Joel Jang
Impact in
-
- Artificial Intelligence in Healthcare and Education
-
- Topic Modeling
- Natural Language Processing Techniques
- Privacy-Preserving Technologies in Data
- Imbalanced Data Classification Techniques
Papers in
-
- Topic Modeling 6
- Natural Language Processing Techniques 5
- Domain Adaptation and Few-Shot Learning 2
- Imbalanced Data Classification Techniques 1
- Advanced Text Analysis Techniques 1
- Multi-Agent Systems and Negotiation 1
- Speech Recognition and Synthesis 1
- Co-authors
- Minjoon Seo (9 shared papers)Sungho Suh (2 shared papers)Sohee Yang (4 shared papers)Kyoung-Ho Choi (1 shared paper)Yong Oh Lee (1 shared paper)Mayank Jha (1 shared paper)Lajanugen Logeswaran (1 shared paper)Chang-Ho Lee (1 shared paper)
- Journals
- Sensors (1 paper)Expert Systems with Applications (1 paper)Transactions of the Association for Computational Linguistics (1 paper)
- Partner nations
- South KoreaCanadaUnited States
In The Last Decade
Joel Jang
12 papers receiving 134 citations
Peers
Comparison fields: 5 of 50
- Health Informatics 8
- Artificial Intelligence 81
- Control and Systems Engineering 20
- Computer Vision and Pattern Recognition 16
- Medical Laboratory Technology 1
Countries citing papers authored by Joel Jang
This map shows the geographic impact of Joel Jang'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 Joel Jang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joel Jang more than expected).
Fields of papers citing papers by Joel Jang
This network shows the impact of papers produced by Joel Jang. 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 Joel Jang. The network helps show where Joel Jang may publish in the future.
Co-authors
The 19 scholars most cited alongside Joel Jang, 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 | 2022 | 27 | |
| 2 | 2021 | 25 | |
| 3 | 2020 | 23 | |
| 4 | 2023 | 22 | |
| 5 | 2023 | 22 | |
| 6 | 2024 | 5 | |
| 7 | 2025 | 5 | |
| 8 | 2024 | 2 | |
| 9 | 2023 | 1 | |
| 10 | 2024 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2024 | 1 |
About Joel Jang
Joel Jang is a scholar working on Artificial Intelligence, Control and Systems Engineering, Information Systems, Computer Vision and Pattern Recognition and Health Informatics, having authored 12 papers that have together received 135 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Domain Adaptation and Few-Shot Learning (2 papers), Imbalanced Data Classification Techniques (1 paper), Advanced Text Analysis Techniques (1 paper), Multi-Agent Systems and Negotiation (1 paper), Speech Recognition and Synthesis (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Health Informatics (8 citations), Artificial Intelligence (81 citations), Control and Systems Engineering (20 citations), Computer Vision and Pattern Recognition (16 citations) and Medical Laboratory Technology (1 citation). Joel Jang has collaborated with scholars based in South Korea, Canada and United States. Frequent co-authors include Minjoon Seo, Sungho Suh, Sohee Yang, Kyoung-Ho Choi, Yong Oh Lee, Mayank Jha, Lajanugen Logeswaran, Chang-Ho Lee, Jamin Shin and Joongbo Shin. Their work appears in journals such as Sensors, Expert Systems with Applications and Transactions of the Association for Computational Linguistics.
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.