Charles C. Margossian
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies 1
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- COVID-19 Clinical Research Studies 1
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- Statistical Methods and Inference 3
- Statistical Methods and Bayesian Inference 2
- Markov Chains and Monte Carlo Methods 1
- Statistical Methods in Clinical Trials 1
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- Bayesian Methods and Mixture Models 2
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- Advanced Control Systems Optimization 1
- Co-authors
- Nicola LowGaryfallos KonstantinoudisChristian L. AlthausJulien RiouMichel Jacques CounotteAnthony HauserWilliam R. GillespieAndrew Gelman
- Journals
- Bayesian Analysis (2 papers)PLoS Medicine (1 paper)CPT Pharmacometrics & Systems Pharmacology (1 paper)
- Partner nations
- United StatesFinlandSwitzerland
In The Last Decade
Charles C. Margossian
5 papers receiving 286 citations
Peers
Comparison fields: 5 of 89
- Modeling and Simulation 74
- Computational Mathematics 3
- Statistical and Nonlinear Physics 61
- Infectious Diseases 53
- Numerical Analysis 14
Countries citing papers authored by Charles C. Margossian
This map shows the geographic impact of Charles C. Margossian'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 Charles C. Margossian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles C. Margossian more than expected).
Fields of papers citing papers by Charles C. Margossian
This network shows the impact of papers produced by Charles C. Margossian. 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 Charles C. Margossian. The network helps show where Charles C. Margossian may publish in the future.
Co-authorship network
The 12 scholars most cited alongside Charles C. Margossian, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2022 | 10 | |
| 3 | 2022 | 0 | |
| 4 | 2020 | 96 | |
| 5 | 2019 | 185 | |
| 6 | 2018 | 1 |
About Charles C. Margossian
Charles C. Margossian is a scholar working on Statistics and Probability, Modeling and Simulation and Artificial Intelligence, having authored 6 papers that have together received 293 indexed citations. Recurring topics across this work include Statistical Methods and Inference (3 papers), Statistical Methods and Bayesian Inference (2 papers), Bayesian Methods and Mixture Models (2 papers), Markov Chains and Monte Carlo Methods (1 paper), COVID-19 epidemiological studies (1 paper), Statistical Methods in Clinical Trials (1 paper), COVID-19 Clinical Research Studies (1 paper) and Advanced Control Systems Optimization (1 paper). The work is most often cited by research in Modeling and Simulation (74 citations), Computational Mathematics (3 citations) and Statistical and Nonlinear Physics (61 citations). Charles C. Margossian has collaborated with scholars based in United States, Finland and Switzerland. Frequent co-authors include Nicola Low, Garyfallos Konstantinoudis, Christian L. Althaus, Julien Riou, Michel Jacques Counotte, Anthony Hauser, William R. Gillespie, Andrew Gelman, Andrew Gelman and Aki Vehtari. Their work appears in journals such as Bayesian Analysis, PLoS Medicine, CPT Pharmacometrics & Systems Pharmacology, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery and Zenodo (CERN European Organization for Nuclear Research).
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.