Shu‐Yin Chiang

50 total papers · 880 total citations
35 papers, 591 citations indexed

About

Shu‐Yin Chiang is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering and Aerospace Engineering. According to data from OpenAlex, Shu‐Yin Chiang has authored 35 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 11 papers in Industrial and Manufacturing Engineering and 8 papers in Aerospace Engineering. Recurrent topics in Shu‐Yin Chiang's work include Scheduling and Optimization Algorithms (11 papers), Robotics and Sensor-Based Localization (8 papers) and Advanced Queuing Theory Analysis (7 papers). Shu‐Yin Chiang is often cited by papers focused on Scheduling and Optimization Algorithms (11 papers), Robotics and Sensor-Based Localization (8 papers) and Advanced Queuing Theory Analysis (7 papers). Shu‐Yin Chiang collaborates with scholars based in Taiwan, United States and South Korea. Shu‐Yin Chiang's co-authors include Semyon M. Meerkov, C.-T. Kuo, J.-T. Lim, Yao‐Chiang Kan, Jingshan Li, Ming-Che Lee, Sheng‐Cheng Yeh, Kai Lee, Jinlong Wang and Jared Reynolds and has published in prestigious journals such as Sensors, International Journal of Production Research and IEEE Transactions on Robotics and Automation.

In The Last Decade

Shu‐Yin Chiang

35 papers receiving 566 citations

Author Peers

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

Author						Papers	Cites
Shu‐Yin Chiang	371	187	77	62	54	35	591
Maurício C. de Souza	342 0.9×	56 0.3×	54 0.7×	14 0.2×	23 0.4×	50	631
Nur Evin Özdemirel	435 1.2×	50 0.3×	80 1.0×	41 0.7×	11 0.2×	27	673
András Pfeiffer	519 1.4×	143 0.8×	105 1.4×	12 0.2×	18 0.3×	36	686
D. J. Medeiros	339 0.9×	116 0.6×	160 2.1×	36 0.6×	6 0.1×	53	637
Justyna Zander	148 0.4×	62 0.3×	87 1.1×	25 0.4×	28 0.5×	34	543
Velusamy Subramaniam	532 1.4×	101 0.5×	40 0.5×	9 0.1×	71 1.3×	32	689
Michael Masin	224 0.6×	68 0.4×	120 1.6×	15 0.2×	26 0.5×	35	531
Si̇nan Gürel	405 1.1×	37 0.2×	108 1.4×	15 0.2×	24 0.4×	31	665
Russell S. Peak	254 0.7×	91 0.5×	103 1.3×	13 0.2×	52 1.0×	52	534
Shih‐Cheng Horng	184 0.5×	42 0.2×	81 1.1×	26 0.4×	17 0.3×	71	567

Countries citing papers authored by Shu‐Yin Chiang

Since Specialization

Citations

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

Fields of papers citing papers by Shu‐Yin Chiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shu‐Yin Chiang. 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 Shu‐Yin Chiang. The network helps show where Shu‐Yin Chiang may publish in the future.

Co-authorship network of co-authors of Shu‐Yin Chiang

This figure shows the co-authorship network connecting the top 25 collaborators of Shu‐Yin Chiang. A scholar is included among the top collaborators of Shu‐Yin Chiang 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 Shu‐Yin Chiang. Shu‐Yin Chiang 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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