Aaron Ray
Impact in
-
- Neural Networks and Applications
- Reinforcement Learning in Robotics
- Educational Robotics and Engineering
- Neural Networks and Reservoir Computing
-
- Robotics and Sensor-Based Localization
Papers in
-
- Neural Networks and Reservoir Computing 1
- Reinforcement Learning in Robotics 1
- Neural Networks and Applications 1
-
- Robotic Path Planning Algorithms 2
- Video Surveillance and Tracking Methods 1
- Co-authors
- Daniela L. Rus (4 shared papers)Alexander Amini (2 shared papers)Ramin Hasani (2 shared papers)Mathias Lechner (2 shared papers)Max Tschaikowski (1 shared paper)Gerald Teschl (1 shared paper)Luca Carlone (2 shared papers)Yun Chang (1 shared paper)
- Journals
- Science Robotics (1 paper)Nature Machine Intelligence (1 paper)Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (1 paper)IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (3 papers)
- Partner nations
- United StatesDenmarkAustria
In The Last Decade
Aaron Ray
7 papers receiving 221 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 73
- Aerospace Engineering 36
- Health Informatics 3
- Control and Systems Engineering 28
- Computer Vision and Pattern Recognition 25
Countries citing papers authored by Aaron Ray
This map shows the geographic impact of Aaron Ray'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 Aaron Ray with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aaron Ray more than expected).
Fields of papers citing papers by Aaron Ray
This network shows the impact of papers produced by Aaron Ray. 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 Aaron Ray. The network helps show where Aaron Ray may publish in the future.
Co-authors
The 14 scholars most cited alongside Aaron Ray, 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 | 106 | |
| 2 | 2023 | 59 | |
| 3 | 2018 | 27 | |
| 4 | 2023 | 15 | |
| 5 | 2024 | 13 | |
| 6 | 2021 | 5 | |
| 7 | 2022 | 4 |
About Aaron Ray
Aaron Ray is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Control and Systems Engineering and Aerospace Engineering, having authored 7 papers that have together received 229 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (2 papers), Neural Networks and Reservoir Computing (1 paper), Robotics and Sensor-Based Localization (1 paper), Reinforcement Learning in Robotics (1 paper), Neural Networks and Applications (1 paper), Video Surveillance and Tracking Methods (1 paper), Robot Manipulation and Learning (1 paper) and Distributed Control Multi-Agent Systems (1 paper). The work is most often cited by research in Artificial Intelligence (73 citations), Aerospace Engineering (36 citations), Health Informatics (3 citations), Control and Systems Engineering (28 citations) and Computer Vision and Pattern Recognition (25 citations). Aaron Ray has collaborated with scholars based in United States, Denmark and Austria. Frequent co-authors include Daniela L. Rus, Alexander Amini, Ramin Hasani, Mathias Lechner, Max Tschaikowski, Gerald Teschl, Luca Carlone, Yun Chang, Nathan Hughes and Jared Strader. Their work appears in journals such as Science Robotics, Nature Machine Intelligence, Proceedings - IEEE International Conference on Robotics and Automation/Proceedings 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.