Serina Chang
Impact in
- Modeling and Simulation top 0.5%
- COVID-19 epidemiological studies
- Transportation top 2%
- Human Mobility and Location-Based Analysis
- Urban Transport and Accessibility
Papers in
-
- Topic Modeling 3
- Natural Language Processing Techniques 2
-
- Data-Driven Disease Surveillance 2
- Co-authors
- Jure Leskovec (6 shared papers)Emma Pierson (3 shared papers)Pang Wei Koh (2 shared papers)Beth Redbird (2 shared papers)David B. Grusky (2 shared papers)Jaline Gerardin (2 shared papers)Kathy McKeown (3 shared papers)Dan Jurafsky (1 shared paper)
- Journals
- Nature Communications (1 paper)Nature (1 paper)Journal of the American Medical Informatics Association (1 paper)Physical Review Research (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United StatesJapanSouth Korea
In The Last Decade
Serina Chang
14 papers receiving 1.1k citations
Serina Chang's Hit Papers
Peers
Comparison fields: 5 of 101
- Modeling and Simulation 625
- Transportation 291
- Health 88
- Economics and Econometrics 208
- Epidemiology 246
Countries citing papers authored by Serina Chang
This map shows the geographic impact of Serina Chang'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 Serina Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Serina Chang more than expected).
Fields of papers citing papers by Serina Chang
This network shows the impact of papers produced by Serina Chang. 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 Serina Chang. The network helps show where Serina Chang may publish in the future.
Co-authors
The 25 scholars most cited alongside Serina Chang, 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 | Mobility network models of COVID-19 explain inequities and inform reopening Hit paper breakdown → | 2020 | 958 |
| 2 | 2022 | 35 | |
| 3 | 2021 | 19 | |
| 4 | 2009 | 14 | |
| 5 | 2017 | 13 | |
| 6 | 2018 | 11 | |
| 7 | 2021 | 7 | |
| 8 | 2023 | 6 | |
| 9 | 2019 | 4 | |
| 10 | 2022 | 4 | |
| 11 | 2025 | 2 | |
| 12 | 2025 | 2 | |
| 13 | 2024 | 1 | |
| 14 | 2022 | 1 | |
| 15 | 2025 | 1 | |
| 16 | 2025 | 0 |
About Serina Chang
Serina Chang is a scholar working on Artificial Intelligence, Epidemiology, Transportation, Modeling and Simulation and Sociology and Political Science, having authored 16 papers that have together received 1.1k indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (4 papers), Human Mobility and Location-Based Analysis (3 papers), Topic Modeling (3 papers), Natural Language Processing Techniques (2 papers), Vaccine Coverage and Hesitancy (2 papers), Computational and Text Analysis Methods (2 papers), Data-Driven Disease Surveillance (2 papers) and Opinion Dynamics and Social Influence (2 papers). The work is most often cited by research in Modeling and Simulation (625 citations), Transportation (291 citations), Health (88 citations), Economics and Econometrics (208 citations) and Epidemiology (246 citations). Serina Chang has collaborated with scholars based in United States, Japan and South Korea. Frequent co-authors include Jure Leskovec, Emma Pierson, Pang Wei Koh, Beth Redbird, David B. Grusky, Jaline Gerardin, Kathy McKeown, Dan Jurafsky, Leah Platt Boustan and Rob Voigt. Their work appears in journals such as Nature Communications, Nature, Journal of the American Medical Informatics Association, Physical Review Research and Proceedings of the National Academy of Sciences.
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.