James Tu
Impact in
- Automotive Engineering top 2%
- Autonomous Vehicle Technology and Safety
- Artificial Intelligence top 5%
- Adversarial Robustness in Machine Learning
- Reinforcement Learning in Robotics
Papers in
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- Autonomous Vehicle Technology and Safety 3
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- Adversarial Robustness in Machine Learning 3
- Domain Adaptation and Few-Shot Learning 1
- Machine Learning and Algorithms 1
- Co-authors
- Raquel Urtasun (5 shared papers)Sivabalan Manivasagam (3 shared papers)Mengye Ren (3 shared papers)Jingkang Wang (2 shared papers)Bin Yang (2 shared papers)Sergio Casas (3 shared papers)Ming Xuan Liang (1 shared paper)Abbas Sadat (2 shared papers)
- Journals
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (3 papers)IEEE International Conference on Distributed Computing Systems (ICDCS) (1 paper)IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1 paper)
- Partner nations
- CanadaUnited StatesTaiwan
In The Last Decade
James Tu
6 papers receiving 437 citations
Peers
Comparison fields: 5 of 34
- Automotive Engineering 178
- Artificial Intelligence 231
- Instrumentation 9
- Software 18
- Safety, Risk, Reliability and Quality 28
Countries citing papers authored by James Tu
This map shows the geographic impact of James Tu'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 James Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Tu more than expected).
Fields of papers citing papers by James Tu
This network shows the impact of papers produced by James Tu. 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 James Tu. The network helps show where James Tu may publish in the future.
Co-authors
The 12 scholars most cited alongside James Tu, 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 | 177 | |
| 2 | 2021 | 148 | |
| 3 | 2019 | 51 | |
| 4 | 2021 | 39 | |
| 5 | 2023 | 23 | |
| 6 | 2021 | 9 |
About James Tu
James Tu is a scholar working on Automotive Engineering, Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering and Information Systems, having authored 6 papers that have together received 447 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Autonomous Vehicle Technology and Safety (3 papers), Cloud Computing and Resource Management (1 paper), Distributed and Parallel Computing Systems (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Traffic control and management (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Automotive Engineering (178 citations), Artificial Intelligence (231 citations), Instrumentation (9 citations), Software (18 citations) and Safety, Risk, Reliability and Quality (28 citations). James Tu has collaborated with scholars based in Canada, United States and Taiwan. Frequent co-authors include Raquel Urtasun, Sivabalan Manivasagam, Mengye Ren, Jingkang Wang, Bin Yang, Sergio Casas, Ming Xuan Liang, Abbas Sadat, Zhiming Hu and Baochun Li. Their work appears in journals such as IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE/CVF International Conference on Computer Vision (ICCV) and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.