Dan Gutfreund

65 total papers · 1.1k total citations
24 papers, 300 citations indexed

About

Dan Gutfreund is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dan Gutfreund has authored 24 papers receiving a total of 300 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 8 papers in Computational Theory and Mathematics and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dan Gutfreund's work include Complexity and Algorithms in Graphs (8 papers), Cryptography and Data Security (6 papers) and Reinforcement Learning in Robotics (4 papers). Dan Gutfreund is often cited by papers focused on Complexity and Algorithms in Graphs (8 papers), Cryptography and Data Security (6 papers) and Reinforcement Learning in Robotics (4 papers). Dan Gutfreund collaborates with scholars based in United States, Israel and United Kingdom. Dan Gutfreund's co-authors include Amnon Ta‐Shma, Ronen Shaltiel, Hana Chockler, Noam Slonim, Ehud Aharoni, Tamar Lavee, Ruty Rinott, Daniel Hershcovich, Ran Levy and Akash Srivastava and has published in prestigious journals such as Machine Learning, Computer-Aided Design and Information Processing Letters.

In The Last Decade

Dan Gutfreund

23 papers receiving 272 citations

Author Peers

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

Author						Papers	Cites
Dan Gutfreund	228	118	46	32	29	24	300
Xusheng Luo	261 1.1×	51 0.4×	55 1.2×	91 2.8×	34 1.2×	26	344
Yongliang Chen	145 0.6×	116 1.0×	20 0.4×	30 0.9×	14 0.5×	21	254
Tian Gao	261 1.1×	50 0.4×	51 1.1×	61 1.9×	13 0.4×	37	347
Cheng Ji	245 1.1×	26 0.2×	49 1.1×	51 1.6×	30 1.0×	16	360
Mohammad Reza Rajati	202 0.9×	76 0.6×	13 0.3×	21 0.7×	15 0.5×	25	312
Nareli Cruz-Cortés	162 0.7×	49 0.4×	97 2.1×	29 0.9×	25 0.9×	22	225
Yifei Li	226 1.0×	38 0.3×	45 1.0×	42 1.3×	35 1.2×	23	344
Manuel S. Lazo-Cortés	144 0.6×	76 0.6×	61 1.3×	34 1.1×	7 0.2×	28	219
Mariusz Flasiński	147 0.6×	38 0.3×	25 0.5×	72 2.3×	11 0.4×	24	280
Rodrigo Toro Icarte	290 1.3×	109 0.9×	27 0.6×	37 1.2×	29 1.0×	12	357

Countries citing papers authored by Dan Gutfreund

Since Specialization

Citations

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

Fields of papers citing papers by Dan Gutfreund

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Gutfreund

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