Will Wei Sun

21 papers receiving 287 citations

Peers

Will Wei Sun
Comparison fields: 5 of 51
  • Computational Mathematics 134
  • Computational Mechanics 74
  • Statistics and Probability 28
  • Radiology, Nuclear Medicine and Imaging 75
  • Artificial Intelligence 95
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Namgil Lee South Korea
Brian Baingana United States
Beyza Ermiş Türkiye
Yunlong He United States
Vincent Q. Vu United States
Kai-Yang Chiang United States
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Weixiang Shao United States
Vassilis N. Ioannidis United States
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Citations per year

Countries citing papers authored by Will Wei Sun

Since Specialization
Citations

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

Fields of papers citing papers by Will Wei Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Will Wei Sun. 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 Will Wei Sun. The network helps show where Will Wei Sun may publish in the future.

Co-authors

The 22 scholars most cited alongside Will Wei Sun, 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 Will Wei Sun Line = papers co-authored together Will Wei Sun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201665
2 201839
3 201737
4
STORE: sparse tensor response regression and neuroimaging analysis
201719
5 201918
6 201918
7 202117
8
Provable Convex Co-clustering of Tensors.
202012
9 202211
10
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models.
201810
11 202310
12
Network Response Regression for Modeling Population of Networks with Covariates
20188
13 20158
14 20228
15 20233
16 20242
17 20242
18
Sparse Low-rank Tensor Response Regression
20161
19 20241
20 20221

About Will Wei Sun

Will Wei Sun is a scholar working on Computational Mathematics, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Management Science and Operations Research and Computational Mechanics, having authored 23 papers that have together received 292 indexed citations. Recurring topics across this work include Tensor decomposition and applications (10 papers), Advanced Neuroimaging Techniques and Applications (6 papers), Advanced Bandit Algorithms Research (6 papers), Sparse and Compressive Sensing Techniques (5 papers), Complex Network Analysis Techniques (3 papers), Consumer Market Behavior and Pricing (3 papers), Bayesian Methods and Mixture Models (3 papers) and Smart Grid Energy Management (3 papers). The work is most often cited by research in Computational Mathematics (134 citations), Computational Mechanics (74 citations), Statistics and Probability (28 citations), Radiology, Nuclear Medicine and Imaging (75 citations) and Artificial Intelligence (95 citations). Will Wei Sun has collaborated with scholars based in United States, Germany and Hong Kong. Frequent co-authors include Lexin Li, Guang Cheng, Han Liu, Jingfei Zhang, Junwei Lu, Binhuan Wang, Yilong Zhang, Yixin Fang, Jian Yang and Zhaoran Wang. Their work appears in journals such as Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Decision Support Systems, Journal of the Royal Statistical Society Series B (Statistical Methodology) and Operations Research.

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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