Chieh‐Han Wu

19 papers receiving 617 citations

Chieh‐Han Wu's Hit Papers

Microsoft Academic Graph: When experts are not enough 2020 · 293 citations
2930+2+4Years since publication50100150200250

Peers

Chieh‐Han Wu
Comparison fields: 5 of 92
  • Statistics, Probability and Uncertainty 101
  • Artificial Intelligence 252
  • Polymers and Plastics 105
  • Statistical and Nonlinear Physics 67
  • Information Systems and Management 37
Replace Jianmin Yao with:
Jianmin Yao China
Mengxiong Liu China
Hailiang Huang China
Jin Shi China
António Correia Portugal
Bo Pang China
Dongwook Shin South Korea
Fariba Karimi Austria
Wentao Zheng China
Wen Dong United States
Chieh‐Han Wu relative to Jianmin Yao China Jianmin Yao's profile →
Citations per field
00.5×6.7×
Jianmin Yao · 1×
Citations per year

Countries citing papers authored by Chieh‐Han Wu

Since Specialization
Citations

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

Fields of papers citing papers by Chieh‐Han Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

19 of 19 papers shown
#Work
1
Microsoft Academic Graph: When experts are not enough
Hit paper breakdown →
2020293
2 201990
3 201744
4 201241
5 201228
6 201127
7 201822
8 201317
9 202217
10 201915
11 20159
12 20117
13 20206
14 20204
15 20134
16 20162
17 20212
18 20181
19 20091

About Chieh‐Han Wu

Chieh‐Han Wu is a scholar working on Biomedical Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Polymers and Plastics and Automotive Engineering, having authored 19 papers that have together received 630 indexed citations. Recurring topics across this work include Advanced Sensor and Energy Harvesting Materials (7 papers), Conducting polymers and applications (6 papers), Topic Modeling (5 papers), Organic Electronics and Photovoltaics (4 papers), Scientific Computing and Data Management (2 papers), Semantic Web and Ontologies (2 papers), Supercapacitor Materials and Fabrication (2 papers) and Biomedical Text Mining and Ontologies (2 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (101 citations), Artificial Intelligence (252 citations), Polymers and Plastics (105 citations), Statistical and Nonlinear Physics (67 citations) and Information Systems and Management (37 citations). Chieh‐Han Wu has collaborated with scholars based in Taiwan, United States and United Arab Emirates. Frequent co-authors include Kuansan Wang, Z. Shen, Anshul Kanakia, Yuxiao Dong, Wen‐Yen Chiu, Trong‐Ming Don, Saif Almheiri, Ibrahim Mustafa, Rahmat Agung Susantyoko and Horng‐Jer Tai. Their work appears in journals such as Polymer, Materials Chemistry and Physics, Scientific Reports, Journal of Materials Chemistry and Journal of Materials Chemistry C.

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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