Tailin Wu
Impact in
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- Model Reduction and Neural Networks
Papers in
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- Adversarial Robustness in Machine Learning 2
- Neural Networks and Applications 2
- Computational Physics and Python Applications 2
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- Geomagnetism and Paleomagnetism Studies 1
- Co-authors
- Max Tegmark (4 shared papers)Meiling Wang (1 shared paper)Mengyin Fu (1 shared paper)Haoyuan Zhang (1 shared paper)Jure Leskovec (3 shared papers)Isaac L. Chuang (2 shared papers)Pan Li (1 shared paper)Hongyu Ren (1 shared paper)
- Journals
- Advanced Engineering Informatics (1 paper)Physical review. E (1 paper)Physics of Fluids (1 paper)New Journal of Physics (1 paper)BMC Bioinformatics (1 paper)
- Partner nations
- United StatesChinaSpain
In The Last Decade
Tailin Wu
12 papers receiving 136 citations
Peers
Comparison fields: 5 of 72
- Aging 5
- Statistical and Nonlinear Physics 31
- Structural Biology 3
- Artificial Intelligence 59
- Health Informatics 2
Countries citing papers authored by Tailin Wu
This map shows the geographic impact of Tailin Wu'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 Tailin Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tailin Wu more than expected).
Fields of papers citing papers by Tailin Wu
This network shows the impact of papers produced by Tailin Wu. 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 Tailin Wu. The network helps show where Tailin Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Tailin Wu, 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 | 2019 | 68 | |
| 2 | 2018 | 25 | |
| 3 | 2016 | 14 | |
| 4 | 2022 | 14 | |
| 5 | 2022 | 7 | |
| 6 | Graph Information Bottleneck | 2020 | 6 |
| 7 | 2024 | 2 | |
| 8 | AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity | 2020 | 1 |
| 9 | 2025 | 1 | |
| 10 | 2023 | 1 | |
| 11 | 2025 | 1 | |
| 12 | 2019 | 1 | |
| 13 | 2025 | 0 |
About Tailin Wu
Tailin Wu is a scholar working on Artificial Intelligence, Molecular Biology, Computer Networks and Communications, Computer Vision and Pattern Recognition and Information Systems and Management, having authored 13 papers that have together received 141 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Neural Networks and Applications (2 papers), Computational Physics and Python Applications (2 papers), Image and Signal Denoising Methods (1 paper), Atomic and Subatomic Physics Research (1 paper), Geomagnetism and Paleomagnetism Studies (1 paper), Geophysical and Geoelectrical Methods (1 paper) and Genetics, Aging, and Longevity in Model Organisms (1 paper). The work is most often cited by research in Aging (5 citations), Statistical and Nonlinear Physics (31 citations), Structural Biology (3 citations), Artificial Intelligence (59 citations) and Health Informatics (2 citations). Tailin Wu has collaborated with scholars based in United States, China and Spain. Frequent co-authors include Max Tegmark, Meiling Wang, Mengyin Fu, Haoyuan Zhang, Jure Leskovec, Isaac L. Chuang, Pan Li, Hongyu Ren, Guang Hao Low and Jules Stuart. Their work appears in journals such as Advanced Engineering Informatics, Physical review. E, Physics of Fluids, New Journal of Physics and BMC Bioinformatics.
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.