Steven D. Whitehead

32 total papers · 1.7k total citations
17 papers, 898 citations indexed

About

Steven D. Whitehead is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computer Networks and Communications. According to data from OpenAlex, Steven D. Whitehead has authored 17 papers receiving a total of 898 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 5 papers in Cognitive Neuroscience and 3 papers in Computer Networks and Communications. Recurrent topics in Steven D. Whitehead's work include Reinforcement Learning in Robotics (6 papers), Evolutionary Algorithms and Applications (4 papers) and Neural dynamics and brain function (4 papers). Steven D. Whitehead is often cited by papers focused on Reinforcement Learning in Robotics (6 papers), Evolutionary Algorithms and Applications (4 papers) and Neural dynamics and brain function (4 papers). Steven D. Whitehead collaborates with scholars based in United States and United Kingdom. Steven D. Whitehead's co-authors include Dana H. Ballard, D.H. Ballard, Mary Hayhoe, Long-Ji Lin, John N. Daigle, Richard S. Sutton, Himanshu Sinha, Jeff B. Pelz and Marie‐José Montpetit and has published in prestigious journals such as Philosophical Transactions of the Royal Society B Biological Sciences, Artificial Intelligence and Neural Computation.

In The Last Decade

Steven D. Whitehead

17 papers receiving 796 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Steven D. Whitehead	476	275	148	106	70	17	898
John F. Kolen	484 1.0×	283 1.0×	236 1.6×	53 0.5×	46 0.7×	25	1.0k
Olivier Sigaud	407 0.9×	209 0.8×	83 0.6×	268 2.5×	41 0.6×	50	902
Vijaykumar Gullapalli	345 0.7×	184 0.7×	54 0.4×	288 2.7×	42 0.6×	16	709
Ron Chrisley	524 1.1×	289 1.1×	221 1.5×	89 0.8×	22 0.3×	31	1.1k
Xiaoqing Gu	402 0.8×	169 0.6×	271 1.8×	73 0.7×	70 1.0×	68	980
Kathryn Merrick	408 0.9×	56 0.2×	92 0.6×	98 0.9×	95 1.4×	60	806
Vicenç Gómez	284 0.6×	154 0.6×	72 0.5×	75 0.7×	99 1.4×	48	799
Lei Shi	285 0.6×	105 0.4×	232 1.6×	43 0.4×	36 0.5×	63	816
Francisco S. Melo	590 1.2×	88 0.3×	107 0.7×	200 1.9×	111 1.6×	85	1.0k
Kurt Driessens	594 1.2×	58 0.2×	190 1.3×	63 0.6×	46 0.7×	52	843

Countries citing papers authored by Steven D. Whitehead

Since Specialization

Citations

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

Fields of papers citing papers by Steven D. Whitehead

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Steven D. Whitehead. 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 Steven D. Whitehead. The network helps show where Steven D. Whitehead may publish in the future.

Co-authorship network of co-authors of Steven D. Whitehead

This figure shows the co-authorship network connecting the top 25 collaborators of Steven D. Whitehead. A scholar is included among the top collaborators of Steven D. Whitehead 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 Steven D. Whitehead. Steven D. Whitehead is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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