Johnathan M. Bardsley
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Biomedical Engineering
- Mathematical Physics top 5%
- Co-authors
- James G. NagyHeikki HaarioAaron LuttmanAntti SolonenCurtis R. VogelMarko LaineS. M. JefferiesRobert J. Plemmons
- Topics
- Sparse and Compressive Sensing Techniques (19 papers)Medical Imaging Techniques and Applications (14 papers)Gaussian Processes and Bayesian Inference (11 papers)
- Partner nations
- United StatesFinlandDenmark
In The Last Decade
Johnathan M. Bardsley
44 papers receiving 809 citations
Peers
Comparison fields: 5 of 79
- Computer Vision and Pattern Recognition 288
- Computational Mechanics 284
- Radiology, Nuclear Medicine and Imaging 212
- Biomedical Engineering 178
- Mathematical Physics 163
Countries citing papers authored by Johnathan M. Bardsley
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 6 | |
| 3 | 10 | |
| 4 | 3 | |
| 5 | 90 | |
| 6 | 2 | |
| 7 | 7 | |
| 8 | 14 | |
| 9 | 21 | |
| 10 | LARGE-SCALE KALMAN FILTERING USING THE LIMITED MEMORY BFGS METHOD | 8 |
| 11 | A Fixed Point Formulation Of The k-Means Algorithm And A Connection To Mumford-Shah | 1 |
| 12 | 21 | |
| 13 | 29 | |
| 14 | 14 | |
| 15 | 45 | |
| 16 | A NONNEGATIVELY CONSTRAINED TRUST REGION ALGORITHM FOR THE RESTORATION OF IMAGES WITH AN UNKNOWN BLUR | 4 |
| 17 | 28 | |
| 18 | 15 | |
| 19 | 4 | |
| 20 | 57 |
About Johnathan M. Bardsley
Johnathan M. Bardsley is a scholar working on Mathematical Physics, Statistics and Probability and Computational Mechanics, having authored 44 papers that have together received 901 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (19 papers), Medical Imaging Techniques and Applications (14 papers) and Gaussian Processes and Bayesian Inference (11 papers). The work is most often cited by research in Mathematical Physics (163 citations), Computer Vision and Pattern Recognition (288 citations) and Computational Mechanics (284 citations). Johnathan M. Bardsley has collaborated with scholars based in United States, Finland and Denmark. Frequent 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. Their work appears in journals such as Optics Express, Astronomy and Astrophysics and Remote Sensing.
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.