Tae Jong Choi
- Artificial Intelligence top 10%
- Metaheuristic Optimization Algorithms Research 15
- Evolutionary Algorithms and Applications 14
- Domain Adaptation and Few-Shot Learning 5
- Machine Learning and ELM 3
- Neural Networks and Reservoir Computing 2
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- Advanced Multi-Objective Optimization Algorithms 9
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- Multimodal Machine Learning Applications 4
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- Forest Biomass Utilization and Management 2
- Co-authors
- Chang Wook AhnJulian TogeliusJinung AnYun-Gyung CheongNikhil PachauriHee Yong YounJong‐Hyun LeeLifan Wang
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsComputer Vision and Pattern Recognition
- Journals
- SHILAP Revista de lepidopterología (1 paper)Expert Systems with Applications (3 papers)IEEE Access (3 papers)
- Partner nations
- South KoreaVietnamUnited States
In The Last Decade
Tae Jong Choi
26 papers receiving 223 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 126
- Computational Theory and Mathematics 62
- Computer Vision and Pattern Recognition 45
- Industrial and Manufacturing Engineering 11
- Control and Systems Engineering 24
Countries citing papers authored by Tae Jong Choi
This map shows the geographic impact of Tae Jong Choi'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 Tae Jong Choi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tae Jong Choi more than expected).
Fields of papers citing papers by Tae Jong Choi
This network shows the impact of papers produced by Tae Jong Choi. 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 Tae Jong Choi. The network helps show where Tae Jong Choi may publish in the future.
Co-authorship network
The 11 scholars most cited alongside Tae Jong Choi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 4 | |
| 6 | 2025 | 1 | |
| 7 | 2024 | 5 | |
| 8 | 2024 | 10 | |
| 9 | 2024 | 8 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 5 | |
| 12 | 2022 | 3 | |
| 13 | 2021 | 4 | |
| 14 | 2020 | 23 | |
| 15 | 2019 | 12 | |
| 16 | 2018 | 3 | |
| 17 | 2017 | 0 | |
| 18 | 2017 | 12 | |
| 19 | 2016 | 5 | |
| 20 | 2013 | 32 |
About Tae Jong Choi
Tae Jong Choi is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 30 papers that have together received 230 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (15 papers), Evolutionary Algorithms and Applications (14 papers), Advanced Multi-Objective Optimization Algorithms (9 papers), Domain Adaptation and Few-Shot Learning (5 papers), Multimodal Machine Learning Applications (4 papers), Machine Learning and ELM (3 papers), Neural Networks and Reservoir Computing (2 papers) and Forest Biomass Utilization and Management (2 papers). The work is most often cited by research in Artificial Intelligence (126 citations), Computational Theory and Mathematics (62 citations) and Computer Vision and Pattern Recognition (45 citations). Tae Jong Choi has collaborated with scholars based in South Korea, Vietnam and United States. Frequent co-authors include Chang Wook Ahn, Julian Togelius, Jinung An, Yun-Gyung Cheong, Nikhil Pachauri, Hee Yong Youn, Jong‐Hyun Lee, Lifan Wang, Hae‐Gon Jeon and Son Nguyen. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.
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.