Sui Tang

26 papers receiving 250 citations

Peers

Sui Tang
Comparison fields: 5 of 69
  • Statistical and Nonlinear Physics 86
  • Modeling and Simulation 23
  • Applied Mathematics 46
  • Mathematical Physics 36
  • Statistics, Probability and Uncertainty 17
Replace Franca Hoffmann with:
Franca Hoffmann United States
Leonid Koralov United States
Yulong Lu United States
Aimé Lachal France
Oliver Tse Netherlands
Nicola Bruti‐Liberati Australia
Claudia Totzeck Germany
S. Martin Germany
Владимир Антонович Зорич Russia
Bálint Farkas Germany
Sui Tang relative to Franca Hoffmann United States Franca Hoffmann's profile →
Citations per field
00.5×2.7×
Franca Hoffmann · 1×
Citations per year

Countries citing papers authored by Sui Tang

Since Specialization
Citations

This map shows the geographic impact of Sui Tang'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 Sui Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sui Tang more than expected).

Fields of papers citing papers by Sui Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sui Tang. 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 Sui Tang. The network helps show where Sui Tang may publish in the future.

Co-authors

The 25 scholars most cited alongside Sui Tang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sui Tang Line = papers co-authored together Sui Tang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201956
2 201544
3 201422
4 202121
5
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
202118
6 201816
7 202014
8 202313
9 201611
10 20237
11 20186
12 20225
13 20155
14 19634
15 20233
16 20232
17 20172
18 20172
19 19882
20
Undersampled windowed exponentials, spectra of Toeplitz operators and its applications
20171

About Sui Tang

Sui Tang is a scholar working on Statistical and Nonlinear Physics, Applied Mathematics, Computational Mechanics, Artificial Intelligence and Mathematical Physics, having authored 28 papers that have together received 260 indexed citations. Recurring topics across this work include Mathematical Analysis and Transform Methods (6 papers), Sparse and Compressive Sensing Techniques (5 papers), Complex Network Analysis Techniques (4 papers), Model Reduction and Neural Networks (4 papers), Blind Source Separation Techniques (3 papers), Gaussian Processes and Bayesian Inference (3 papers), Advanced X-ray Imaging Techniques (2 papers) and Mathematical Biology Tumor Growth (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (86 citations), Modeling and Simulation (23 citations), Applied Mathematics (46 citations), Mathematical Physics (36 citations) and Statistics, Probability and Uncertainty (17 citations). Sui Tang has collaborated with scholars based in United States, China and Spain. Frequent co-authors include Mauro Maggioni, Fei Lu, Akram Aldroubi, Carlos Cabrelli, Ursula Molter, Wenjing Liao, Yonina C. Eldar, Yu Guo, Yangyi Zhang and Binbin Luo. Their work appears in journals such as Applied and Computational Harmonic Analysis, Applied Surface Science, Journal of Fourier Analysis and Applications, Information and Inference A Journal of the IMA and Proceedings of the National Academy of Sciences.

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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