Tae Young Yang
- Statistics and Probability top 2%
- Software top 2%
- Safety, Risk, Reliability and Quality top 2%
- Artificial Intelligence
- Statistics, Probability and Uncertainty top 5%
- Co-authors
- Lynn KuoTim B. SwartzAlbert VexlerYoel HaitovskyDong‐Min KimNa Ra YunSo Yeon RyuChang Youl Lee
- Topics
- Gene expression and cancer classification (7 papers)Software Reliability and Analysis Research (6 papers)Bayesian Methods and Mixture Models (6 papers)
- Partner nations
- South KoreaUnited StatesCanada
In The Last Decade
Tae Young Yang
27 papers receiving 439 citations
Peers
Comparison fields: 5 of 96
- Statistics and Probability 221
- Software 159
- Safety, Risk, Reliability and Quality 157
- Artificial Intelligence 65
- Statistics, Probability and Uncertainty 61
Countries citing papers authored by Tae Young Yang
This map shows the geographic impact of Tae Young Yang'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 Tae Young Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tae Young Yang more than expected).
Fields of papers citing papers by Tae Young Yang
This network shows the impact of papers produced by Tae Young Yang. 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 Tae Young Yang. The network helps show where Tae Young Yang may publish in the future.
Co-authorship network of co-authors of Tae Young Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Tae Young Yang. A scholar is included among the top collaborators of Tae Young Yang 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 Tae Young Yang. Tae Young Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 17 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 46 | |
| 8 | 8 | |
| 9 | 1 | |
| 10 | 5 | |
| 11 | 36 | |
| 12 | 3 | |
| 13 | Insulin Secretion and Insulin Sensitivity in Korean Subjects with Impaired Glucose Intolerance. | 3 |
| 14 | 45 | |
| 15 | 12 | |
| 16 | 20 | |
| 17 | 114 | |
| 18 | 32 | |
| 19 | 15 | |
| 20 | Computational approaches to Bayesian inference for software reliability | 5 |
About Tae Young Yang
Tae Young Yang is a scholar working on Software, Statistics and Probability and Statistics, Probability and Uncertainty, having authored 28 papers that have together received 468 indexed citations. Recurring topics across this work include Gene expression and cancer classification (7 papers), Software Reliability and Analysis Research (6 papers) and Bayesian Methods and Mixture Models (6 papers). The work is most often cited by research in Software (159 citations), Statistics and Probability (221 citations) and Safety, Risk, Reliability and Quality (157 citations). Tae Young Yang has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include Lynn Kuo, Tim B. Swartz, Albert Vexler, Yoel Haitovsky, Dong‐Min Kim, Na Ra Yun, So Yeon Ryu, Chang Youl Lee, Sung Heui Shin and Jong Soo Choi. Their work appears in journals such as Journal of the American Statistical Association, PLoS ONE and Statistics in Medicine.
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.