Yongdai Kim
- Statistics and Probability top 1%
- Artificial Intelligence top 5%
- Surgery
- Pathology and Forensic Medicine top 5%
- Molecular Biology
- Co-authors
- Hosik ChoiSunghoon KwonByung-Joon ShinJae Chul LeeHee‐Seok OhJin‐Seog KimJaeyong LeeJune Young Chun
- Topics
- Statistical Methods and Inference (32 papers)Statistical Methods and Bayesian Inference (15 papers)Bayesian Methods and Mixture Models (13 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of the American Statistical AssociationIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- South KoreaUnited StatesEthiopia
In The Last Decade
Yongdai Kim
93 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 164
- Statistics and Probability 465
- Artificial Intelligence 368
- Surgery 255
- Pathology and Forensic Medicine 255
- Molecular Biology 146
Countries citing papers authored by Yongdai Kim
This map shows the geographic impact of Yongdai Kim'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 Yongdai Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yongdai Kim more than expected).
Fields of papers citing papers by Yongdai Kim
This network shows the impact of papers produced by Yongdai Kim. 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 Yongdai Kim. The network helps show where Yongdai Kim may publish in the future.
Co-authorship network of co-authors of Yongdai Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Yongdai Kim. A scholar is included among the top collaborators of Yongdai Kim 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 Yongdai Kim. Yongdai Kim 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 | 3 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 33 | |
| 7 | 18 | |
| 8 | 0 | |
| 9 | 1 | |
| 10 | 72 | |
| 11 | 20 | |
| 12 | 14 | |
| 13 | 3 | |
| 14 | Consistent model selection criteria on high dimensions | 58 |
| 15 | 6 | |
| 16 | 36 | |
| 17 | 14 | |
| 18 | 165 | |
| 19 | 3 | |
| 20 | 2 |
About Yongdai Kim
Yongdai Kim is a scholar working on Statistics and Probability, Modeling and Simulation and Artificial Intelligence, having authored 104 papers that have together received 1.7k indexed citations. Recurring topics across this work include Statistical Methods and Inference (32 papers), Statistical Methods and Bayesian Inference (15 papers) and Bayesian Methods and Mixture Models (13 papers). The work is most often cited by research in Statistics and Probability (465 citations), Pathology and Forensic Medicine (255 citations) and Artificial Intelligence (368 citations). Yongdai Kim has collaborated with scholars based in South Korea, United States and Ethiopia. Frequent co-authors include Hosik Choi, Sunghoon Kwon, Byung-Joon Shin, Jae Chul Lee, Hee‐Seok Oh, Jin‐Seog Kim, Jaeyong Lee, June Young Chun, Sangin Lee and Jong‐June Jeon. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.