Seungwhan Moon
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Experimental and Cognitive Psychology
- Social Psychology
- Co-authors
- Rajen SubbaPararth ShahAnuj KumarVı́tor CarvalhoLeonardo NevesGunhee KimLeonid SigalAndrea Madotto
- Topics
- Topic Modeling (17 papers)Multimodal Machine Learning Applications (13 papers)Speech and dialogue systems (10 papers)
- Journals
- National University of SingaporeProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United StatesIsraelSouth Korea
In The Last Decade
Seungwhan Moon
31 papers receiving 576 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 503
- Computer Vision and Pattern Recognition 218
- Information Systems 77
- Experimental and Cognitive Psychology 43
- Social Psychology 24
Countries citing papers authored by Seungwhan Moon
This map shows the geographic impact of Seungwhan Moon'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 Seungwhan Moon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seungwhan Moon more than expected).
Fields of papers citing papers by Seungwhan Moon
This network shows the impact of papers produced by Seungwhan Moon. 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 Seungwhan Moon. The network helps show where Seungwhan Moon may publish in the future.
Co-authorship network of co-authors of Seungwhan Moon
This figure shows the co-authorship network connecting the top 25 collaborators of Seungwhan Moon. A scholar is included among the top collaborators of Seungwhan Moon 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 Seungwhan Moon. Seungwhan Moon 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 | 1 | |
| 4 | 2 | |
| 5 | 14 | |
| 6 | 6 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 6 | |
| 10 | 6 | |
| 11 | 48 | |
| 12 | 1 | |
| 13 | 22 | |
| 14 | 26 | |
| 15 | 176 | |
| 16 | 49 | |
| 17 | 4 | |
| 18 | 15 | |
| 19 | 21 | |
| 20 | 18 |
About Seungwhan Moon
Seungwhan Moon is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 33 papers that have together received 602 indexed citations. Recurring topics across this work include Topic Modeling (17 papers), Multimodal Machine Learning Applications (13 papers) and Speech and dialogue systems (10 papers). The work is most often cited by research in Artificial Intelligence (503 citations), Computer Vision and Pattern Recognition (218 citations) and Information Systems (77 citations). Seungwhan Moon has collaborated with scholars based in United States, Israel and South Korea. Frequent co-authors include Rajen Subba, Pararth Shah, Anuj Kumar, Vı́tor Carvalho, Leonardo Neves, Gunhee Kim, Leonid Sigal, Andrea Madotto, Paul Crook and Jaime Carbonell. Their work appears in journals such as National University of Singapore and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.