Jason Xu
Impact in
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
-
- Parasitic Infections and Diagnostics
Papers in
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- COVID-19 epidemiological studies 5
- Co-authors
- Kenneth LangeVladimir N. MininAlexander R. ParedezWang WangChuanfeng WuAndrew M. SheltonCynthia E. DunbarSwagatam Das
- Journals
- Proceedings of the National Academy of Sciences (2 papers)Journal of the American Statistical Association (2 papers)Biometrics (2 papers)PLoS ONE (2 papers)Computational Optimization and Applications (1 paper)
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Jason Xu
34 papers receiving 425 citations
Peers
Comparison fields: 5 of 120
- Modeling and Simulation 26
- Parasitology 28
- Statistics and Probability 25
- Hematology 29
- Molecular Biology 178
Countries citing papers authored by Jason Xu
This map shows the geographic impact of Jason Xu'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 Jason Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Xu more than expected).
Fields of papers citing papers by Jason Xu
This network shows the impact of papers produced by Jason Xu. 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 Jason Xu. The network helps show where Jason Xu may publish in the future.
Co-authors
The 25 scholars most cited alongside Jason Xu, 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 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2022 | 3 | |
| 5 | 2022 | 70 | |
| 6 | 2022 | 9 | |
| 7 | 2022 | 3 | |
| 8 | 2022 | 3 | |
| 9 | 2021 | 8 | |
| 10 | 2021 | 1 | |
| 11 | 2021 | 10 | |
| 12 | 2021 | 1 | |
| 13 | Entropy Weighted Power k-Means Clustering. | 2020 | 20 |
| 14 | 2019 | 8 | |
| 15 | Power k-Means Clustering | 2019 | 23 |
| 16 | Projection onto Minkowski Sums with Application to Constrained Learning. | 2019 | 6 |
| 17 | 2017 | 41 | |
| 18 | 2017 | 37 | |
| 19 | Generalized Linear Model Regression under Distance-to-set Penalties | 2017 | 3 |
| 20 | 2017 | 25 |
About Jason Xu
Jason Xu is a scholar working on Modeling and Simulation, Numerical Analysis, Statistics and Probability, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 39 papers that have together received 434 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (6 papers), COVID-19 epidemiological studies (5 papers), Face and Expression Recognition (4 papers), Single-cell and spatial transcriptomics (3 papers), Bayesian Methods and Mixture Models (3 papers), Hematopoietic Stem Cell Transplantation (3 papers), Evolution and Genetic Dynamics (3 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Modeling and Simulation (26 citations), Parasitology (28 citations), Statistics and Probability (25 citations), Hematology (29 citations) and Molecular Biology (178 citations). Jason Xu has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Kenneth Lange, Vladimir N. Minin, Alexander R. Paredez, Wang Wang, Chuanfeng Wu, Andrew M. Shelton, Cynthia E. Dunbar, Swagatam Das, Samson Koelle and Stephen C. Kolwicz. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association, Biometrics, PLoS ONE and Computational Optimization and Applications.
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.