Leonard Tang
Impact in
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- Online Learning and Analytics
Papers in
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- Topic Modeling 2
- Explainable Artificial Intelligence (XAI) 2
- Anomaly Detection Techniques and Applications 1
- Intelligent Tutoring Systems and Adaptive Learning 1
- Natural Language Processing Techniques 1
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Dan Hendrycks (2 shared papers)Andy Zou (1 shared paper)Mantas Mazeika (1 shared paper)Bo Li (1 shared paper)Dawn Song (1 shared paper)Jacob Steinhardt (1 shared paper)Iddo Drori (2 shared papers)Gilbert Strang (1 shared paper)
- Journals
- Proceedings of the National Academy of Sciences (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)
- Partner nations
- United StatesSwitzerlandCanada
In The Last Decade
Leonard Tang
5 papers receiving 150 citations
Peers
Comparison fields: 5 of 51
- Health Informatics 8
- Computer Science Applications 19
- Artificial Intelligence 106
- Theoretical Computer Science 2
- Computer Vision and Pattern Recognition 36
Countries citing papers authored by Leonard Tang
This map shows the geographic impact of Leonard Tang'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 Leonard Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonard Tang more than expected).
Fields of papers citing papers by Leonard Tang
This network shows the impact of papers produced by Leonard Tang. 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 Leonard Tang. The network helps show where Leonard Tang may publish in the future.
Co-authors
The 25 scholars most cited alongside Leonard Tang, 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 | 70 | |
| 2 | 2022 | 50 | |
| 3 | 2022 | 26 | |
| 4 | 2023 | 6 | |
| 5 | 2023 | 6 |
About Leonard Tang
Leonard Tang is a scholar working on Artificial Intelligence, Political Science and International Relations, Strategy and Management, Computer Science Applications and Statistics and Probability, having authored 5 papers that have together received 158 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Dispute Resolution and Class Actions (1 paper), Anomaly Detection Techniques and Applications (1 paper), Statistics Education and Methodologies (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper), Natural Language Processing Techniques (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (8 citations), Computer Science Applications (19 citations), Artificial Intelligence (106 citations), Theoretical Computer Science (2 citations) and Computer Vision and Pattern Recognition (36 citations). Leonard Tang has collaborated with scholars based in United States, Switzerland and Canada. Frequent co-authors include Dan Hendrycks, Andy Zou, Mantas Mazeika, Bo Li, Dawn Song, Jacob Steinhardt, Iddo Drori, Gilbert Strang, Avi Shporer and Jayson Lynch. Their work appears in journals such as Proceedings of the National Academy of Sciences, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and DSpace@MIT (Massachusetts Institute of Technology).
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.