Tailin Wu
Impact in
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- Model Reduction and Neural Networks
Papers in
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- Computational Physics and Python Applications 2
- Neural Networks and Applications 2
- Adversarial Robustness in Machine Learning 2
- Evolutionary Algorithms and Applications 1
- Co-authors
- Max Tegmark (4 shared papers)Haoyuan Zhang (1 shared paper)Mengyin Fu (1 shared paper)Meiling Wang (1 shared paper)Jure Leskovec (3 shared papers)Richard Rines (1 shared paper)Hongyu Ren (1 shared paper)Guang Hao Low (1 shared paper)
- Journals
- Advanced Engineering Informatics (1 paper)BMC Bioinformatics (1 paper)New Journal of Physics (1 paper)Physical review. E (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)
- Partner nations
- United StatesChinaSaudi Arabia
In The Last Decade
Tailin Wu
11 papers receiving 120 citations
Peers
Comparison fields: 5 of 68
- Statistical and Nonlinear Physics 28
- Structural Biology 3
- Aging 3
- Artificial Intelligence 52
- 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 | 62 | |
| 2 | 2018 | 23 | |
| 3 | 2016 | 13 | |
| 4 | 2022 | 11 | |
| 5 | Graph Information Bottleneck | 2020 | 6 |
| 6 | 2022 | 4 | |
| 7 | AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity | 2020 | 1 |
| 8 | 2023 | 1 | |
| 9 | 2024 | 1 | |
| 10 | 2019 | 1 | |
| 11 | 2025 | 1 | |
| 12 | 2025 | 0 |
About Tailin Wu
Tailin Wu is a scholar working on Artificial Intelligence, Molecular Biology, Computer Networks and Communications, Information Systems and Management and Atomic and Molecular Physics, and Optics, having authored 12 papers that have together received 124 indexed citations. Recurring topics across this work include Computational Physics and Python Applications (2 papers), Neural Networks and Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers), Cell Image Analysis Techniques (1 paper), Time Series Analysis and Forecasting (1 paper), Computational Drug Discovery Methods (1 paper), Evolutionary Algorithms and Applications (1 paper) and Reservoir Engineering and Simulation Methods (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (28 citations), Structural Biology (3 citations), Aging (3 citations), Artificial Intelligence (52 citations) and Health Informatics (2 citations). Tailin Wu has collaborated with scholars based in United States, China and Saudi Arabia. Frequent co-authors include Max Tegmark, Haoyuan Zhang, Mengyin Fu, Meiling Wang, Jure Leskovec, Richard Rines, Hongyu Ren, Guang Hao Low, Isaac L. Chuang and Pan Li. Their work appears in journals such as Advanced Engineering Informatics, BMC Bioinformatics, New Journal of Physics, Physical review. E and Proceedings of the AAAI Conference on Artificial 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.