Adam Dai
Impact in
- Polymers and Plastics top 10%
- Conducting polymers and applications
- Biomedical Engineering top 5%
- Advanced Sensor and Energy Harvesting Materials
Papers in
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- Robotic Path Planning Algorithms 2
- Advanced Neural Network Applications 2
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- Motor Control and Adaptation 2
- Tactile and Sensory Interactions 2
- Neural dynamics and brain function 1
- Co-authors
- Yiran Yang (1 shared paper)Aaron D. Ames (1 shared paper)Wei Gao (1 shared paper)You Yu (1 shared paper)Rohan Doshi (1 shared paper)Rachel Gehlhar (1 shared paper)Joanna M. Nassar (1 shared paper)Yu Song (1 shared paper)
- Journals
- IEEE Transactions on Automatic Control (1 paper)Journal of Visualized Experiments (1 paper)Science Robotics (1 paper)Proceedings of the Satellite Division's International Technical Meeting (Online) (1 paper)CaltechAUTHORS (California Institute of Technology) (2 papers)
- Partner nations
- United StatesChina
In The Last Decade
Adam Dai
8 papers receiving 590 citations
Adam Dai's Hit Papers
Peers
Comparison fields: 5 of 70
- Polymers and Plastics 166
- Biomedical Engineering 478
- Cognitive Neuroscience 144
- Bioengineering 38
- Human-Computer Interaction 26
Countries citing papers authored by Adam Dai
This map shows the geographic impact of Adam Dai'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 Adam Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam Dai more than expected).
Fields of papers citing papers by Adam Dai
This network shows the impact of papers produced by Adam Dai. 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 Adam Dai. The network helps show where Adam Dai may publish in the future.
Co-authors
The 17 scholars most cited alongside Adam Dai, 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 | Biofuel-powered soft electronic skin with multiplexed and wireless sensing for human-machine interfaces Hit paper breakdown → | 2020 | 551 |
| 2 | 2022 | 35 | |
| 3 | 2016 | 4 | |
| 4 | 2019 | 3 | |
| 5 | 2023 | 2 | |
| 6 | 2023 | 2 | |
| 7 | 2020 | 1 | |
| 8 | 2023 | 1 |
About Adam Dai
Adam Dai is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience, Artificial Intelligence, Aerospace Engineering and Social Psychology, having authored 8 papers that have together received 599 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (2 papers), Motor Control and Adaptation (2 papers), Tactile and Sensory Interactions (2 papers), Advanced Neural Network Applications (2 papers), Robotics and Sensor-Based Localization (2 papers), AI-based Problem Solving and Planning (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Polymers and Plastics (166 citations), Biomedical Engineering (478 citations), Cognitive Neuroscience (144 citations), Bioengineering (38 citations) and Human-Computer Interaction (26 citations). Adam Dai has collaborated with scholars based in United States and China. Frequent co-authors include Yiran Yang, Aaron D. Ames, Wei Gao, You Yu, Rohan Doshi, Rachel Gehlhar, Joanna M. Nassar, Yu Song, Changhao Xu and Jihong Min. Their work appears in journals such as IEEE Transactions on Automatic Control, Journal of Visualized Experiments, Science Robotics, Proceedings of the Satellite Division's International Technical Meeting (Online) and CaltechAUTHORS (California 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.