Dongryeol Lee

508 total citations
22 papers, 318 citations indexed

About

Dongryeol Lee is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Dongryeol Lee has authored 22 papers receiving a total of 318 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 5 papers in Signal Processing. Recurrent topics in Dongryeol Lee's work include Algorithms and Data Compression (8 papers), Topic Modeling (3 papers) and Electromagnetic Scattering and Analysis (3 papers). Dongryeol Lee is often cited by papers focused on Algorithms and Data Compression (8 papers), Topic Modeling (3 papers) and Electromagnetic Scattering and Analysis (3 papers). Dongryeol Lee collaborates with scholars based in United States, South Korea and United Kingdom. Dongryeol Lee's co-authors include Kihwan Kim, Irfan Essa, Alexander Gray, Parikshit Ram, Daehee Choi, Jin-Young Jung, Andrew Moore, Ping Wang, James M. Rehg and Richard Vuduc and has published in prestigious journals such as Journal of Computational Physics, Review of Financial Studies and International Journal of Hydrogen Energy.

In The Last Decade

Dongryeol Lee

20 papers receiving 300 citations

Peers

Dongryeol Lee
Comparison fields: 5 of 73
  • Computer Vision and Pattern Recognition 160
  • Artificial Intelligence 144
  • Signal Processing 49
  • Automotive Engineering 44
  • Pollution 27
Replace Fei Yan with:
Fei Yan China
Rifat Edizkan Türkiye
Lihua Hu China
Guocheng Li China
Paul Navrátil United States
Jyoti Jain India
Mortuza Ali Australia
Chi-Tat Leung Hong Kong
Arto Kaarna Finland
Anant Ram India
Fei Yan China View profile →
Citations per field, relative to Dongryeol Lee
Dongryeol Lee · 1×
Citations per year, relative to Dongryeol Lee
Dongryeol Lee · 1×

Countries citing papers authored by Dongryeol Lee

Since Specialization
Citations

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

Fields of papers citing papers by Dongryeol Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongryeol Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Dongryeol Lee. A scholar is included among the top collaborators of Dongryeol Lee 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 Dongryeol Lee. Dongryeol Lee 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
# Work Indexed citations
1 3
2 1
3 2
4 2
5 1
6 0
7 31
8 8
9 6
10 23
11 1
12 12
13 2
14 6
15 21
16 117
17
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
12
18
Linear-time Algorithms for Pairwise Statistical Problems
23
19
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method
14
20
Fast Mean Shift with Accurate and Stable Convergence
25

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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