Jennifer Newman

281 total papers · 2.2k total citations
101 papers, 1.5k citations indexed

About

Jennifer Newman is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Jennifer Newman has authored 101 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Computer Vision and Pattern Recognition, 16 papers in Artificial Intelligence and 14 papers in Cellular and Molecular Neuroscience. Recurrent topics in Jennifer Newman's work include Medical Image Segmentation Techniques (13 papers), Digital Media Forensic Detection (11 papers) and Image Retrieval and Classification Techniques (11 papers). Jennifer Newman is often cited by papers focused on Medical Image Segmentation Techniques (13 papers), Digital Media Forensic Detection (11 papers) and Image Retrieval and Classification Techniques (11 papers). Jennifer Newman collaborates with scholars based in United States, United Kingdom and Germany. Jennifer Newman's co-authors include Gerhard X. Ritter, Carlos Jensen, Joseph N. Wilson, A. A. Fouad, Qin Zhou, Noel Cressie, Patrick M. Beardsley, Nancy K. Mello, Clifford Bergman and Marilyn E. Carroll and has published in prestigious journals such as IEEE Transactions on Power Systems, Developmental Cell and Journal of Medicinal Chemistry.

In The Last Decade

Jennifer Newman

95 papers receiving 1.4k citations

Author Peers

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

Author Last Decade Papers Cites
Jennifer Newman 291 264 262 205 173 101 1.5k
Juan A. Botía 258 0.9× 221 0.8× 115 0.4× 392 1.9× 129 0.7× 94 1.5k
David Wong 145 0.5× 122 0.5× 63 0.2× 206 1.0× 108 0.6× 117 1.7k
Taewan Kim 189 0.6× 202 0.8× 187 0.7× 163 0.8× 113 0.7× 112 1.1k
Péter András 187 0.6× 371 1.4× 86 0.3× 65 0.3× 118 0.7× 101 1.5k
Vincenzo De Florio 200 0.7× 225 0.9× 106 0.4× 72 0.4× 90 0.5× 122 1.1k
Mu Mu 422 1.5× 54 0.2× 481 1.8× 262 1.3× 88 0.5× 84 1.5k
Jiang Li 178 0.6× 322 1.2× 38 0.1× 82 0.4× 197 1.1× 97 1.4k
William R. Harris 62 0.2× 398 1.5× 103 0.4× 449 2.2× 100 0.6× 66 1.8k
Dharmendra Sharma 209 0.7× 371 1.4× 43 0.2× 162 0.8× 110 0.6× 188 1.6k
Tara Madhyastha 232 0.8× 158 0.6× 50 0.2× 75 0.4× 33 0.2× 71 1.8k

Countries citing papers authored by Jennifer Newman

Since Specialization
Citations

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

Fields of papers citing papers by Jennifer Newman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jennifer Newman

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