Chao Qu
- Plant Science top 5%
- Computer Vision and Pattern Recognition top 5%
- Aerospace Engineering top 5%
- Human-Computer Interaction top 2%
- Mechanical Engineering top 10%
- Co-authors
- Camillo J. TaylorVijay KumarJnaneshwar DasSteven W. ChenWillem‐Paul BrinkmanIngrid HeynderickxYun LingShreyas S. Shivakumar
- Topics
- Virtual Reality Applications and Impacts (8 papers)Robotics and Sensor-Based Localization (6 papers)Action Observation and Synchronization (5 papers)
- Partner nations
- United StatesChinaNetherlands
In The Last Decade
Chao Qu
36 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Plant Science 407
- Computer Vision and Pattern Recognition 340
- Aerospace Engineering 265
- Human-Computer Interaction 186
- Mechanical Engineering 180
Countries citing papers authored by Chao Qu
This map shows the geographic impact of Chao Qu'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 Chao Qu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chao Qu more than expected).
Fields of papers citing papers by Chao Qu
This network shows the impact of papers produced by Chao Qu. 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 Chao Qu. The network helps show where Chao Qu may publish in the future.
Co-authorship network of co-authors of Chao Qu
This figure shows the co-authorship network connecting the top 25 collaborators of Chao Qu. A scholar is included among the top collaborators of Chao Qu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chao Qu. Chao Qu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 8 | |
| 5 | 7 | |
| 6 | 30 | |
| 7 | 18 | |
| 8 | 22 | |
| 9 | Nonlinear Distributional Gradient Temporal-Difference Learning | 2 |
| 10 | Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning | 1 |
| 11 | Non-convex Conditional Gradient Sliding | 3 |
| 12 | 10 | |
| 13 | Fast rate analysis of some stochastic optimization algorithms | 1 |
| 14 | Subspace clustering with irrelevant features via robust Dantzig selector | 10 |
| 15 | 27 | |
| 16 | 40 | |
| 17 | 16 | |
| 18 | 60 | |
| 19 | 4 | |
| 20 | 0 |
About Chao Qu
Chao Qu is a scholar working on Human-Computer Interaction, Media Technology and Computer Vision and Pattern Recognition, having authored 39 papers that have together received 1.3k indexed citations. Recurring topics across this work include Virtual Reality Applications and Impacts (8 papers), Robotics and Sensor-Based Localization (6 papers) and Action Observation and Synchronization (5 papers). The work is most often cited by research in Human-Computer Interaction (186 citations), Computer Vision and Pattern Recognition (340 citations) and Analytical Chemistry (142 citations). Chao Qu has collaborated with scholars based in United States, China and Netherlands. Frequent co-authors include Camillo J. Taylor, Vijay Kumar, Jnaneshwar Das, Steven W. Chen, Willem‐Paul Brinkman, Ingrid Heynderickx, Yun Ling, Shreyas S. Shivakumar, Dahu Zhu and Harold T. Nefs. Their work appears in journals such as PLoS ONE, Computers in Human Behavior and IEEE Access.
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.