Maya L. Petersen
- Infectious Diseases top 1%
- Epidemiology top 2%
- Statistics and Probability top 0.5%
- General Health Professions top 2%
- Economics and Econometrics top 2%
- Co-authors
- Mark J. van der LaanSandra E. SinisiDiane V. HavlirMoses R. KamyaEdwin D. CharleboisYue WangDalsone KwarisiimaTamara D. Clark
- Topics
- HIV/AIDS Research and Interventions (62 papers)Advanced Causal Inference Techniques (35 papers)HIV, Drug Use, Sexual Risk (32 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of the American Statistical AssociationPLoS ONE
- Partner nations
- United StatesUgandaKenya
In The Last Decade
Maya L. Petersen
112 papers receiving 3.5k citations
Peers
Comparison fields: 5 of 159
- Infectious Diseases 1.5k
- Epidemiology 997
- Statistics and Probability 949
- General Health Professions 740
- Economics and Econometrics 488
Countries citing papers authored by Maya L. Petersen
This map shows the geographic impact of Maya L. Petersen'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 Maya L. Petersen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya L. Petersen more than expected).
Fields of papers citing papers by Maya L. Petersen
This network shows the impact of papers produced by Maya L. Petersen. 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 Maya L. Petersen. The network helps show where Maya L. Petersen may publish in the future.
Co-authorship network of co-authors of Maya L. Petersen
This figure shows the co-authorship network connecting the top 25 collaborators of Maya L. Petersen. A scholar is included among the top collaborators of Maya L. Petersen 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 Maya L. Petersen. Maya L. Petersen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 3 | |
| 7 | 8 | |
| 8 | 3 | |
| 9 | 4 | |
| 10 | 21 | |
| 11 | 7 | |
| 12 | 4 | |
| 13 | 7 | |
| 14 | 17 | |
| 15 | 56 | |
| 16 | 65 | |
| 17 | 35 | |
| 18 | 22 | |
| 19 | 26 | |
| 20 | 35 |
About Maya L. Petersen
Maya L. Petersen is a scholar working on Virology, Statistics and Probability and Infectious Diseases, having authored 118 papers that have together received 3.6k indexed citations. Recurring topics across this work include HIV/AIDS Research and Interventions (62 papers), Advanced Causal Inference Techniques (35 papers) and HIV, Drug Use, Sexual Risk (32 papers). The work is most often cited by research in Statistics and Probability (949 citations), Infectious Diseases (1.5k citations) and Virology (349 citations). Maya L. Petersen has collaborated with scholars based in United States, Uganda and Kenya. Frequent co-authors include Mark J. van der Laan, Sandra E. Sinisi, Diane V. Havlir, Moses R. Kamya, Edwin D. Charlebois, Yue Wang, Dalsone Kwarisiima, Tamara D. Clark, Laura B. Balzer and Kristin E. Porter. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and PLoS ONE.
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.