Devin Schwab
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
Papers in
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- Reinforcement Learning in Robotics 5
- Adversarial Robustness in Machine Learning 2
- Data Stream Mining Techniques 2
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- Video Surveillance and Tracking Methods 1
- Co-authors
- Soumya Ray (1 shared paper)Manuela Veloso (3 shared papers)Roland Hafner (1 shared paper)K. B. Bhasin (1 shared paper)Thomas Lampe (1 shared paper)Francesco Nori (1 shared paper)Yifeng Zhu (1 shared paper)Martin Riedmiller (1 shared paper)
- Journals
- Machine Learning (1 paper)NASA STI Repository (National Aeronautics and Space Administration) (1 paper)Adaptive Agents and Multi-Agents Systems (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesGermanySwitzerland
In The Last Decade
Devin Schwab
7 papers receiving 212 citations
Peers
Comparison fields: 5 of 52
- Computational Mathematics 3
- Artificial Intelligence 127
- Computational Theory and Mathematics 49
- Control and Systems Engineering 53
- Management Science and Operations Research 26
Countries citing papers authored by Devin Schwab
This map shows the geographic impact of Devin Schwab'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 Devin Schwab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Devin Schwab more than expected).
Fields of papers citing papers by Devin Schwab
This network shows the impact of papers produced by Devin Schwab. 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 Devin Schwab. The network helps show where Devin Schwab may publish in the future.
Co-authors
The 12 scholars most cited alongside Devin Schwab, 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 | FOR LEAST-SQUARES POLICY ITERATION | 2016 | 194 |
| 2 | 2017 | 7 | |
| 3 | 2018 | 7 | |
| 4 | 2019 | 5 | |
| 5 | 2019 | 2 | |
| 6 | 2020 | 1 | |
| 7 | 2012 | 1 |
About Devin Schwab
Devin Schwab is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Control and Systems Engineering and Computational Mathematics, having authored 7 papers that have together received 217 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Adversarial Robustness in Machine Learning (2 papers), Data Stream Mining Techniques (2 papers), Mobile Ad Hoc Networks (1 paper), Tensor decomposition and applications (1 paper), Video Surveillance and Tracking Methods (1 paper), Markov Chains and Monte Carlo Methods (1 paper) and Teaching and Learning Programming (1 paper). The work is most often cited by research in Computational Mathematics (3 citations), Artificial Intelligence (127 citations), Computational Theory and Mathematics (49 citations), Control and Systems Engineering (53 citations) and Management Science and Operations Research (26 citations). Devin Schwab has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include Soumya Ray, Manuela Veloso, Roland Hafner, K. B. Bhasin, Thomas Lampe, Francesco Nori, Yifeng Zhu, Martin Riedmiller, David Bittner and Rachel Coulter. Their work appears in journals such as Machine Learning, NASA STI Repository (National Aeronautics and Space Administration), Adaptive Agents and Multi-Agents Systems 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.