Fred Lu

862 total citations
20 papers, 327 citations indexed

About

Fred Lu is a scholar working on Epidemiology, Modeling and Simulation and Artificial Intelligence. According to data from OpenAlex, Fred Lu has authored 20 papers receiving a total of 327 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Epidemiology, 6 papers in Modeling and Simulation and 5 papers in Artificial Intelligence. Recurrent topics in Fred Lu's work include Data-Driven Disease Surveillance (8 papers), Influenza Virus Research Studies (6 papers) and COVID-19 epidemiological studies (6 papers). Fred Lu is often cited by papers focused on Data-Driven Disease Surveillance (8 papers), Influenza Virus Research Studies (6 papers) and COVID-19 epidemiological studies (6 papers). Fred Lu collaborates with scholars based in United States, Mexico and Belgium. Fred Lu's co-authors include Mauricio Santillana, John S. Brownstein, Mohammad W. Hattab, Matthew Biggerstaff, S. C. Kou, Shihao Yang, Nicholas Brooke, Leonardo Clemente, Jure Leskovec and André T. Nguyen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Fred Lu

19 papers receiving 324 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fred Lu United States	10	184	137	53	53	30	20	327
Logan Brooks United States	9	315 1.7×	333 2.4×	54 1.0×	56 1.1×	67 2.2×	12	524
Sangwon Hyun United States	7	186 1.0×	178 1.3×	33 0.6×	41 0.8×	31 1.0×	11	303
André T. Nguyen United States	9	395 2.1×	221 1.6×	83 1.6×	54 1.0×	35 1.2×	15	616
Todd Bodnar United States	6	204 1.1×	84 0.6×	76 1.4×	55 1.0×	30 1.0×	10	435
Sangeeta Bhatia United Kingdom	9	85 0.5×	114 0.8×	14 0.3×	37 0.7×	117 3.9×	26	328
Canelle Poirier United States	9	106 0.6×	239 1.7×	36 0.7×	29 0.5×	67 2.2×	10	340
Carl Koppeschaar Portugal	10	371 2.0×	160 1.2×	19 0.4×	57 1.1×	24 0.8×	11	481
Yu-Tsen Yeh United Kingdom	14	42 0.2×	73 0.5×	29 0.5×	64 1.2×	38 1.3×	26	381
J.-A. Moraño Spain	11	83 0.5×	89 0.6×	61 1.2×	63 1.2×	34 1.1×	29	402
Vivian Wan In Wei Hong Kong	7	115 0.6×	202 1.5×	13 0.2×	35 0.7×	81 2.7×	9	320

Countries citing papers authored by Fred Lu

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Fred Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Fred Lu. A scholar is included among the top collaborators of Fred Lu 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 Fred Lu. Fred Lu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Meyer, Austin G., et al.. (2025). Ensemble approaches for short-term dengue fever forecasts: A global evaluation study. Proceedings of the National Academy of Sciences. 122(33). e2422335122–e2422335122. 1 indexed citations

Meyer, Austin G., Fred Lu, Leonardo Clemente, & Mauricio Santillana. (2025). A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data. Epidemics. 50. 100816–100816. 2 indexed citations

Lu, Fred, et al.. (2024). High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. arXiv (Cornell University). 2037–2047. 1 indexed citations

Gupta, Siddhant, Fred Lu, Andrew Barlow, et al.. (2024). Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection. 2624–2634.

Lu, Fred, et al.. (2023). Differentially Private Logistic Regression with Sparse Solutions. 1–9. 4 indexed citations

Lu, Fred, et al.. (2023). A Coreset Learning Reality Check. Proceedings of the AAAI Conference on Artificial Intelligence. 37(7). 8940–8948. 1 indexed citations

Muñoz, Joseph, et al.. (2023). Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile. 23–33. 1 indexed citations

Salazar, Pablo M. De, Fred Lu, James A. Hay, et al.. (2022). Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data. PLoS Computational Biology. 18(3). e1009964–e1009964. 11 indexed citations

Lu, Fred, et al.. (2022). Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil. PLoS neglected tropical diseases. 16(1). e0010071–e0010071. 9 indexed citations

10.

Kassani, Peyman Hosseinzadeh, Fred Lu, Yann Le Guen, Michaël E. Belloy, & Zihuai He. (2022). Deep neural networks with controlled variable selection for the identification of putative causal genetic variants. Nature Machine Intelligence. 4(9). 761–771. 9 indexed citations

11.

Nguyen, André T., et al.. (2022). Out of Distribution Data Detection Using Dropout Bayesian Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 7877–7885. 12 indexed citations

12.

Lu, Fred, et al.. (2022). Deep learning-assisted genome-wide characterization of massively parallel reporter assays. Nucleic Acids Research. 50(20). 11442–11454. 1 indexed citations

13.

He, Zihuai, Chen Wang, Yann Le Guen, et al.. (2021). Identification of putative causal loci in whole-genome sequencing data via knockoff statistics. Nature Communications. 12(1). 3152–3152. 19 indexed citations

14.

Lu, Fred, André T. Nguyen, Nicholas Link, et al.. (2021). Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches. PLoS Computational Biology. 17(6). e1008994–e1008994. 29 indexed citations

15.

Clemente, Leonardo, Fred Lu, & Mauricio Santillana. (2019). Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries. JMIR Public Health and Surveillance. 5(2). e12214–e12214. 20 indexed citations

16.

Lu, Fred, et al.. (2019). Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nature Communications. 10(1). 147–147. 65 indexed citations

17.

Lu, Fred, Kristin Baltrusaitis, Manan Shah, et al.. (2018). Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis. JMIR Public Health and Surveillance. 4(1). e4–e4. 67 indexed citations

18.

Yang, Shihao, S. C. Kou, Fred Lu, et al.. (2017). Advances in using Internet searches to track dengue. PLoS Computational Biology. 13(7). e1005607–e1005607. 67 indexed citations

19.

Perry, C. H., Ling Ma, Fred Lu, et al.. (1991). Photoluminescence in GaAsAlGaAs coupled double quantum wells in electric and magnetic fields. Journal of Luminescence. 48-49. 725–730. 7 indexed citations

20.

Lu, Fred, et al.. (1989). Anisotropy in the Far Infrared Conductivity of Magnetically-Oriented YBa₂Cu₃O_7-δ Between 50–300K. MRS Proceedings. 169. 1 indexed citations

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.

Explore authors with similar magnitude of impact