Minsuk Chang

889 citations
28 papers · 475 · h-index 12

Impact in

Papers in

Minsuk Chang

27 papers receiving 463 citations

Peers

Minsuk Chang
Comparison fields: 5 of 83
  • Human-Computer Interaction 119
  • Health Informatics 17
  • Computer Science Applications 45
  • Computer Vision and Pattern Recognition 115
  • Artificial Intelligence 180
Replace John Joon Young Chung with:
John Joon Young Chung United States
Toby Jia-Jun Li United States
Andy Coenen United States
Graham Dove United States
Advait Sarkar United Kingdom
Marketta Niemelä Finland
J.D. Zamfirescu-Pereira United States
Keith Edwards United States
Michal Luria United States
Samuel Marcos-Pablos Spain
Minsuk Chang relative to John Joon Young Chung United States John Joon Young Chung's profile →
Citations per field
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John Joon Young Chung · 1×
Citations per year

Countries citing papers authored by Minsuk Chang

Since Specialization
Citations

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

Fields of papers citing papers by Minsuk Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2022104
2 202349
3 201941
4 202328
5 202128
6 201826
7 202126
8 202121
9 202120
10 200618
11 202413
12 202212
13 202111
14 202210
15 202210
16 20239
17 20198
18 20237
19 20247
20 20226

About Minsuk Chang

Minsuk Chang is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Human-Computer Interaction and Computer Science Applications, having authored 28 papers that have together received 475 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Speech and dialogue systems (4 papers), Software Engineering Research (4 papers), Natural Language Processing Techniques (3 papers), Personal Information Management and User Behavior (3 papers), Educational Games and Gamification (3 papers), Innovative Human-Technology Interaction (3 papers) and Online Learning and Analytics (2 papers). The work is most often cited by research in Human-Computer Interaction (119 citations), Health Informatics (17 citations), Computer Science Applications (45 citations), Computer Vision and Pattern Recognition (115 citations) and Artificial Intelligence (180 citations). Minsuk Chang has collaborated with scholars based in South Korea, United States and Switzerland. Frequent co-authors include Juho Kim, John Joon Young Chung, Kang Min Yoo, Eytan Adar, Woo Seok Kim, Hwaran Lee, Maneesh Agrawala, Irina Shklovski, Michael Terry and Byungjoo Lee. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computer, IEEE Robotics and Automation Letters, Designing Interactive Systems Conference and Canada Human-Computer Communications Society.

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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