Chia-Chien Wu

785 citations
32 papers · 512 · h-index 12

Impact in

Papers in

Chia-Chien Wu

31 papers receiving 499 citations

Peers

Chia-Chien Wu
Comparison fields: 5 of 105
  • Human-Computer Interaction 69
  • Cognitive Neuroscience 208
  • Sensory Systems 34
  • Computer Vision and Pattern Recognition 126
  • Experimental and Cognitive Psychology 50
Replace Jeffrey S. Johnson with:
Jeffrey S. Johnson United States
Sven‐Thomas Graupner Germany
Christoph Strauch Netherlands
Ian van der Linde United Kingdom
Allen M. Y. Cheong Hong Kong
Ho-Wan Kwak South Korea
Eri Nakagawa Japan
Stephen H. Butler United Kingdom
Shun‐nan Yang United States
Joseph L. Brooks United Kingdom
Chia-Chien Wu relative to Jeffrey S. Johnson United States Jeffrey S. Johnson's profile →
Citations per field
00.5×1.5×2.5×
Jeffrey S. Johnson · 1×
Citations per year

Countries citing papers authored by Chia-Chien Wu

Since Specialization
Citations

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

Fields of papers citing papers by Chia-Chien Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014102
2 201283
3 201869
4 201050
5 201329
6 201927
7 201423
8 202315
9 201814
10 202113
11 202313
12 202112
13 20228
14 20178
15 20217
16 20196
17 20164
18 20244
19 20224
20 20243

About Chia-Chien Wu

Chia-Chien Wu is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Materials Chemistry and Pulmonary and Respiratory Medicine, having authored 32 papers that have together received 512 indexed citations. Recurring topics across this work include Visual perception and processing mechanisms (7 papers), Neural and Behavioral Psychology Studies (5 papers), Visual Attention and Saliency Detection (5 papers), Digital Radiography and Breast Imaging (5 papers), Ferroelectric and Piezoelectric Materials (4 papers), Gaze Tracking and Assistive Technology (4 papers), Microwave Dielectric Ceramics Synthesis (4 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Human-Computer Interaction (69 citations), Cognitive Neuroscience (208 citations), Sensory Systems (34 citations), Computer Vision and Pattern Recognition (126 citations) and Experimental and Cognitive Psychology (50 citations). Chia-Chien Wu has collaborated with scholars based in United States, Taiwan and Malaysia. Frequent co-authors include Marc Pomplun, Jeremy M. Wolfe, Eileen Kowler, Nien‐Tsu Huang, Oh‐Sang Kwon, Cheng‐Liang Huang, Tsung-Hsien Hsu, Meena Vythilingam, Karina S. Blair and Daniel McCaffrey. Their work appears in journals such as Vision Research, Journal of Vision, Journal of Alloys and Compounds, Journal of the European Ceramic Society and Cognitive Science.

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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