Kagan Tumer

96 papers and 1.9k indexed citations i.

About

Kagan Tumer is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Networks and Communications. According to data from OpenAlex, Kagan Tumer has authored 96 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Artificial Intelligence, 23 papers in Management Science and Operations Research and 13 papers in Computer Networks and Communications. Recurrent topics in Kagan Tumer’s work include Reinforcement Learning in Robotics (42 papers), Auction Theory and Applications (14 papers) and Adaptation to Concept Drift in Data Streams (13 papers). Kagan Tumer is often cited by papers focused on Reinforcement Learning in Robotics (42 papers), Auction Theory and Applications (14 papers) and Adaptation to Concept Drift in Data Streams (13 papers). Kagan Tumer collaborates with scholars based in United States, Switzerland and United Kingdom. Kagan Tumer's co-authors include Joydeep Ghosh, Adrian Agogino, David H. Wolpert, Nikunj C. Oza, Jen Jen Chung, Roland Siegwart, Damjan Miklić, Lorenzo Sabattini, Kevin Wheeler and Rebecca Richards‐Kortum and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, EPL (Europhysics Letters) and Information Fusion.

In The Last Decade

Co-authorship network of co-authors of Kagan Tumer i

Fields of papers citing papers by Kagan Tumer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Kagan Tumer

Since Specialization
Citations

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

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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