Daniel Toyama

1.4k total citations
2 papers, 16 citations indexed

About

Daniel Toyama is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Infectious Diseases. According to data from OpenAlex, Daniel Toyama has authored 2 papers receiving a total of 16 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Artificial Intelligence, 1 paper in Computational Theory and Mathematics and 0 papers in Infectious Diseases. Recurrent topics in Daniel Toyama's work include Reinforcement Learning in Robotics (2 papers), Evolutionary Algorithms and Applications (1 paper) and Computability, Logic, AI Algorithms (1 paper). Daniel Toyama is often cited by papers focused on Reinforcement Learning in Robotics (2 papers), Evolutionary Algorithms and Applications (1 paper) and Computability, Logic, AI Algorithms (1 paper). Daniel Toyama collaborates with scholars based in Canada and United States. Daniel Toyama's co-authors include Jonathan J. Hunt, Shaobo Hou, Philippe Hamel, Diana Borsa, André Barreto, David Silver and Doina Precup and has published in prestigious journals such as arXiv (Cornell University).

In The Last Decade

Daniel Toyama

2 papers receiving 16 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Toyama Canada	2	15	6	5	2	2	2	16
Marcell Vazquez-Chanlatte United States	3	18 1.2×	7 1.2×	4 0.8×		2 1.0×	5	21
Heidy Khlaaf United Kingdom	3	21 1.4×	11 1.8×	2 0.4×	2 1.0×	1 0.5×	4	25
Dami Choi Canada	3	13 0.9×	5 0.8×	2 0.4×			5	16
Bernardo Ávila Pires Canada	3	13 0.9×	3 0.5×	2 0.4×	1 0.5×	1 0.5×	7	15
Yahachiro TSUKAMOTO Japan	3	15 1.0×	7 1.2×	6 1.2×	1 0.5×	2 1.0×	10	22
Maximilian Weininger Germany	4	17 1.1×	15 2.5×	3 0.6×		5 2.5×	12	32
Hao-Jun Michael Shi United States	3	7 0.5×	12 2.0×	2 0.4×	1 0.5×	3 1.5×	3	29
Tim Quatmann Germany	3	18 1.2×	17 2.8×	2 0.4×			7	22
Girish Sastry United States	2	12 0.8×	3 0.5×	5 1.0×			4	18
M. Lucio Martínez United States	1	8 0.5×	16 2.7×	3 0.6×			2	28

Countries citing papers authored by Daniel Toyama

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Toyama

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Toyama

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Toyama. A scholar is included among the top collaborators of Daniel Toyama based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Toyama. Daniel Toyama is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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2 of 2 papers shown

1.

Barreto, André, Diana Borsa, Shaobo Hou, et al.. (2019). The Option Keyboard: Combining Skills in Reinforcement Learning. arXiv (Cornell University). 32. 13031–13041. 15 indexed citations

2.

Precup, Doina, et al.. (2018). Knowledge Representation for Reinforcement Learning using General Value Functions. 1 indexed citations

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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