Kathryn Maupin
- Statistics, Probability and Uncertainty top 10%
- Computational Theory and Mathematics
- Aerospace Engineering
- Control and Systems Engineering
- Statistical and Nonlinear Physics
- Co-authors
- Laura SwilerTheron RodgersAnh TranEric PhippsPatrick BloniganMichael E. GlinskyJaideep RayVincent A. Mousseau
- Topics
- Probabilistic and Robust Engineering Design (4 papers)Gaussian Processes and Bayesian Inference (3 papers)Machine Learning in Materials Science (3 papers)
- Cited by
- Statistics, Probability and UncertaintyComputational MathematicsComputational Theory and Mathematics
- Partner nations
- United States
In The Last Decade
Kathryn Maupin
8 papers receiving 62 citations
Peers
Comparison fields: 5 of 36
- Statistics, Probability and Uncertainty 28
- Computational Theory and Mathematics 13
- Aerospace Engineering 13
- Control and Systems Engineering 10
- Statistical and Nonlinear Physics 10
Countries citing papers authored by Kathryn Maupin
This map shows the geographic impact of Kathryn Maupin'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 Kathryn Maupin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kathryn Maupin more than expected).
Fields of papers citing papers by Kathryn Maupin
This network shows the impact of papers produced by Kathryn Maupin. 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 Kathryn Maupin. The network helps show where Kathryn Maupin may publish in the future.
Co-authorship network of co-authors of Kathryn Maupin
This figure shows the co-authorship network connecting the top 25 collaborators of Kathryn Maupin. A scholar is included among the top collaborators of Kathryn Maupin 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 Kathryn Maupin. Kathryn Maupin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 7 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 1 | |
| 11 | 22 | |
| 12 | 25 |
About Kathryn Maupin
Kathryn Maupin is a scholar working on Statistics, Probability and Uncertainty, Management Science and Operations Research and Computational Theory and Mathematics, having authored 12 papers that have together received 62 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (4 papers), Gaussian Processes and Bayesian Inference (3 papers) and Machine Learning in Materials Science (3 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (28 citations), Computational Mathematics (1 citation) and Computational Theory and Mathematics (13 citations). Kathryn Maupin has collaborated with scholars based in United States. Frequent co-authors include Laura Swiler, Theron Rodgers, Anh Tran, Eric Phipps, Patrick Blonigan, Michael E. Glinsky, Jaideep Ray, Vincent A. Mousseau, Danial Faghihi and Zhen Hu. Their work appears in journals such as AIAA Journal, Reliability Engineering & System Safety and Computational Mechanics.
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.