Moo K. Chung
- Cognitive Neuroscience top 0.5%
- Radiology, Nuclear Medicine and Imaging top 0.5%
- Psychiatry and Mental health top 2%
- Computer Vision and Pattern Recognition top 2%
- Computational Theory and Mathematics top 1%
- Co-authors
- Richard J. DavidsonAndrew L. AlexanderKim M. DaltonAlan C. EvansSeth D. PollakKeith J. WorsleyJamie L. HansonHyejin Kang
- Topics
- Advanced Neuroimaging Techniques and Applications (69 papers)Functional Brain Connectivity Studies (53 papers)Topological and Geometric Data Analysis (34 papers)
- Partner nations
- United StatesSouth KoreaCanada
In The Last Decade
Moo K. Chung
178 papers receiving 5.7k citations
Peers
Comparison fields: 5 of 173
- Cognitive Neuroscience 2.4k
- Radiology, Nuclear Medicine and Imaging 1.7k
- Psychiatry and Mental health 608
- Computer Vision and Pattern Recognition 566
- Computational Theory and Mathematics 560
Countries citing papers authored by Moo K. Chung
This map shows the geographic impact of Moo K. 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 Moo K. Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Moo K. Chung more than expected).
Fields of papers citing papers by Moo K. Chung
This network shows the impact of papers produced by Moo K. 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 Moo K. Chung. The network helps show where Moo K. Chung may publish in the future.
Co-authorship network of co-authors of Moo K. Chung
This figure shows the co-authorship network connecting the top 25 collaborators of Moo K. Chung. A scholar is included among the top collaborators of Moo K. 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 Moo K. Chung. Moo K. 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 | 2 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 7 | |
| 6 | 2 | |
| 7 | Hill Climbing Optimized Twin Classification Using Resting-State Functional MRI. | 2 |
| 8 | 17 | |
| 9 | 32 | |
| 10 | 2 | |
| 11 | 20 | |
| 12 | 76 | |
| 13 | 54 | |
| 14 | 11 | |
| 15 | 13 | |
| 16 | 173 | |
| 17 | Encoding Cortical Surface by Spherical Harmonics | 29 |
| 18 | 295 | |
| 19 | 1 | |
| 20 | CURVATURE CORRECTIONS TO REYNOLDS STRESS MODEL FOR COMPUTATION OF TURBULENT RECIRCULATING FLOWS | 0 |
About Moo K. Chung
Moo K. Chung is a scholar working on Computational Mathematics, Radiology, Nuclear Medicine and Imaging and Cognitive Neuroscience, having authored 187 papers that have together received 5.9k indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (69 papers), Functional Brain Connectivity Studies (53 papers) and Topological and Geometric Data Analysis (34 papers). The work is most often cited by research in Cognitive Neuroscience (2.4k citations), Computational Mathematics (49 citations) and Radiology, Nuclear Medicine and Imaging (1.7k citations). Moo K. Chung has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Richard J. Davidson, Andrew L. Alexander, Kim M. Dalton, Alan C. Evans, Seth D. Pollak, Keith J. Worsley, Jamie L. Hanson, Hyejin Kang, Houri K. Vorperian and Vikas Singh. Their work appears in journals such as Journal of Neuroscience, PLoS ONE and Journal of Applied Physics.
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.