Harris Chan
Impact in
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- Multimodal Machine Learning Applications
- Robotic Path Planning Algorithms
- Advanced Neural Network Applications
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- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
- Evolutionary Algorithms and Applications
Papers in
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- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 1
- Machine Learning and ELM 1
- Advanced Graph Neural Networks 1
- Reinforcement Learning in Robotics 1
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- Smart Grid Security and Resilience 1
- Co-authors
- Jimmy Ba (5 shared papers)Silviu Pitis (2 shared papers)Bradly C. Stadie (1 shared paper)Jonathan Tompson (1 shared paper)Ayzaan Wahid (1 shared paper)Ted Xiao (1 shared paper)Anthony Brohan (1 shared paper)Karol Hausman (1 shared paper)
- Journals
- International Conference on Artificial Intelligence and Statistics (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- Canada
In The Last Decade
Harris Chan
6 papers receiving 50 citations
Peers
Comparison fields: 5 of 22
- Computer Vision and Pattern Recognition 22
- Artificial Intelligence 34
- Control and Systems Engineering 18
- Computational Theory and Mathematics 7
- Aerospace Engineering 6
Countries citing papers authored by Harris Chan
This map shows the geographic impact of Harris Chan'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 Harris Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harris Chan more than expected).
Fields of papers citing papers by Harris Chan
This network shows the impact of papers produced by Harris Chan. 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 Harris Chan. The network helps show where Harris Chan may publish in the future.
Co-authors
The 15 scholars most cited alongside Harris Chan, 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 | 2023 | 22 | |
| 2 | 2020 | 18 | |
| 3 | Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise. | 2019 | 7 |
| 4 | 2016 | 2 | |
| 5 | An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise | 2020 | 1 |
| 6 | 2020 | 1 | |
| 7 | 2023 | 0 |
About Harris Chan
Harris Chan is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 7 papers that have together received 51 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), Machine Learning and Data Classification (1 paper), Sparse and Compressive Sensing Techniques (1 paper), Machine Learning and ELM (1 paper), Advanced Graph Neural Networks (1 paper), Network Security and Intrusion Detection (1 paper), Smart Grid Security and Resilience (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (22 citations), Artificial Intelligence (34 citations), Control and Systems Engineering (18 citations), Computational Theory and Mathematics (7 citations) and Aerospace Engineering (6 citations). Harris Chan has collaborated with scholars based in Canada. Frequent co-authors include Jimmy Ba, Silviu Pitis, Bradly C. Stadie, Jonathan Tompson, Ayzaan Wahid, Ted Xiao, Anthony Brohan, Karol Hausman, Sergey Levine and Pierre Sermanet. Their work appears in journals such as International Conference on Artificial Intelligence and Statistics and arXiv (Cornell University).
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.