Mark Chen
- Modeling and Simulation top 0.1%
- COVID-19 epidemiological studies 43
- Infectious Diseases top 0.2%
- SARS-CoV-2 and COVID-19 Research 25
- HIV/AIDS Research and Interventions 18
- COVID-19 Clinical Research Studies 12
- Applied Psychology top 0.5%
- General Decision Sciences top 1%
- Social Psychology top 0.2%
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- Influenza Virus Research Studies 53
- Respiratory viral infections research 33
- HIV, Drug Use, Sexual Risk 15
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- Animal Disease Management and Epidemiology 16
Mark Chen
218 papers receiving 13.3k citations
Hit Papers
Peers
Comparison fields: 5 of 218
- Modeling and Simulation 1.2k
- Infectious Diseases 3.7k
- Applied Psychology 965
- General Decision Sciences 237
- Social Psychology 2.4k
Countries citing papers authored by Mark Chen
This map shows the geographic impact of Mark Chen'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 Mark Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Chen more than expected).
Fields of papers citing papers by Mark Chen
This network shows the impact of papers produced by Mark Chen. 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 Mark Chen. The network helps show where Mark Chen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mark Chen, 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 | 5 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 19 | |
| 4 | 2022 | 1 | |
| 5 | 2022 | 2 | |
| 6 | 2022 | 4 | |
| 7 | Viral Load of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Respiratory Aerosols Emitted by Patients With Coronavirus Disease 2019 (COVID-19) While Breathing, Talking, and Singingbreakdown → | 2021 | 162 |
| 8 | 2021 | 16 | |
| 9 | 2021 | 93 | |
| 10 | 2021 | 1 | |
| 11 | 2021 | 72 | |
| 12 | Zero-Shot Text-to-Image Generation | 2021 | 6 |
| 13 | 2020 | 70 | |
| 14 | 2020 | 10 | |
| 15 | Distribution Augmentation for Generative Modeling | 2020 | 8 |
| 16 | 2019 | 24 | |
| 17 | 2018 | 10 | |
| 18 | 2012 | 7 | |
| 19 | Modeling and Measuring Engagement in Computer Games. | 2005 | 6 |
| 20 | 2000 | 141 |
About Mark Chen
Mark Chen is a scholar working on Modeling and Simulation, Infectious Diseases, Epidemiology, Applied Microbiology and Biotechnology and Agronomy and Crop Science, having authored 221 papers that have together received 13.9k indexed citations. Recurring topics across this work include Influenza Virus Research Studies (53 papers), COVID-19 epidemiological studies (43 papers), Respiratory viral infections research (33 papers), SARS-CoV-2 and COVID-19 Research (25 papers), HIV/AIDS Research and Interventions (18 papers), Animal Disease Management and Epidemiology (16 papers), HIV, Drug Use, Sexual Risk (15 papers) and COVID-19 Clinical Research Studies (12 papers). The work is most often cited by research in Modeling and Simulation (1.2k citations), Infectious Diseases (3.7k citations), Applied Psychology (965 citations), General Decision Sciences (237 citations) and Social Psychology (2.4k citations). Mark Chen has collaborated with scholars based in Singapore, United States and Australia. Frequent co-authors include John A. Bargh, Lara J. Burrows, Robert Lam, Dean Ho, Eiji Ōsawa, Yee‐Sin Leo, Lin‐Fa Wang, Alex R. Cook, Wan Ni Chia and Barnaby Edward Young. Their work appears in journals such as PLoS ONE, Emerging infectious diseases, Sexually Transmitted Infections, Influenza and Other Respiratory Viruses and BMC Infectious Diseases.
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.