Johnathan M. Bardsley

1.4k total citations
44 papers, 901 citations indexed

About

Johnathan M. Bardsley is a scholar working on Computational Mechanics, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Johnathan M. Bardsley has authored 44 papers receiving a total of 901 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computational Mechanics, 15 papers in Artificial Intelligence and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in Johnathan M. Bardsley's work include Sparse and Compressive Sensing Techniques (19 papers), Medical Imaging Techniques and Applications (14 papers) and Gaussian Processes and Bayesian Inference (11 papers). Johnathan M. Bardsley is often cited by papers focused on Sparse and Compressive Sensing Techniques (19 papers), Medical Imaging Techniques and Applications (14 papers) and Gaussian Processes and Bayesian Inference (11 papers). Johnathan M. Bardsley collaborates with scholars based in United States, Finland and Denmark. Johnathan M. Bardsley's co-authors include James G. Nagy, Heikki Haario, Aaron Luttman, Antti Solonen, Curtis R. Vogel, Marko Laine, S. M. Jefferies, Robert J. Plemmons, W. Wamsteker and R. Vio and has published in prestigious journals such as Optics Express, Astronomy and Astrophysics and Remote Sensing.

In The Last Decade

Johnathan M. Bardsley

44 papers receiving 809 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Johnathan M. Bardsley United States 17 288 284 212 178 163 44 901
Marcelo Pereyra United Kingdom 18 201 0.7× 221 0.8× 169 0.8× 102 0.6× 38 0.2× 53 930
G. Demoment France 10 264 0.9× 161 0.6× 148 0.7× 114 0.6× 55 0.3× 35 727
Serena Morigi Italy 20 488 1.7× 558 2.0× 117 0.6× 215 1.2× 364 2.2× 83 1.4k
Rosemary A. Renaut United States 24 190 0.7× 297 1.0× 218 1.0× 109 0.6× 152 0.9× 92 1.3k
W. R. Madych United States 14 285 1.0× 554 2.0× 71 0.3× 53 0.3× 206 1.3× 57 1.4k
Jason D. McEwen United Kingdom 17 285 1.0× 222 0.8× 85 0.4× 80 0.4× 35 0.2× 63 1.0k
Bradley J. Lucier United States 18 1.1k 3.8× 597 2.1× 123 0.6× 169 0.9× 220 1.3× 44 1.9k
Ronny Ramlau Austria 24 353 1.2× 423 1.5× 178 0.8× 488 2.7× 708 4.3× 118 1.6k
Jinjun Xu United States 4 905 3.1× 661 2.3× 197 0.9× 233 1.3× 236 1.4× 6 1.4k
Armin Iske Germany 18 238 0.8× 597 2.1× 53 0.3× 33 0.2× 64 0.4× 61 1.1k

Countries citing papers authored by Johnathan M. Bardsley

Since Specialization
Citations

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

Fields of papers citing papers by Johnathan M. Bardsley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johnathan M. Bardsley. 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 Johnathan M. Bardsley. The network helps show where Johnathan M. Bardsley may publish in the future.

