Sugil Lee

496 citations
25 papers · 351 · h-index 12

Impact in

Papers in

Sugil Lee

22 papers receiving 344 citations

Peers

Sugil Lee
Comparison fields: 5 of 59
  • Management of Technology and Innovation 43
  • Electrical and Electronic Engineering 213
  • Computer Vision and Pattern Recognition 74
  • Energy Engineering and Power Technology 10
  • Artificial Intelligence 80
Replace Dominik Baumann with:
Dominik Baumann Germany
Runsheng Liu China
Sergey Serebryakov United States
A. B. Pawar India
Morshed Chowdhury Australia
Eric D. Simmon United States
Cornelius Glackin United Kingdom
Jianfeng Zhao China
Preeti Sharma India
Luis Díez Spain
Sugil Lee relative to Dominik Baumann Germany Dominik Baumann's profile →
Citations per field
00.5×8.2×
Dominik Baumann · 1×
Citations per year

Countries citing papers authored by Sugil Lee

Since Specialization
Citations

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

Fields of papers citing papers by Sugil Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Sugil Lee, 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 Sugil Lee Line = papers co-authored together Sugil Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201945
2 202035
3 201934
4 201828
5 201825
6 202024
7 202223
8 201818
9 202118
10 202117
11 202116
12 201916
13 20229
14 20227
15 20227
16 20186
17 20206
18 20215
19 20225
20 20223

About Sugil Lee

Sugil Lee is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications and Cellular and Molecular Neuroscience, having authored 25 papers that have together received 351 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (12 papers), Ferroelectric and Negative Capacitance Devices (10 papers), Advanced Neural Network Applications (6 papers), Neuroscience and Neural Engineering (3 papers), Error Correcting Code Techniques (3 papers), Intellectual Property and Patents (3 papers), Stochastic Gradient Optimization Techniques (3 papers) and Machine Learning in Materials Science (2 papers). The work is most often cited by research in Management of Technology and Innovation (43 citations), Electrical and Electronic Engineering (213 citations), Computer Vision and Pattern Recognition (74 citations), Energy Engineering and Power Technology (10 citations) and Artificial Intelligence (80 citations). Sugil Lee has collaborated with scholars based in South Korea, United States and Saudi Arabia. Frequent co-authors include Jongeun Lee, Mohammed E. Fouda, Fadi Kurdahi, Hyeonuk Sim, Ahmed M. Eltawil, Chulung Lee, Sung Won Cho, Dae Woo Kim, Dong Thanh Nguyen and Gildong Kim. Their work appears in journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Sustainability, IEEE Access, IEEE Transactions on Nanotechnology and Environmental Engineering 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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