SueYeon Chung
- Cognitive Neuroscience top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Statistical and Nonlinear Physics
- Cellular and Molecular Neuroscience
- Co-authors
- Daniel D. LeeHaim SompolinskyAmy BernardTamara J. SussmanI. KovácsChristos PapadimitriouZoltán ToroczkaiAlbert-Ĺaszló Barabási
- Topics
- Neural dynamics and brain function (6 papers)Neural Networks and Applications (5 papers)Visual perception and processing mechanisms (5 papers)
- Journals
- Proceedings of the National Academy of SciencesPhysical Review LettersNature Communications
- Partner nations
- United StatesIsraelCanada
In The Last Decade
SueYeon Chung
14 papers receiving 240 citations
Peers
Comparison fields: 5 of 65
- Cognitive Neuroscience 130
- Artificial Intelligence 104
- Computer Vision and Pattern Recognition 33
- Statistical and Nonlinear Physics 30
- Cellular and Molecular Neuroscience 24
Countries citing papers authored by SueYeon Chung
This map shows the geographic impact of SueYeon 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 SueYeon Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites SueYeon Chung more than expected).
Fields of papers citing papers by SueYeon Chung
This network shows the impact of papers produced by SueYeon 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 SueYeon Chung. The network helps show where SueYeon Chung may publish in the future.
Co-authorship network of co-authors of SueYeon Chung
This figure shows the co-authorship network connecting the top 25 collaborators of SueYeon Chung. A scholar is included among the top collaborators of SueYeon 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 SueYeon Chung. SueYeon 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 | 38 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 5 | |
| 7 | 13 | |
| 8 | 3 | |
| 9 | 8 | |
| 10 | 6 | |
| 11 | 83 | |
| 12 | 2 | |
| 13 | 7 | |
| 14 | 53 | |
| 15 | 0 | |
| 16 | 16 | |
| 17 | 7 |
About SueYeon Chung
SueYeon Chung is a scholar working on Cognitive Neuroscience, Aging and Developmental Biology, having authored 17 papers that have together received 243 indexed citations. Recurring topics across this work include Neural dynamics and brain function (6 papers), Neural Networks and Applications (5 papers) and Visual perception and processing mechanisms (5 papers). The work is most often cited by research in Cognitive Neuroscience (130 citations), Biophysics (23 citations) and Artificial Intelligence (104 citations). SueYeon Chung has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Daniel D. Lee, Haim Sompolinsky, Amy Bernard, Tamara J. Sussman, I. Kovács, Christos Papadimitriou, Zoltán Toroczkai, Albert-Ĺaszló Barabási, Hernán A. Makse and Edward T. Bullmore. Their work appears in journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nature Communications.
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.