Co-authorship network of co-authors of Johnathan M. Bardsley

This figure shows the co-authorship network connecting the top 25 collaborators of Johnathan M. Bardsley. A scholar is included among the top collaborators of Johnathan M. Bardsley 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 Johnathan M. Bardsley. Johnathan M. Bardsley 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.
Bardsley, Johnathan M., et al.. (2021). Matlab Software for Supervised Habitat Mapping of Freshwater Systems Using Image Processing. Remote Sensing. 13(23). 4906–4906. 7 indexed citations
2.
Bardsley, Johnathan M. & Per Christian Hansen. (2020). MCMC Algorithms for Computational UQ of Nonnegativity Constrained Linear Inverse Problems. SIAM Journal on Scientific Computing. 42(2). A1269–A1288. 6 indexed citations
3.
Chung, Matthias, et al.. (2019). Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes. SIAM Journal on Scientific Computing. 41(4). A2212–A2238. 10 indexed citations
4.
Bardsley, Johnathan M. & Aaron Luttman. (2014). Dealing with boundary artifacts in MCMC-based deconvolution. Linear Algebra and its Applications. 473. 339–358. 3 indexed citations
5.
Bardsley, Johnathan M., Antti Solonen, Heikki Haario, & Marko Laine. (2014). Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems. SIAM Journal on Scientific Computing. 36(4). A1895–A1910. 90 indexed citations
6.
Solonen, Antti, et al.. (2014). OPTIMIZATION-BASED SAMPLING IN ENSEMBLE KALMAN FILTERING. International Journal for Uncertainty Quantification. 4(4). 349–364. 2 indexed citations
7.
Bardsley, Johnathan M., et al.. (2012). AN ENSEMBLE KALMAN FILTER USING THE CONJUGATE GRADIENT SAMPLER. International Journal for Uncertainty Quantification. 3(4). 357–370. 7 indexed citations
8.
Bardsley, Johnathan M., et al.. (2011). Structured linear algebra problems in adaptive optics imaging. Advances in Computational Mathematics. 35(2-4). 103–117. 14 indexed citations
9.
Bardsley, Johnathan M., Daniela Calvetti, & Erkki Somersalo. (2010). Hierarchical regularization for edge-preserving reconstruction of PET images. Inverse Problems. 26(3). 35010–35010. 21 indexed citations
10.
Auvinen, Harri, Johnathan M. Bardsley, Heikki Haario, & Tuomo Kauranne. (2009). LARGE-SCALE KALMAN FILTERING USING THE LIMITED MEMORY BFGS METHOD. 35. 217–233. 8 indexed citations
11.
Bardsley, Johnathan M. & Aaron Luttman. (2009). A Fixed Point Formulation Of The k-Means Algorithm And A Connection To Mumford-Shah. 9. 274–276. 1 indexed citations
12.
Auvinen, Harri, Johnathan M. Bardsley, Heikki Haario, & Tuomo Kauranne. (2009). The variational Kalman filter and an efficient implementation using limited memory BFGS. International Journal for Numerical Methods in Fluids. 64(3). 314–335. 21 indexed citations
13.
Bardsley, Johnathan M., et al.. (2008). Tikhonov regularized Poisson likelihood estimation: theoretical justification and a computational method. Inverse Problems in Science and Engineering. 16(2). 199–215. 29 indexed citations
14.
Bardsley, Johnathan M.. (2008). Stopping rules for a nonnegatively constrained iterative method for ill-posed Poisson imaging problems. BIT Numerical Mathematics. 48(4). 651–664. 14 indexed citations
15.
Bardsley, Johnathan M. & James G. Nagy. (2006). Covariance-Preconditioned Iterative Methods for Nonnegatively Constrained Astronomical Imaging. SIAM Journal on Matrix Analysis and Applications. 27(4). 1184–1197. 45 indexed citations
16.
Bardsley, Johnathan M.. (2005). A NONNEGATIVELY CONSTRAINED TRUST REGION ALGORITHM FOR THE RESTORATION OF IMAGES WITH AN UNKNOWN BLUR. 20. 139–153. 4 indexed citations
17.
Vio, R., Johnathan M. Bardsley, & W. Wamsteker. (2005). Least-squares methods with Poissonian noise: Analysis and comparison with the Richardson-Lucy algorithm. Astronomy and Astrophysics. 436(2). 741–755. 28 indexed citations
18.
Vio, R., Johnathan M. Bardsley, Marco Donatelli, & W. Wamsteker. (2005). Dealing with edge effects in least-squares image deconvolution problems. Astronomy and Astrophysics. 442(1). 397–403. 15 indexed citations
19.
Bardsley, Johnathan M.. (2004). A limited-memory, quasi-Newton preconditioner for nonnegatively constrained image reconstruction. Journal of the Optical Society of America A. 21(5). 724–724. 4 indexed citations
20.
Bardsley, Johnathan M. & Curtis R. Vogel. (2003). A Nonnegatively Constrained Convex Programming Method for Image Reconstruction. SIAM Journal on Scientific Computing. 25(4). 1326–1343. 57 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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