Keuntaek Lee

554 total citations
9 papers, 292 citations indexed

About

Keuntaek Lee is a scholar working on Artificial Intelligence, Automotive Engineering and Control and Systems Engineering. According to data from OpenAlex, Keuntaek Lee has authored 9 papers receiving a total of 292 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Automotive Engineering and 4 papers in Control and Systems Engineering. Recurrent topics in Keuntaek Lee's work include Reinforcement Learning in Robotics (6 papers), Autonomous Vehicle Technology and Safety (5 papers) and Advanced Control Systems Optimization (3 papers). Keuntaek Lee is often cited by papers focused on Reinforcement Learning in Robotics (6 papers), Autonomous Vehicle Technology and Safety (5 papers) and Advanced Control Systems Optimization (3 papers). Keuntaek Lee collaborates with scholars based in United States and Sweden. Keuntaek Lee's co-authors include Evangelos A. Theodorou, Kamil Saigol, Yunpeng Pan, Ching-An Cheng, Xinyan Yan, Byron Boots, David Isele, Sangjae Bae, Grady Williams and James M. Rehg and has published in prestigious journals such as The International Journal of Robotics Research, IEEE Robotics and Automation Letters and arXiv (Cornell University).

In The Last Decade

Keuntaek Lee

9 papers receiving 280 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Keuntaek Lee United States	6	127	117	117	97	58	9	292
Kamil Saigol United States	5	118 0.9×	105 0.9×	115 1.0×	109 1.1×	47 0.8×	8	291
Jingwei Zhang Germany	3	198 1.6×	64 0.5×	185 1.6×	61 0.6×	70 1.2×	5	333
Markus Spies Germany	6	208 1.6×	112 1.0×	143 1.2×	91 0.9×	49 0.8×	11	372
Alireza Nakhaei United States	9	115 0.9×	147 1.3×	74 0.6×	150 1.5×	28 0.5×	15	279
Daniel Althoff Germany	9	194 1.5×	167 1.4×	52 0.4×	126 1.3×	65 1.1×	14	315
S. Kreiss Switzerland	5	250 2.0×	143 1.2×	186 1.6×	61 0.6×	47 0.8×	8	396
Beat Flepp United States	3	190 1.5×	127 1.1×	111 0.9×	57 0.6×	68 1.2×	3	300
Adarsh Jagan Sathyamoorthy United States	9	226 1.8×	46 0.4×	75 0.6×	62 0.6×	99 1.7×	17	335
Yuanfu Luo Singapore	8	137 1.1×	162 1.4×	79 0.7×	75 0.8×	22 0.4×	10	272
Stefan Vacek Germany	10	213 1.7×	84 0.7×	57 0.5×	55 0.6×	41 0.7×	16	308

Countries citing papers authored by Keuntaek Lee

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Keuntaek Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Keuntaek Lee. A scholar is included among the top collaborators of Keuntaek Lee 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 Keuntaek Lee. Keuntaek Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Lee, Keuntaek, David Isele, Evangelos A. Theodorou, & Sangjae Bae. (2022). Risk-sensitive MPCs with Deep Distributional Inverse RL for Autonomous Driving. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 32. 7635–7642. 1 indexed citations

2.

Lee, Keuntaek, David Isele, Evangelos A. Theodorou, & Sangjae Bae. (2022). Spatiotemporal Costmap Inference for MPC Via Deep Inverse Reinforcement Learning. IEEE Robotics and Automation Letters. 7(2). 3194–3201. 19 indexed citations

3.

Lee, Keuntaek, et al.. (2020). Aggressive Perception-Aware Navigation Using Deep Optical Flow Dynamics and PixelMPC. IEEE Robotics and Automation Letters. 5(2). 1207–1214. 27 indexed citations

4.

Williams, Grady, et al.. (2019). Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control. 969–978. 7 indexed citations

5.

Lee, Keuntaek, Kamil Saigol, & Evangelos A. Theodorou. (2019). Early Failure Detection of Deep End-to-End Control Policy by Reinforcement Learning. 8543–8549. 3 indexed citations

6.

Pan, Yunpeng, Ching-An Cheng, Kamil Saigol, et al.. (2019). Imitation learning for agile autonomous driving. The International Journal of Robotics Research. 39(2-3). 286–302. 86 indexed citations

7.

Pan, Yunpeng, Ching-An Cheng, Kamil Saigol, et al.. (2018). Agile Autonomous Driving using End-to-End Deep Imitation Learning. 134 indexed citations

8.

Pan, Yunpeng, Ching-An Cheng, Kamil Saigol, et al.. (2017). Imitation Learning for Agile Autonomous Driving. arXiv (Cornell University). 1 indexed citations

9.

Pan, Yunpeng, Ching-An Cheng, Kamil Saigol, et al.. (2017). Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning.. 14 indexed citations

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.

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