Jun‐Young Chung

16.7k citations
33 papers · 683 · h-index 11

Impact in

Papers in

Jun‐Young Chung

30 papers receiving 651 citations

Peers

Jun‐Young Chung
Comparison fields: 5 of 105
  • Cognitive Neuroscience 184
  • Radiology, Nuclear Medicine and Imaging 217
  • Artificial Intelligence 157
  • Computer Vision and Pattern Recognition 97
  • Signal Processing 44
Replace Anthony J. Sherbondy with:
Anthony J. Sherbondy United States
Pedro Martins Portugal
Guha Balakrishnan United States
S.S. Furuie Brazil
Paola Campadelli Italy
Nicha C. Dvornek United States
Hyun Wook Park South Korea
Nikhil Singh United States
Hao Tang China
Guihu Zhao China
Jun‐Young Chung relative to Anthony J. Sherbondy United States Anthony J. Sherbondy's profile →
Citations per field
00.5×3.4×
Anthony J. Sherbondy · 1×
Citations per year

Countries citing papers authored by Jun‐Young Chung

Since Specialization
Citations

This map shows the geographic impact of Jun‐Young 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 Jun‐Young Chung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun‐Young Chung more than expected).

Fields of papers citing papers by Jun‐Young Chung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun‐Young 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 Jun‐Young Chung. The network helps show where Jun‐Young Chung may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun‐Young Chung, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun‐Young Chung Line = papers co-authored together Jun‐Young Chung links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015261
2 201262
3 200549
4 202138
5 200638
6 201131
7 200530
8 200427
9 201126
10 202019
11 201911
12 20129
13 20089
14 20189
15 20078
16 20146
17 20226
18 20226
19 20085
20 20225

About Jun‐Young Chung

Jun‐Young Chung is a scholar working on Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience, Atomic and Molecular Physics, and Optics, Spectroscopy and Artificial Intelligence, having authored 33 papers that have together received 683 indexed citations. Recurring topics across this work include Advanced MRI Techniques and Applications (17 papers), Advanced Neuroimaging Techniques and Applications (7 papers), Advanced NMR Techniques and Applications (6 papers), Medical Imaging Techniques and Applications (6 papers), Atomic and Subatomic Physics Research (6 papers), Functional Brain Connectivity Studies (4 papers), Natural Language Processing Techniques (3 papers) and Neurobiology of Language and Bilingualism (3 papers). The work is most often cited by research in Cognitive Neuroscience (184 citations), Radiology, Nuclear Medicine and Imaging (217 citations), Artificial Intelligence (157 citations), Computer Vision and Pattern Recognition (97 citations) and Signal Processing (44 citations). Jun‐Young Chung has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include Yoshua Bengio, Kyunghyun Cho, Çaǧlar Gülçehre, Hyo Woon Yoon, Hyunwook Park, Zang‐Hee Cho, Se‐Hong Oh, Yeji Han, Oliver Speck and Maxim Zaitsev. Their work appears in journals such as Sensors, Journal of Magnetic Resonance Imaging, Magnetic Resonance Materials in Physics Biology and Medicine, Medical Physics and Neuroscience Research.

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.

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