Jungjun Kim

1.1k citations
27 papers · 337 · h-index 10

Impact in

Papers in

Jungjun Kim

25 papers receiving 324 citations

Peers

Jungjun Kim
Comparison fields: 5 of 85
  • Molecular Medicine 26
  • Infectious Diseases 80
  • Instrumentation 12
  • Organic Chemistry 70
  • Computer Networks and Communications 55
Replace Shicong Liu with:
Shicong Liu China
Lijing Dong China
Fan Cheng China
Alexandre Sedoglavic France
Zengyuan Liu China
Satoshi Yamane Japan
Rahul Thakur India
Chih-Hao Liu Taiwan
Xingchen Lin China
Jungjun Kim relative to Shicong Liu China Shicong Liu's profile →
Citations per field
00.5×10×15×21×
Shicong Liu · 1×
Citations per year

Countries citing papers authored by Jungjun Kim

Since Specialization
Citations

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

Fields of papers citing papers by Jungjun Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014149
2 202326
3 201820
4 201719
5 201719
6 201812
7 202111
8 201911
9 201610
10 20199
11 20248
12 20086
13 20205
14 20195
15 20204
16 20224
17 20083
18 20093
19 20053
20 20093

About Jungjun Kim

Jungjun Kim is a scholar working on Computer Networks and Communications, Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Electrical and Electronic Engineering, having authored 27 papers that have together received 337 indexed citations. Recurring topics across this work include Ultrasound Imaging and Elastography (5 papers), Mobile Ad Hoc Networks (3 papers), Analog and Mixed-Signal Circuit Design (3 papers), Engineering Applied Research (3 papers), Wireless Networks and Protocols (3 papers), Network Security and Intrusion Detection (2 papers), Advanced MIMO Systems Optimization (2 papers) and Natural Language Processing Techniques (2 papers). The work is most often cited by research in Molecular Medicine (26 citations), Infectious Diseases (80 citations), Instrumentation (12 citations), Organic Chemistry (70 citations) and Computer Networks and Communications (55 citations). Jungjun Kim has collaborated with scholars based in South Korea and United States. Frequent co-authors include Saewoong Bahk, Jeongyeup Paek, Sei Jin Park, Hwankyu Kang, Sun‐Hee Kang, Sujin Ahn, Saeyeon Lee, Kiyean Nam, Tai‐Kyong Song and Inhee Choi. Their work appears in journals such as Ultrasonic Imaging, Sensors, IEEE Access, Journal of Natural Products and Signal Processing Image Communication.

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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