Zai Huang
Impact in
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- Online Learning and Analytics
- Artificial Intelligence top 5%
- Intelligent Tutoring Systems and Adaptive Learning
- Topic Modeling
- Text and Document Classification Technologies
- Advanced Graph Neural Networks
- Sentiment Analysis and Opinion Mining
- Natural Language Processing Techniques
Papers in
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- Intelligent Tutoring Systems and Adaptive Learning 2
- Topic Modeling 2
- Advanced Text Analysis Techniques 1
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- Recommender Systems and Techniques 3
- Co-authors
- Enhong Chen (5 shared papers)Shijin Wang (2 shared papers)Yuying Chen (2 shared papers)Zhenya Huang (2 shared papers)Qi Liu (2 shared papers)Fei Wang (1 shared paper)Yu Yin (1 shared paper)Dan Zhang (1 shared paper)
- Journals
- Frontiers of Computer Science (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Zai Huang
7 papers receiving 354 citations
Peers
Comparison fields: 5 of 67
- Computer Science Applications 115
- Artificial Intelligence 281
- Information Systems 86
- Computer Vision and Pattern Recognition 53
- Health Informatics 3
Countries citing papers authored by Zai Huang
This map shows the geographic impact of Zai Huang'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 Zai Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zai Huang more than expected).
Fields of papers citing papers by Zai Huang
This network shows the impact of papers produced by Zai Huang. 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 Zai Huang. The network helps show where Zai Huang may publish in the future.
Co-authors
The 24 scholars most cited alongside Zai Huang, 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 | 2020 | 158 | |
| 2 | 2019 | 94 | |
| 3 | 2018 | 50 | |
| 4 | 2020 | 38 | |
| 5 | 2017 | 11 | |
| 6 | 2021 | 8 | |
| 7 | 2021 | 6 |
About Zai Huang
Zai Huang is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 7 papers that have together received 365 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (3 papers), Caching and Content Delivery (2 papers), Intelligent Tutoring Systems and Adaptive Learning (2 papers), Advanced Bandit Algorithms Research (2 papers), Topic Modeling (2 papers), Advanced Text Analysis Techniques (1 paper), Image and Video Quality Assessment (1 paper) and Online Learning and Analytics (1 paper). The work is most often cited by research in Computer Science Applications (115 citations), Artificial Intelligence (281 citations), Information Systems (86 citations), Computer Vision and Pattern Recognition (53 citations) and Health Informatics (3 citations). Zai Huang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Enhong Chen, Shijin Wang, Yuying Chen, Zhenya Huang, Qi Liu, Fei Wang, Yu Yin, Dan Zhang, Wei Huang and Zhou Zhao. Their work appears in journals such as Frontiers of Computer Science and Proceedings of the AAAI Conference on Artificial Intelligence.
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.