. 41(3). 1443–1476. 2 indexed citations
11.
Pearson, John W.. (2016). Fast iterative solvers for large matrix systems arising from time-dependent Stokes control problems. Applied Numerical Mathematics. 108. 87–101. 6 indexed citations
12.
Pearson, John W.. (2015). Preconditioned iterative methods for Navier–Stokes control problems. Journal of Computational Physics. 292. 194–207. 11 indexed citations
13.
Pearson, John W. & Andrew J. Wathen. (2013). FAST ITERATIVE SOLVERS FOR CONVECTION-DIFFUSION CONTROL PROBLEMS ∗. ETNA - Electronic Transactions on Numerical Analysis. 40. 294–310. 29 indexed citations
14.
Pearson, John W. & Andrew J. Wathen. (2011). A new approximation of the Schur complement in preconditioners for PDE‐constrained optimization. Numerical Linear Algebra with Applications. 19(5). 816–829. 139 indexed citations
15.
Cooper, John A., et al.. (2001). Attenuation of Interleukin 8-induced Nasal Inflammation by an Inhibitor Peptide. American Journal of Respiratory and Critical Care Medicine. 163(5). 1198–1205. 12 indexed citations
16.
Pearson, John W., et al.. (1975). Drug therapy against a transplantable guinea pig leukemia.. PubMed. 35(4). 1093–8. 8 indexed citations
17.
Chirigos, M. A., et al.. (1973). BCG immunization for the lymphoid leukemia line (LSTRA) in CDF1 mice.. PubMed. 39. 87–8. 1 indexed citations
18.
Pearson, John W., et al.. (1972). Combined chemoimmunostimulation therapy against murine leukemia.. PubMed. 32(5). 904–7. 59 indexed citations
19.
Redding, Joseph S. & John W. Pearson. (1963). Evaluation of Drugs for Cardiac Resuscitation. Anesthesiology. 24(2). 203–207. 200 indexed citations
20.
Pearson, John W.. (1962). EFFICIENCY OF CARDIAC MASSAGE. The Lancet. 280(7255). 559–559. 1 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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