Chong Wu

50 total papers · 431 total citations
24 papers, 297 citations indexed

About

Chong Wu is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Chong Wu has authored 24 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 6 papers in Molecular Biology and 6 papers in Computational Theory and Mathematics. Recurrent topics in Chong Wu's work include Medical Image Segmentation Techniques (5 papers), Bioinformatics and Genomic Networks (5 papers) and Machine Learning in Bioinformatics (4 papers). Chong Wu is often cited by papers focused on Medical Image Segmentation Techniques (5 papers), Bioinformatics and Genomic Networks (5 papers) and Machine Learning in Bioinformatics (4 papers). Chong Wu collaborates with scholars based in China, Hong Kong and Nigeria. Chong Wu's co-authors include Shuangshuang Qin, Le Zhang, Yuan Yuan, Yunjun Liu, Hong Yan, Luqi Huang, Tao Wu, Yidong Chen, Xiaodong Shi and Youguang Guo and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Industrial Electronics and IEEE Access.

In The Last Decade

Chong Wu

22 papers receiving 291 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Chong Wu	91	65	64	41	37	24	297
Kenta Yamamoto	31 0.3×	22 0.3×	83 1.3×	12 0.3×	10 0.3×	47	339
Cheng‐Chang Lien	186 2.0×	48 0.7×	6 0.1×	43 1.0×	46 1.2×	47	352
Sathish	104 1.1×	33 0.5×	12 0.2×	14 0.3×	37 1.0×	13	344
Karun Verma	125 1.4×	50 0.8×	44 0.7×	22 0.5×	65 1.8×	26	362
Harjeet Singh	92 1.0×	70 1.1×	11 0.2×	25 0.6×	53 1.4×	29	302
Peter Sýkora	184 2.0×	21 0.3×	11 0.2×	32 0.8×	17 0.5×	27	318
Dong-Youn Kim	62 0.7×	61 0.9×	11 0.2×	75 1.8×	5 0.1×	41	299
Karen Das	94 1.0×	25 0.4×	9 0.1×	107 2.6×	26 0.7×	22	258
Varsha Singh	43 0.5×	135 2.1×	9 0.1×	96 2.3×	7 0.2×	20	306
Usha Mittal	126 1.4×	77 1.2×	4 0.1×	18 0.4×	50 1.4×	28	348

Countries citing papers authored by Chong Wu

Since Specialization

Citations

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

Fields of papers citing papers by Chong Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chong Wu

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

All Works

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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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