Jun Park

45 papers receiving 464 citations

Peers

Jun Park
Comparison fields: 5 of 117
  • Human-Computer Interaction 76
  • Psychiatry and Mental health 116
  • Biotechnology 45
  • Computer Vision and Pattern Recognition 93
  • Nutrition and Dietetics 55
Replace Dan Liao with:
Dan Liao China
Zi Ye China
So Young Kim South Korea
Theekshana Dissanayake Australia
Faijan Akhtar China
H. J. Wright United Kingdom
Hélder Silva Portugal
Chen Xiu China
Kanako Nakajima Japan
Andrea Casanova Italy
Jun Park relative to Dan Liao China Dan Liao's profile →
Citations per field
00.5×8.3×
Dan Liao · 1×
Citations per year

Countries citing papers authored by Jun Park

Since Specialization
Citations

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

Fields of papers citing papers by Jun Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Park, 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 Park Line = papers co-authored together Jun Park links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200563
2 199446
3 201540
4 201637
5 199935
6 201835
7 201825
8 201918
9 200817
10 201517
11 201814
12 201413
13 200610
14 201910
15 201810
16 19959
17 19998
18 20217
19 19886
20 20206

About Jun Park

Jun Park is a scholar working on Computer Vision and Pattern Recognition, Psychiatry and Mental health, Pediatrics, Perinatology and Child Health, Human-Computer Interaction and Cognitive Neuroscience, having authored 50 papers that have together received 489 indexed citations. Recurring topics across this work include Epilepsy research and treatment (13 papers), Augmented Reality Applications (13 papers), Interactive and Immersive Displays (6 papers), Pharmacological Effects and Toxicity Studies (5 papers), Robotics and Sensor-Based Localization (5 papers), Virtual Reality Applications and Impacts (4 papers), Neonatal and fetal brain pathology (4 papers) and Indoor and Outdoor Localization Technologies (4 papers). The work is most often cited by research in Human-Computer Interaction (76 citations), Psychiatry and Mental health (116 citations), Biotechnology (45 citations), Computer Vision and Pattern Recognition (93 citations) and Nutrition and Dietetics (55 citations). Jun Park has collaborated with scholars based in United States, South Korea and Australia. Frequent co-authors include Woohun Lee, J. E. Rollings, Guadalupe Fernández‐Baca Vaca, Rajkumar Agarwal, Piercarlo Valdesolo, Harry T. Chugani, Harald Conrad, Andrew S. Magee, Asim Shahid and Joon‐Suk Park. Their work appears in journals such as Epileptic Disorders, Biotechnology and Bioengineering, Seizure, Journal of Neurosurgery Pediatrics and World Neurosurgery.

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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