Sungahn Ko
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Artificial Intelligence top 10%
- Building and Construction top 10%
- Signal Processing top 10%
- Co-authors
- David S. EbertRoss MaciejewskiSeungmin JinNiklas ElmqvistYun JangChinghai KaoLeland W.K. ChungBum Chul Kwon
- Topics
- Data Visualization and Analytics (20 papers)Traffic Prediction and Management Techniques (5 papers)Virus-based gene therapy research (3 papers)
- Journals
- International Journal of Radiation Oncology*Biology*PhysicsIEEE AccessIEEE Transactions on Biomedical Engineering
- Partner nations
- United StatesSouth KoreaChina
In The Last Decade
Sungahn Ko
34 papers receiving 525 citations
Peers
Comparison fields: 5 of 109
- Computer Vision and Pattern Recognition 223
- Molecular Biology 97
- Artificial Intelligence 97
- Building and Construction 74
- Signal Processing 71
Countries citing papers authored by Sungahn Ko
This map shows the geographic impact of Sungahn Ko'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 Sungahn Ko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungahn Ko more than expected).
Fields of papers citing papers by Sungahn Ko
This network shows the impact of papers produced by Sungahn Ko. 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 Sungahn Ko. The network helps show where Sungahn Ko may publish in the future.
Co-authorship network of co-authors of Sungahn Ko
This figure shows the co-authorship network connecting the top 25 collaborators of Sungahn Ko. A scholar is included among the top collaborators of Sungahn Ko 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 Sungahn Ko. Sungahn Ko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 14 | |
| 5 | 23 | |
| 6 | 8 | |
| 7 | 10 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. | 27 |
| 11 | 1 | |
| 12 | 16 | |
| 13 | 3 | |
| 14 | 3 | |
| 15 | 13 | |
| 16 | 23 | |
| 17 | 28 | |
| 18 | 7 | |
| 19 | 6 | |
| 20 | 40 |
About Sungahn Ko
Sungahn Ko is a scholar working on Computer Vision and Pattern Recognition, Structural Biology and Transportation, having authored 36 papers that have together received 546 indexed citations. Recurring topics across this work include Data Visualization and Analytics (20 papers), Traffic Prediction and Management Techniques (5 papers) and Virus-based gene therapy research (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (223 citations), Transportation (63 citations) and Signal Processing (71 citations). Sungahn Ko has collaborated with scholars based in United States, South Korea and China. Frequent co-authors include David S. Ebert, Ross Maciejewski, Seungmin Jin, Niklas Elmqvist, Yun Jang, Chinghai Kao, Leland W.K. Chung, Bum Chul Kwon, Kyung‐Tae Kim and Robert A. Sikes. Their work appears in journals such as International Journal of Radiation Oncology*Biology*Physics, IEEE Access and IEEE Transactions on Biomedical Engineering.
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.