Sang-Woo Jun

626 citations
33 papers · 490 · h-index 11

Impact in

Papers in

Sang-Woo Jun

29 papers receiving 480 citations

Peers

Sang-Woo Jun
Comparison fields: 5 of 36
  • Hardware and Architecture 245
  • Computer Networks and Communications 364
  • Information Systems 139
  • Computer Vision and Pattern Recognition 97
  • Artificial Intelligence 111
Replace Guoyang Chen with:
Guoyang Chen United States
Huynh Phung Huynh Singapore
Muhsen Owaida Switzerland
Guangdeng Liao United States
Muthian Sivathanu United States
Kern Koh South Korea
Abdullah Gharaibeh Canada
Farzad Khorasani United States
Kyle Wheeler United States
Andrew Kerr United States
Sang-Woo Jun relative to Guoyang Chen United States Guoyang Chen's profile →
Citations per field
00.5×1.6×
Guoyang Chen · 1×
Citations per year

Countries citing papers authored by Sang-Woo Jun

Since Specialization
Citations

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

Fields of papers citing papers by Sang-Woo Jun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Sang-Woo Jun, 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 Sang-Woo Jun Line = papers co-authored together Sang-Woo Jun 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 2015117
2 201867
3 201650
4
Application-managed flash
201649
5 201728
6 201424
7 201517
8 201616
9 202115
10 201513
11 202011
12 20099
13 20098
14 20197
15 20167
16 20217
17 20226
18 20206
19 20235
20 20225

About Sang-Woo Jun

Sang-Woo Jun is a scholar working on Computer Networks and Communications, Hardware and Architecture, Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition, having authored 33 papers that have together received 490 indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (14 papers), Parallel Computing and Optimization Techniques (12 papers), Cloud Computing and Resource Management (8 papers), Caching and Content Delivery (7 papers), Algorithms and Data Compression (6 papers), Advanced Memory and Neural Computing (3 papers), Neural Networks and Applications (3 papers) and Graph Theory and Algorithms (3 papers). The work is most often cited by research in Hardware and Architecture (245 citations), Computer Networks and Communications (364 citations), Information Systems (139 citations), Computer Vision and Pattern Recognition (97 citations) and Artificial Intelligence (111 citations). Sang-Woo Jun has collaborated with scholars based in United States, South Korea and Belgium. Frequent co-authors include Arvind Arvind, Shuotao Xu, Sungjin Lee, Ming Liu, Jamey Hicks, Myron King, Andy Wright, Jihong Kim, Ming Liu and Jinyeong Moon. Their work appears in journals such as ACM Transactions on Reconfigurable Technology and Systems, ACM Transactions on Computer Systems, Electronics, Proceedings of the VLDB Endowment and Lecture notes in computer science.

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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