Justin K. Pugh
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics 6
- Evolutionary Algorithms and Applications 5
- Metaheuristic Optimization Algorithms Research 2
- Domain Adaptation and Few-Shot Learning 2
- Anomaly Detection Techniques and Applications 2
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- Evolutionary Game Theory and Cooperation 2
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- Modular Robots and Swarm Intelligence 1
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- Cardiac, Anesthesia and Surgical Outcomes 1
- Co-authors
- Kenneth O. StanleyL. B. SorosPaul SzerlipDavinder RamsinghAndrea SoltoggioMark A. RingerJay LeeAlexandra Chang
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsComputer Vision and Pattern Recognition
- Journals
- Journal of Clinical Medicine (2 papers)Journal of Clinical Anesthesia (1 paper)Frontiers in Robotics and AI (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Justin K. Pugh
12 papers receiving 332 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 240
- Computational Theory and Mathematics 61
- Computer Vision and Pattern Recognition 49
- Industrial and Manufacturing Engineering 21
- Biophysics 8
Countries citing papers authored by Justin K. Pugh
This map shows the geographic impact of Justin K. Pugh'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 Justin K. Pugh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Justin K. Pugh more than expected).
Fields of papers citing papers by Justin K. Pugh
This network shows the impact of papers produced by Justin K. Pugh. 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 Justin K. Pugh. The network helps show where Justin K. Pugh may publish in the future.
Co-authorship network
The 11 scholars most cited alongside Justin K. Pugh, 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 | 2020 | 1 | |
| 2 | 2020 | 3 | |
| 3 | 2019 | 5 | |
| 4 | 2017 | 4 | |
| 5 | 2016 | 2 | |
| 6 | 2016 | 239 | |
| 7 | 2016 | 1 | |
| 8 | 2015 | 57 | |
| 9 | 2015 | 10 | |
| 10 | 2014 | 2 | |
| 11 | 2014 | 1 | |
| 12 | 2013 | 16 |
About Justin K. Pugh
Justin K. Pugh is a scholar working on Artificial Intelligence, Critical Care and Intensive Care Medicine and Anesthesiology and Pain Medicine, having authored 12 papers that have together received 341 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Evolutionary Algorithms and Applications (5 papers), Evolutionary Game Theory and Cooperation (2 papers), Metaheuristic Optimization Algorithms Research (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Anomaly Detection Techniques and Applications (2 papers), Modular Robots and Swarm Intelligence (1 paper) and Cardiac, Anesthesia and Surgical Outcomes (1 paper). The work is most often cited by research in Artificial Intelligence (240 citations), Computational Theory and Mathematics (61 citations) and Computer Vision and Pattern Recognition (49 citations). Justin K. Pugh has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Kenneth O. Stanley, L. B. Soros, Paul Szerlip, Davinder Ramsingh, Andrea Soltoggio, Mark A. Ringer, Jay Lee, Alexandra Chang, Linda J. Mason and Brent M. Gordon. Their work appears in journals such as Journal of Clinical Medicine, Journal of Clinical Anesthesia, Frontiers in Robotics and AI, Proceedings of the Genetic and Evolutionary Computation Conference Companion and Proceedings of the AAAI 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.