Yuchung J. Wang
- Statistical and Nonlinear Physics top 5%
- Statistics and Probability top 2%
- Artificial Intelligence top 10%
- Management Science and Operations Research top 10%
- Management Information Systems top 10%
- Co-authors
- George Y. WongPaul W. HollandJinn‐Tsair TengHui‐Ling YangMaw‐Sheng ChernEdward H. IpMichel MeuldersPaul De Boeck
- Topics
- Statistical Methods and Bayesian Inference (7 papers)Bayesian Modeling and Causal Inference (6 papers)Bayesian Methods and Mixture Models (5 papers)
- Partner nations
- United StatesTaiwanNetherlands
In The Last Decade
Yuchung J. Wang
18 papers receiving 566 citations
Peers
Comparison fields: 5 of 78
- Statistical and Nonlinear Physics 229
- Statistics and Probability 205
- Artificial Intelligence 189
- Management Science and Operations Research 85
- Management Information Systems 72
Countries citing papers authored by Yuchung J. Wang
This map shows the geographic impact of Yuchung J. 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 Yuchung J. Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuchung J. Wang more than expected).
Fields of papers citing papers by Yuchung J. Wang
This network shows the impact of papers produced by Yuchung J. 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 Yuchung J. Wang. The network helps show where Yuchung J. Wang may publish in the future.
Co-authorship network of co-authors of Yuchung J. Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Yuchung J. Wang. A scholar is included among the top collaborators of Yuchung J. 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 Yuchung J. Wang. Yuchung J. 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 | 1 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 8 | |
| 6 | 7 | |
| 7 | 7 | |
| 8 | 16 | |
| 9 | 18 | |
| 10 | 3 | |
| 11 | 80 | |
| 12 | 5 | |
| 13 | 11 | |
| 14 | 107 | |
| 15 | 263 | |
| 16 | 45 | |
| 17 | 18 | |
| 18 | 2 |
About Yuchung J. Wang
Yuchung J. Wang is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 18 papers that have together received 598 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (7 papers), Bayesian Modeling and Causal Inference (6 papers) and Bayesian Methods and Mixture Models (5 papers). The work is most often cited by research in Statistics and Probability (205 citations), Statistical and Nonlinear Physics (229 citations) and Management Information Systems (72 citations). Yuchung J. Wang has collaborated with scholars based in United States, Taiwan and Netherlands. Frequent co-authors include George Y. Wong, Paul W. Holland, Jinn‐Tsair Teng, Hui‐Ling Yang, Maw‐Sheng Chern, Edward H. Ip, Michel Meulders, Paul De Boeck and Shyh‐Huei Chen. Their work appears in journals such as Journal of the American Statistical Association, Biometrika and Psychometrika.
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.