Tailin Wu

703 total citations
12 papers, 124 citations indexed

About

Tailin Wu is a scholar working on Artificial Intelligence, Molecular Biology and Computer Networks and Communications. According to data from OpenAlex, Tailin Wu has authored 12 papers receiving a total of 124 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Molecular Biology and 1 paper in Computer Networks and Communications. Recurrent topics in Tailin Wu's work include Neural Networks and Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Computational Physics and Python Applications (2 papers). Tailin Wu is often cited by papers focused on Neural Networks and Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Computational Physics and Python Applications (2 papers). Tailin Wu collaborates with scholars based in United States, China and Saudi Arabia. Tailin Wu's co-authors include Max Tegmark, Mengyin Fu, Meiling Wang, Haoyuan Zhang, Jure Leskovec, Isaac L. Chuang, Guang Hao Low, Richard Rines, Pan Li and Hongyu Ren and has published in prestigious journals such as BMC Bioinformatics, New Journal of Physics and Advanced Engineering Informatics.

In The Last Decade

Tailin Wu

11 papers receiving 120 citations

Peers

Tailin Wu
Zheng Ma United States
Stefano Spigler France
Dirk Graudenz Germany
A. Van Moffaert Belgium
Pierre Simon Laplace
Dustin Anderson United States
Ágnes Fülöp Hungary
I. Reichardt Spain
Zheng Ma United States
Tailin Wu
Citations per year, relative to Tailin Wu Tailin Wu (= 1×) peers Zheng Ma

Countries citing papers authored by Tailin Wu

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Tailin Wu

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

All Works

12 of 12 papers shown
1.
Zhao, Yingxue, et al.. (2025). Recurrent U-Net-based Graph Neural Network (RUGNN) for accurate deformation predictions in sheet material forming. Advanced Engineering Informatics. 69. 104021–104021.
2.
Wu, Lirong, Haitao Lin, Yufei Huang, et al.. (2025). Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization. Proceedings of the AAAI Conference on Artificial Intelligence. 39(1). 895–904. 1 indexed citations
3.
Wu, Tailin, et al.. (2024). Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. Proceedings of the AAAI Conference on Artificial Intelligence. 38(1). 320–328. 1 indexed citations
4.
Wu, Tailin, et al.. (2023). Study on Intelligent door lock system based on Internet of Things and intelligent identification technology. Highlights in Science Engineering and Technology. 56. 243–245. 1 indexed citations
5.
Wu, Tailin, et al.. (2022). Toward a more accurate 3D atlas of C. elegans neurons. BMC Bioinformatics. 23(1). 195–195. 4 indexed citations
6.
Wu, Tailin, Yinan Zhang, Rex Ying, et al.. (2022). Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4184–4194. 11 indexed citations
7.
Udrescu, Silviu‐Marian, et al.. (2020). AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. Neural Information Processing Systems. 33. 4860–4871. 1 indexed citations
8.
Wu, Tailin, Hongyu Ren, Pan Li, & Jure Leskovec. (2020). Graph Information Bottleneck. Neural Information Processing Systems. 33. 20437–20448. 6 indexed citations
9.
Wu, Tailin & Max Tegmark. (2019). Toward an artificial intelligence physicist for unsupervised learning. Physical review. E. 100(3). 62 indexed citations
10.
Wu, Tailin, Ian Fischer, Isaac L. Chuang, & Max Tegmark. (2019). Learnability for the Information Bottleneck. DSpace@MIT (Massachusetts Institute of Technology). 1050–1060. 1 indexed citations
11.
Wu, Tailin, et al.. (2018). Recurrent Neural Networks based on LSTM for Predicting Geomagnetic Field. 23 indexed citations
12.
Low, Guang Hao, et al.. (2016). Iterative precision measurement of branching ratios applied to 5Pstates in88Sr+. New Journal of Physics. 18(12). 123021–123021. 13 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.

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2026