Soyeon Kim
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Mechanical Engineering
- Hardware and Architecture
- Co-authors
- Hoi‐Jun YooSangyeob KimSangjin KimDonghyeon HanSanghoon KangJuhyoung LeeDongseok ImMinsung Kim
- Topics
- Advanced Memory and Neural Computing (14 papers)Ferroelectric and Negative Capacitance Devices (10 papers)Neural Networks and Reservoir Computing (7 papers)
- Cited by
- Computer Vision and Pattern RecognitionHardware and ArchitectureElectrical and Electronic Engineering
- Partner nations
- South KoreaSwitzerlandUnited States
In The Last Decade
Soyeon Kim
43 papers receiving 367 citations
Peers
Comparison fields: 5 of 76
- Electrical and Electronic Engineering 208
- Computer Vision and Pattern Recognition 110
- Artificial Intelligence 98
- Mechanical Engineering 31
- Hardware and Architecture 28
Countries citing papers authored by Soyeon Kim
This map shows the geographic impact of Soyeon 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 Soyeon Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soyeon Kim more than expected).
Fields of papers citing papers by Soyeon Kim
This network shows the impact of papers produced by Soyeon 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 Soyeon Kim. The network helps show where Soyeon Kim may publish in the future.
Co-authorship network of co-authors of Soyeon Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Soyeon Kim. A scholar is included among the top collaborators of Soyeon Kim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Soyeon Kim. Soyeon Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 13 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 17 | |
| 7 | 12 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 10 | |
| 12 | 16 | |
| 13 | 6 | |
| 14 | 22 | |
| 15 | 15 | |
| 16 | 59 | |
| 17 | 1 | |
| 18 | Multi-class Classification of Database Workloads using PCA-SVM Classifier | 1 |
| 19 | Tuple Pruning Using Bloom Filters for Packet Classification | 1 |
| 20 | 3 |
About Soyeon Kim
Soyeon Kim is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence, having authored 49 papers that have together received 383 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (14 papers), Ferroelectric and Negative Capacitance Devices (10 papers) and Neural Networks and Reservoir Computing (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (110 citations), Hardware and Architecture (28 citations) and Electrical and Electronic Engineering (208 citations). Soyeon Kim has collaborated with scholars based in South Korea, Switzerland and United States. Frequent co-authors include Hoi‐Jun Yoo, Sangyeob Kim, Sangjin Kim, Donghyeon Han, Sanghoon Kang, Juhyoung Lee, Dongseok Im, Minsung Kim, Jeong-Ryeol Kim and Kwantae Kim. Their work appears in journals such as Chemical Engineering Journal, Energy and Molecules.
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.