Fred Lu
Impact in
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies
Papers in
-
- Data-Driven Disease Surveillance 8
- Influenza Virus Research Studies 6
-
- COVID-19 epidemiological studies 6
- Co-authors
- Mauricio Santillana (9 shared papers)John S. Brownstein (2 shared papers)Matthew Biggerstaff (1 shared paper)Mohammad W. Hattab (1 shared paper)Shihao Yang (1 shared paper)S. C. Kou (1 shared paper)Nicholas Brooke (1 shared paper)Leonardo Clemente (4 shared papers)
- Journals
- PLoS Computational Biology (3 papers)Nature Communications (2 papers)JMIR Public Health and Surveillance (2 papers)Epidemics (1 paper)Nature Machine Intelligence (1 paper)
- Partner nations
- United StatesMexicoBelgium
In The Last Decade
Fred Lu
19 papers receiving 324 citations
Peers
Comparison fields: 5 of 93
- Modeling and Simulation 137
- Health Informatics 11
- Epidemiology 184
- Health 19
- Public Health, Environmental and Occupational Health 53
Countries citing papers authored by Fred Lu
This map shows the geographic impact of Fred Lu'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 Fred Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fred Lu more than expected).
Fields of papers citing papers by Fred Lu
This network shows the impact of papers produced by Fred Lu. 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 Fred Lu. The network helps show where Fred Lu may publish in the future.
Co-authors
The 25 scholars most cited alongside Fred Lu, 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 | 2017 | 67 | |
| 2 | 2018 | 67 | |
| 3 | 2019 | 65 | |
| 4 | 2021 | 29 | |
| 5 | 2019 | 20 | |
| 6 | 2021 | 19 | |
| 7 | 2022 | 12 | |
| 8 | 2022 | 11 | |
| 9 | 2022 | 9 | |
| 10 | 2022 | 9 | |
| 11 | 1991 | 7 | |
| 12 | 2023 | 4 | |
| 13 | 2025 | 2 | |
| 14 | 2025 | 1 | |
| 15 | 2024 | 1 | |
| 16 | 2023 | 1 | |
| 17 | 1989 | 1 | |
| 18 | 2023 | 1 | |
| 19 | 2022 | 1 | |
| 20 | 2024 | 0 |
About Fred Lu
Fred Lu is a scholar working on Epidemiology, Modeling and Simulation, Artificial Intelligence, Molecular Biology and Public Health, Environmental and Occupational Health, having authored 20 papers that have together received 327 indexed citations. Recurring topics across this work include Data-Driven Disease Surveillance (8 papers), Influenza Virus Research Studies (6 papers), COVID-19 epidemiological studies (6 papers), Mosquito-borne diseases and control (3 papers), Face and Expression Recognition (2 papers), Adversarial Robustness in Machine Learning (2 papers), Genetic Associations and Epidemiology (2 papers) and Advanced Malware Detection Techniques (2 papers). The work is most often cited by research in Modeling and Simulation (137 citations), Health Informatics (11 citations), Epidemiology (184 citations), Health (19 citations) and Public Health, Environmental and Occupational Health (53 citations). Fred Lu has collaborated with scholars based in United States, Mexico and Belgium. Frequent co-authors include Mauricio Santillana, John S. Brownstein, Matthew Biggerstaff, Mohammad W. Hattab, Shihao Yang, S. C. Kou, Nicholas Brooke, Leonardo Clemente, Rok Sosič and Josh Gray. Their work appears in journals such as PLoS Computational Biology, Nature Communications, JMIR Public Health and Surveillance, Epidemics and Nature Machine Intelligence.
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.