Tu Vu
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Speech and dialogue systems
- Speech Recognition and Synthesis
- Sentiment Analysis and Opinion Mining
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 14
- Natural Language Processing Techniques 11
- Speech and dialogue systems 2
- Domain Adaptation and Few-Shot Learning 1
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- Expert finding and Q&A systems 2
- Spam and Phishing Detection 1
- Co-authors
- Noah Constant (3 shared papers)Brian Lester (2 shared papers)Daniel Cer (2 shared papers)Rami Al‐Rfou (1 shared paper)Mohit Iyyer (5 shared papers)Quan Hung Tran (1 shared paper)Vu Tran (1 shared paper)Le-Minh Nguyen (1 shared paper)
- Journals
- International Review of Education (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United StatesVietnamJapan
In The Last Decade
Tu Vu
15 papers receiving 329 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 283
- Computer Vision and Pattern Recognition 77
- Health Informatics 4
- Information Systems 57
- Marketing 12
Countries citing papers authored by Tu Vu
This map shows the geographic impact of Tu Vu'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 Tu Vu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tu Vu more than expected).
Fields of papers citing papers by Tu Vu
This network shows the impact of papers produced by Tu Vu. 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 Tu Vu. The network helps show where Tu Vu may publish in the future.
Co-authors
The 25 scholars most cited alongside Tu Vu, 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 | 114 | |
| 2 | 2020 | 63 | |
| 3 | 2015 | 49 | |
| 4 | 2024 | 26 | |
| 5 | 2017 | 16 | |
| 6 | 2021 | 14 | |
| 7 | 2022 | 12 | |
| 8 | 2023 | 8 | |
| 9 | 2018 | 8 | |
| 10 | 2024 | 7 | |
| 11 | 2022 | 6 | |
| 12 | 2017 | 6 | |
| 13 | 2019 | 5 | |
| 14 | 2023 | 4 | |
| 15 | 2019 | 1 | |
| 16 | 2025 | 0 | |
| 17 | 2025 | 0 |
About Tu Vu
Tu Vu is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Marketing, having authored 17 papers that have together received 339 indexed citations. Recurring topics across this work include Topic Modeling (14 papers), Natural Language Processing Techniques (11 papers), Multimodal Machine Learning Applications (4 papers), Expert finding and Q&A systems (2 papers), Speech and dialogue systems (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Spam and Phishing Detection (1 paper) and Second Language Learning and Teaching (1 paper). The work is most often cited by research in Artificial Intelligence (283 citations), Computer Vision and Pattern Recognition (77 citations), Health Informatics (4 citations), Information Systems (57 citations) and Marketing (12 citations). Tu Vu has collaborated with scholars based in United States, Vietnam and Japan. Frequent co-authors include Noah Constant, Brian Lester, Daniel Cer, Rami Al‐Rfou, Mohit Iyyer, Quan Hung Tran, Vu Tran, Le-Minh Nguyen, Son Bao Pham and Andrew Mattarella-Micke. Their work appears in journals such as International Review of Education, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 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.