Chan‐Jin Chung
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition
- Computer Science Applications top 10%
- Control and Systems Engineering
- Co-authors
- Robert G. ReynoldsChristopher CartwrightBernhard SendhoffLior ShamirJoshua SiegelThomas M. BurkeMark WilsonSantosh Nair
- Topics
- Autonomous Vehicle Technology and Safety (9 papers)Evolutionary Algorithms and Applications (8 papers)Teaching and Learning Programming (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaComputers in IndustryInternational Journal of Pattern Recognition and Artificial Intelligence
- Partner nations
- United StatesIndiaSouth Korea
In The Last Decade
Chan‐Jin Chung
27 papers receiving 361 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 218
- Computational Theory and Mathematics 89
- Computer Vision and Pattern Recognition 48
- Computer Science Applications 40
- Control and Systems Engineering 39
Countries citing papers authored by Chan‐Jin Chung
This map shows the geographic impact of Chan‐Jin Chung'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 Chan‐Jin Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chan‐Jin Chung more than expected).
Fields of papers citing papers by Chan‐Jin Chung
This network shows the impact of papers produced by Chan‐Jin Chung. 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 Chan‐Jin Chung. The network helps show where Chan‐Jin Chung may publish in the future.
Co-authorship network of co-authors of Chan‐Jin Chung
This figure shows the co-authorship network connecting the top 25 collaborators of Chan‐Jin Chung. A scholar is included among the top collaborators of Chan‐Jin Chung 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 Chan‐Jin Chung. Chan‐Jin Chung 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 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 4 | |
| 8 | 6 | |
| 9 | 0 | |
| 10 | 5 | |
| 11 | 1 | |
| 12 | 15 | |
| 13 | 1 | |
| 14 | 6 | |
| 15 | 22 | |
| 16 | 7 | |
| 17 | 11 | |
| 18 | 2 | |
| 19 | Knowledge-based approaches to self-adaptation in cultural algorithms | 46 |
| 20 | 67 |
About Chan‐Jin Chung
Chan‐Jin Chung is a scholar working on Computer Science Applications, Automotive Engineering and Software, having authored 31 papers that have together received 391 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (9 papers), Evolutionary Algorithms and Applications (8 papers) and Teaching and Learning Programming (7 papers). The work is most often cited by research in Computer Science Applications (40 citations), Artificial Intelligence (218 citations) and Computational Theory and Mathematics (89 citations). Chan‐Jin Chung has collaborated with scholars based in United States, India and South Korea. Frequent co-authors include Robert G. Reynolds, Christopher Cartwright, Bernhard Sendhoff, Lior Shamir, Joshua Siegel, Thomas M. Burke, Mark Wilson, Santosh Nair, Liping Chen and I‐Hsiang Tseng. Their work appears in journals such as SHILAP Revista de lepidopterología, Computers in Industry and International Journal of Pattern Recognition and Artificial 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.