Taowei David Wang
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Molecular Biology
- Health Information Management top 2%
- Co-authors
- Catherine PlaisantBen ShneidermanKrist WongsuphasawatMeirav Taieb‐MaimonShawn N. MurphyAlexander J. QuinnAlexander RindSilvia Miksch
- Topics
- Data Visualization and Analytics (6 papers)Biomedical Text Mining and Ontologies (2 papers)Time Series Analysis and Forecasting (2 papers)
- Partner nations
- United StatesAustriaSouth Korea
In The Last Decade
Taowei David Wang
9 papers receiving 605 citations
Peers
Comparison fields: 5 of 88
- Computer Vision and Pattern Recognition 387
- Artificial Intelligence 203
- Signal Processing 149
- Molecular Biology 124
- Health Information Management 93
Countries citing papers authored by Taowei David Wang
This map shows the geographic impact of Taowei David 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 Taowei David Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taowei David Wang more than expected).
Fields of papers citing papers by Taowei David Wang
This network shows the impact of papers produced by Taowei David 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 Taowei David Wang. The network helps show where Taowei David Wang may publish in the future.
Co-authorship network of co-authors of Taowei David Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Taowei David Wang. A scholar is included among the top collaborators of Taowei David Wang 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 Taowei David Wang. Taowei David Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 23 | |
| 2 | 135 | |
| 3 | Analyzing Incident Management Event Sequences with Interactive Visualization | 9 |
| 4 | 38 | |
| 5 | 215 | |
| 6 | 31 | |
| 7 | 163 | |
| 8 | 24 | |
| 9 | Gauging Ontologies and Schemas by Numbers | 7 |
About Taowei David Wang
Taowei David Wang is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Management and Signal Processing, having authored 9 papers that have together received 645 indexed citations. Recurring topics across this work include Data Visualization and Analytics (6 papers), Biomedical Text Mining and Ontologies (2 papers) and Time Series Analysis and Forecasting (2 papers). The work is most often cited by research in Health Information Management (93 citations), Computer Vision and Pattern Recognition (387 citations) and Signal Processing (149 citations). Taowei David Wang has collaborated with scholars based in United States, Austria and South Korea. Frequent co-authors include Catherine Plaisant, Ben Shneiderman, Krist Wongsuphasawat, Meirav Taieb‐Maimon, Shawn N. Murphy, Alexander J. Quinn, Alexander Rind, Silvia Miksch, Wolfgang Aigner and Michael P. Cummings. Their work appears in journals such as Future Generation Computer Systems, Journal of Medical Systems and Journal of Digital Imaging.
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.