Trevor Davis
Impact in
- Artificial Intelligence top 5%
- Artificial Intelligence in Games
- Reinforcement Learning in Robotics
- Health Informatics top 10%
Papers in
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- Artificial Intelligence in Games 3
- Reinforcement Learning in Robotics 2
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- Sports Analytics and Performance 2
- Co-authors
- Michael Bowling (3 shared papers)Viliam Lisý (2 shared papers)Neil Burch (2 shared papers)Michael Johanson (1 shared paper)Dustin Morrill (1 shared paper)Martin Schmid (1 shared paper)Nolan Bard (1 shared paper)Kevin Waugh (1 shared paper)
- Journals
- Science (1 paper)Journal of Communication (1 paper)Journal of Global Health Reports (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)NASA Technical Reports Server (NASA) (1 paper)
- Partner nations
- CanadaUnited StatesCzechia
In The Last Decade
Trevor Davis
8 papers receiving 459 citations
Trevor Davis's Hit Papers
Peers
Comparison fields: 5 of 100
- Artificial Intelligence 293
- Health Informatics 12
- Management Science and Operations Research 59
- General Decision Sciences 5
- Economics and Econometrics 75
Countries citing papers authored by Trevor Davis
This map shows the geographic impact of Trevor Davis'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 Trevor Davis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Trevor Davis more than expected).
Fields of papers citing papers by Trevor Davis
This network shows the impact of papers produced by Trevor Davis. 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 Trevor Davis. The network helps show where Trevor Davis may publish in the future.
Co-authors
The 13 scholars most cited alongside Trevor Davis, 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 | DeepStack: Expert-level artificial intelligence in heads-up no-limit poker Hit paper breakdown → | 2017 | 409 |
| 2 | 2023 | 35 | |
| 3 | 2016 | 12 | |
| 4 | 2017 | 9 | |
| 5 | 2014 | 9 | |
| 6 | Fortran Automatic Code Evaluation System (FACES) | 1974 | 4 |
| 7 | C-Like 'getopt' Behavior [R package getopt version 1.20.3] | 2019 | 1 |
| 8 | 2018 | 1 |
About Trevor Davis
Trevor Davis is a scholar working on Artificial Intelligence, Economics and Econometrics, Sociology and Political Science, Communication and Clinical Psychology, having authored 8 papers that have together received 480 indexed citations. Recurring topics across this work include Artificial Intelligence in Games (3 papers), Reinforcement Learning in Robotics (2 papers), Sports Analytics and Performance (2 papers), Media Influence and Politics (1 paper), Social Media and Politics (1 paper), Optimal Power Flow Distribution (1 paper), Photovoltaic System Optimization Techniques (1 paper) and Game Theory and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (293 citations), Health Informatics (12 citations), Management Science and Operations Research (59 citations), General Decision Sciences (5 citations) and Economics and Econometrics (75 citations). Trevor Davis has collaborated with scholars based in Canada, United States and Czechia. Frequent co-authors include Michael Bowling, Viliam Lisý, Neil Burch, Michael Johanson, Dustin Morrill, Martin Schmid, Nolan Bard, Kevin Waugh, Matej Moravčík and Matthew Hindman. Their work appears in journals such as Science, Journal of Communication, Journal of Global Health Reports, Proceedings of the AAAI Conference on Artificial Intelligence and NASA Technical Reports Server (NASA).
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.