Weichen Wang

874 citations
22 papers · 466 indexed · h-index 9

Impact in

Papers in

Weichen Wang

20 papers receiving 455 citations

Peers

Weichen Wang
Comparison fields: 5 of 87
  • Statistics and Probability 162
  • Computational Mathematics 6
  • Finance 57
  • Cancer Research 60
  • Signal Processing 35
Replace Beatrix Jones with:
Beatrix Jones New Zealand
Hrvoje Šikić Croatia
Jorge M. Arevalillo Spain
Grace S. Shieh Taiwan
Ker-Chau Li United States
Matthias Kirchner Germany
Robert Bartôszyński United States
Wenyu Jiang Canada
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Citations per field
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Citations per year

Countries citing papers authored by Weichen Wang

Since Specialization
Citations

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

Fields of papers citing papers by Weichen Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Weichen Wang, 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 Weichen Wang Line = papers co-authored together Weichen Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201798
2 202276
3 201555
4 201847
5 201241
6 201839
7 202139
8
An l Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation.
201813
9 202110
10 20257
11
[Two novel EIF2AK3 mutations in a Chinese boy with Wolcott-Rallison syndrome].
20117
12 20186
13 20226
14 20115
15 20245
16 20145
17 20243
18 20222
19 20231
20 20231

About Weichen Wang

Weichen Wang is a scholar working on Statistics and Probability, Molecular Biology, Artificial Intelligence, Genetics and Cancer Research, having authored 22 papers that have together received 466 indexed citations. Recurring topics across this work include Statistical Methods and Inference (6 papers), Statistical Methods and Bayesian Inference (4 papers), Advanced Statistical Methods and Models (4 papers), MicroRNA in disease regulation (2 papers), Neural Networks and Applications (2 papers), Ferroptosis and cancer prognosis (2 papers), RNA modifications and cancer (2 papers) and Sparse and Compressive Sensing Techniques (2 papers). The work is most often cited by research in Statistics and Probability (162 citations), Computational Mathematics (6 citations), Finance (57 citations), Cancer Research (60 citations) and Signal Processing (35 citations). Weichen Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jianqing Fan, Yiqiao Zhong, Han Liu, Ziwei Zhu, Xuegong Zhang, Maoyu Wang, Peipei Gong, Bing Wu, Zhixing Feng and Xi Wang. Their work appears in journals such as The Annals of Statistics, Journal of Econometrics, Chinese Journal of Chemistry, Gene and Frontiers in Immunology.

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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