Nate Kohl
- Artificial Intelligence top 2%
- Reinforcement Learning in Robotics 7
- Evolutionary Algorithms and Applications 6
- Neural Networks and Applications 4
- Metaheuristic Optimization Algorithms Research 2
- Gaussian Processes and Bayesian Inference 2
- Control and Systems Engineering top 10%
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- Robotic Locomotion and Control 3
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- Autonomous Vehicle Technology and Safety 2
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- Aging, Elder Care, and Social Issues 1
- Co-authors
- Peter StoneRisto MiikkulainenKenneth O. StanleyShimon WhitesonRini SheronyGregory KuhlmannNicholas K. JongMohan Sridharan
- Cited by
- Artificial IntelligenceControl and Systems EngineeringComputer Vision and Pattern Recognition
- Journals
- Robotics and Autonomous Systems (1 paper)Neural Networks (1 paper)Machine Learning (1 paper)
- Partner nations
- United States
In The Last Decade
Nate Kohl
11 papers receiving 669 citations
Peers
Comparison fields: 5 of 69
- Artificial Intelligence 481
- Control and Systems Engineering 155
- Computer Vision and Pattern Recognition 109
- Biomedical Engineering 194
- Computational Theory and Mathematics 50
Countries citing papers authored by Nate Kohl
This map shows the geographic impact of Nate Kohl'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 Nate Kohl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nate Kohl more than expected).
Fields of papers citing papers by Nate Kohl
This network shows the impact of papers produced by Nate Kohl. 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 Nate Kohl. The network helps show where Nate Kohl may publish in the future.
Co-authorship network
The 9 scholars most cited alongside Nate Kohl, 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 | 2012 | 11 | |
| 2 | Evolving neural networks for strategic decision-making problems | 2009 | 0 |
| 3 | 2009 | 38 | |
| 4 | Learning in fractured problems with constructive neural network algorithms | 2009 | 5 |
| 5 | 2008 | 12 | |
| 6 | 2006 | 11 | |
| 7 | 2006 | 40 | |
| 8 | 2005 | 37 | |
| 9 | 2005 | 60 | |
| 10 | 2005 | 68 | |
| 11 | Machine learning for fast quadrupedal locomotion | 2004 | 92 |
| 12 | 2004 | 346 |
About Nate Kohl
Nate Kohl is a scholar working on Artificial Intelligence, Software and Automotive Engineering, having authored 12 papers that have together received 720 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Evolutionary Algorithms and Applications (6 papers), Neural Networks and Applications (4 papers), Robotic Locomotion and Control (3 papers), Metaheuristic Optimization Algorithms Research (2 papers), Autonomous Vehicle Technology and Safety (2 papers), Gaussian Processes and Bayesian Inference (2 papers) and Aging, Elder Care, and Social Issues (1 paper). The work is most often cited by research in Artificial Intelligence (481 citations), Control and Systems Engineering (155 citations) and Computer Vision and Pattern Recognition (109 citations). Nate Kohl has collaborated with scholars based in United States. Frequent co-authors include Peter Stone, Risto Miikkulainen, Kenneth O. Stanley, Shimon Whiteson, Rini Sherony, Risto Miikkulainen, Gregory Kuhlmann, Nicholas K. Jong and Mohan Sridharan. Their work appears in journals such as Robotics and Autonomous Systems, Neural Networks, Machine Learning, IEEE Transactions on Evolutionary Computation and National Conference on Artificial Intelligence.
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.