Keuntaek Lee
Impact in
- Automotive Engineering top 10%
- Autonomous Vehicle Technology and Safety
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- Robotic Path Planning Algorithms
- Advanced Neural Network Applications
Papers in
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- Reinforcement Learning in Robotics 6
- Gaussian Processes and Bayesian Inference 2
- Fuzzy Logic and Control Systems 1
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- Autonomous Vehicle Technology and Safety 5
- Co-authors
- Evangelos A. Theodorou (9 shared papers)Kamil Saigol (5 shared papers)Ching-An Cheng (4 shared papers)Xinyan Yan (4 shared papers)Byron Boots (4 shared papers)Yunpeng Pan (4 shared papers)Sangjae Bae (2 shared papers)David Isele (2 shared papers)
- Journals
- IEEE Robotics and Automation Letters (2 papers)The International Journal of Robotics Research (1 paper)2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesSweden
In The Last Decade
Keuntaek Lee
9 papers receiving 288 citations
Peers
Comparison fields: 5 of 53
- Automotive Engineering 118
- Computer Vision and Pattern Recognition 128
- Control and Systems Engineering 98
- Artificial Intelligence 118
- Aerospace Engineering 59
Countries citing papers authored by Keuntaek Lee
This map shows the geographic impact of Keuntaek Lee'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 Keuntaek Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keuntaek Lee more than expected).
Fields of papers citing papers by Keuntaek Lee
This network shows the impact of papers produced by Keuntaek Lee. 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 Keuntaek Lee. The network helps show where Keuntaek Lee may publish in the future.
Co-authors
The 11 scholars most cited alongside Keuntaek Lee, 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 | 2018 | 135 | |
| 2 | 2019 | 88 | |
| 3 | 2020 | 27 | |
| 4 | 2022 | 22 | |
| 5 | Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning. | 2017 | 14 |
| 6 | Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control | 2019 | 7 |
| 7 | 2019 | 3 | |
| 8 | Imitation Learning for Agile Autonomous Driving | 2017 | 1 |
| 9 | 2022 | 1 |
About Keuntaek Lee
Keuntaek Lee is a scholar working on Artificial Intelligence, Automotive Engineering, Control and Systems Engineering, Computer Vision and Pattern Recognition and Aerospace Engineering, having authored 9 papers that have together received 298 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Autonomous Vehicle Technology and Safety (5 papers), Advanced Control Systems Optimization (3 papers), Gaussian Processes and Bayesian Inference (2 papers), Control Systems and Identification (2 papers), Advanced Neural Network Applications (2 papers), Robotics and Sensor-Based Localization (1 paper) and Fuzzy Logic and Control Systems (1 paper). The work is most often cited by research in Automotive Engineering (118 citations), Computer Vision and Pattern Recognition (128 citations), Control and Systems Engineering (98 citations), Artificial Intelligence (118 citations) and Aerospace Engineering (59 citations). Keuntaek Lee has collaborated with scholars based in United States and Sweden. Frequent co-authors include Evangelos A. Theodorou, Kamil Saigol, Ching-An Cheng, Xinyan Yan, Byron Boots, Yunpeng Pan, Sangjae Bae, David Isele, Grady Williams and Brian Goldfain. Their work appears in journals such as IEEE Robotics and Automation Letters, The International Journal of Robotics Research, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 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.