John Reeder

455 citations
19 papers · 226 · h-index 10

Impact in

Papers in

    • Reinforcement Learning in Robotics 3
    • Artificial Intelligence in Games 2
    • Anomaly Detection Techniques and Applications 2
    • Human-Automation Interaction and Safety 3

John Reeder

17 papers receiving 188 citations

Peers

John Reeder
Comparison fields: 5 of 56
  • Earth-Surface Processes 56
  • Oceanography 93
  • Numerical Analysis 34
  • Applied Mathematics 23
  • Algebra and Number Theory 9
Replace Yu. I. Shokin with:
Yu. I. Shokin Russia
Julia Mullen United States
J.M. DeLaurentis United States
Yu. S. Osipov Russia
Keith D. Ward United Kingdom
Alexander A. Davydov Russia
Daniel Rudolf Germany
F. Posner United States
Fabio Luporini United Kingdom
Vassilios Dallas United Kingdom
John Reeder relative to Yu. I. Shokin Russia Yu. I. Shokin's profile →
Citations per field
00.5×6.6×
Yu. I. Shokin · 1×
Citations per year

Countries citing papers authored by John Reeder

Since Specialization
Citations

This map shows the geographic impact of John Reeder'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 John Reeder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Reeder more than expected).

Fields of papers citing papers by John Reeder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Reeder. 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 John Reeder. The network helps show where John Reeder may publish in the future.

Co-authors

The 13 scholars most cited alongside John Reeder, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John Reeder Line = papers co-authored together John Reeder links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 198148
2 198129
3 198124
4 200822
5 201620
6 198118
7 202014
8
197611
9 197910
10 20179
11 20086
12 20176
13 20085
14 19731
15 20191
16 20171
17 20021
18 20130
19 19710

About John Reeder

John Reeder is a scholar working on Artificial Intelligence, Social Psychology, Mathematical Physics, Oceanography and Surgery, having authored 19 papers that have together received 226 indexed citations. Recurring topics across this work include Human-Automation Interaction and Safety (3 papers), Reinforcement Learning in Robotics (3 papers), Artificial Intelligence in Games (2 papers), Anomaly Detection Techniques and Applications (2 papers), Spectral Theory in Mathematical Physics (2 papers), Infrared Target Detection Methodologies (2 papers), Healthcare Technology and Patient Monitoring (2 papers) and Ocean Waves and Remote Sensing (2 papers). The work is most often cited by research in Earth-Surface Processes (56 citations), Oceanography (93 citations), Numerical Analysis (34 citations), Applied Mathematics (23 citations) and Algebra and Number Theory (9 citations). John Reeder has collaborated with scholars based in United States, Canada and Puerto Rico. Frequent co-authors include Marvin Shinbrot, Robert S. Gutzwiller, Michael Georgiopoulos, Jimmy Secretan, Anna Koufakou, Evangelos A. Theodorou, David D. Fan, Jessica L. Sparks, Gita Sukthankar and Georgios C. Anagnostopoulos. Their work appears in journals such as Indiana University Mathematics Journal, Archive for Rational Mechanics and Analysis, Human Factors The Journal of the Human Factors and Ergonomics Society, Proceedings of the American Mathematical Society and Nonlinear Analysis.

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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