David Burkett

16 total papers · 487 total citations
13 papers, 312 citations indexed

About

David Burkett is a scholar working on Artificial Intelligence, Molecular Biology and Computer Networks and Communications. According to data from OpenAlex, David Burkett has authored 13 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 3 papers in Molecular Biology and 1 paper in Computer Networks and Communications. Recurrent topics in David Burkett's work include Topic Modeling (10 papers), Natural Language Processing Techniques (9 papers) and Biomedical Text Mining and Ontologies (3 papers). David Burkett is often cited by papers focused on Topic Modeling (10 papers), Natural Language Processing Techniques (9 papers) and Biomedical Text Mining and Ontologies (3 papers). David Burkett collaborates with scholars based in United States and China. David Burkett's co-authors include Dan Klein, Taylor Berg-Kirkpatrick, John Blitzer, Mohit Bansal, Gerard de Melo, Slav Petrov, David Hall, Jonathan K. Kummerfeld, Alon Cohen and Necip Fazıl Ayan and has published in prestigious journals such as Empirical Methods in Natural Language Processing, North American Chapter of the Association for Computational Linguistics and Proceedings of the International Conference on Automated Planning and Scheduling.

In The Last Decade

David Burkett

12 papers receiving 293 citations

Author Peers

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

Author Last Decade Papers Cites
David Burkett 299 31 25 18 8 13 312
Tim Vieira 227 0.8× 39 1.3× 33 1.3× 8 0.4× 6 0.8× 20 260
Daoud Clarke 240 0.8× 46 1.5× 27 1.1× 23 1.3× 5 0.6× 11 281
Niklas Muennighoff 290 1.0× 36 1.2× 51 2.0× 9 0.5× 9 1.1× 8 357
Swarnadeep Saha 193 0.6× 40 1.3× 33 1.3× 10 0.6× 8 1.0× 14 217
Le Sun 285 1.0× 61 2.0× 28 1.1× 17 0.9× 11 1.4× 25 352
Florian Laws 246 0.8× 16 0.5× 11 0.4× 8 0.4× 9 1.1× 9 276
Eric Nalisnick 172 0.6× 47 1.5× 41 1.6× 9 0.5× 9 1.1× 19 213
Yuxian Gu 255 0.9× 44 1.4× 71 2.8× 14 0.8× 8 1.0× 9 303
Amir Kantor 177 0.6× 47 1.5× 38 1.5× 12 0.7× 21 2.6× 14 232
Roberto Zanoli 251 0.8× 40 1.3× 19 0.8× 39 2.2× 6 0.8× 20 287

Countries citing papers authored by David Burkett

Since Specialization
Citations

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

Fields of papers citing papers by David Burkett

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Burkett

This figure shows the co-authorship network connecting the top 25 collaborators of David Burkett. A scholar is included among the top collaborators of David Burkett 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 David Burkett. David Burkett 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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