Eung Gi Paek

1.7k citations
50 papers · 1.2k indexed · h-index 13
Topics
Photonic and Optical Devices (22 papers)Neural Networks and Reservoir Computing (11 papers)Semiconductor Lasers and Optical Devices (11 papers)

In The Last Decade

Eung Gi Paek

45 papers receiving 1.1k citations

Peers

Eung Gi Paek
Comparison fields: 5 of 74
  • Electrical and Electronic Engineering 750
  • Atomic and Molecular Physics, and Optics 464
  • Artificial Intelligence 447
  • Media Technology 246
  • Biomedical Engineering 134
Replace Sing H. Lee with:
Sing H. Lee United States
Karl‐Heinz Brenner Germany
John Hong United States
Ravindra A. Athale United States
Kazuo Kyuma Japan
Lei Xu United States
John F. Walkup United States
Suganda Jutamulia United States
Francis T. S. Yu United States
Nabil H. Farhat United States
Eung Gi Paek relative to Sing H. Lee United States Sing H. Lee's profile →
Citations per field
00.5×3.7×
Sing H. Lee · 1×
Citations per year

Countries citing papers authored by Eung Gi Paek

Since Specialization
Citations

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

Fields of papers citing papers by Eung Gi Paek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eung Gi Paek. 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 Eung Gi Paek. The network helps show where Eung Gi Paek may publish in the future.

Co-authorship network of co-authors of Eung Gi Paek

This figure shows the co-authorship network connecting the top 25 collaborators of Eung Gi Paek. A scholar is included among the top collaborators of Eung Gi Paek 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 Eung Gi Paek. Eung Gi Paek is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 4
3 1
4 16
5 0
6 3
7 9
8 8
9 1
10 6
11 11
12 4
13 5
14 5
15 1
16 14
17
Optical implementation of the Hopfield model
1
18 3
19 400
20 207

About Eung Gi Paek

Eung Gi Paek is a scholar working on Media Technology, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering, having authored 50 papers that have together received 1.2k indexed citations. Recurring topics across this work include Photonic and Optical Devices (22 papers), Neural Networks and Reservoir Computing (11 papers) and Semiconductor Lasers and Optical Devices (11 papers). The work is most often cited by research in Media Technology (246 citations), Atomic and Molecular Physics, and Optics (464 citations) and Artificial Intelligence (447 citations). Eung Gi Paek has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Demetri Psaltis, A. Prata, Nabil H. Farhat, Santosh S. Venkatesh, Daniel E. Leaird, D. H. Reitze, A. Von Lehmen, Andrew M. Weiner, Hoi‐Jun Yoo and Charles L. Wilson. Their work appears in journals such as Applied Physics Letters, Optics Letters and IEEE Transactions on Communications.

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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