Dan S. Bloomberg

54 total papers · 1.0k total citations
40 papers, 699 citations indexed

About

Dan S. Bloomberg is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. According to data from OpenAlex, Dan S. Bloomberg has authored 40 papers receiving a total of 699 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 16 papers in Atomic and Molecular Physics, and Optics and 10 papers in Electrical and Electronic Engineering. Recurrent topics in Dan S. Bloomberg's work include Magnetic properties of thin films (11 papers), Image Retrieval and Classification Techniques (11 papers) and Handwritten Text Recognition Techniques (10 papers). Dan S. Bloomberg is often cited by papers focused on Magnetic properties of thin films (11 papers), Image Retrieval and Classification Techniques (11 papers) and Handwritten Text Recognition Techniques (10 papers). Dan S. Bloomberg collaborates with scholars based in United States, France and Israel. Dan S. Bloomberg's co-authors include Luc Vincent, John Goutsias, G. Kopec, Lynn Wilcox, Gordon F. Hughes, Karl H. Norris, Meng H. Lean, D. Treves, Petros Maragos and A. S. Arrott and has published in prestigious journals such as Journal of Applied Physics, IEEE Transactions on Magnetics and Computer Vision and Image Understanding.

In The Last Decade

Dan S. Bloomberg

39 papers receiving 630 citations

Author Peers

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

Author						Papers	Cites
Dan S. Bloomberg	402	131	112	93	90	40	699
S. Huang	174 0.4×	158 1.2×	98 0.9×	126 1.4×	90 1.0×	33	633
Jinpeng Liu	92 0.2×	119 0.9×	200 1.8×	88 0.9×	60 0.7×	62	663
Jingbo Zhang	276 0.7×	93 0.7×	106 0.9×	70 0.8×	22 0.2×	19	656
Xiao Yu	109 0.3×	105 0.8×	136 1.2×	254 2.7×	75 0.8×	44	711
P.C.K. Kwok	296 0.7×	95 0.7×	256 2.3×	221 2.4×	63 0.7×	64	879
Thomas J. Meitzler	123 0.3×	98 0.7×	249 2.2×	193 2.1×	79 0.9×	75	768
Yang‐Ming Zhu	171 0.4×	37 0.3×	124 1.1×	83 0.9×	197 2.2×	47	663
Minseok Choi	175 0.4×	198 1.5×	68 0.6×	211 2.3×	104 1.2×	76	772
Wen‐Yuan Chen	390 1.0×	49 0.4×	35 0.3×	112 1.2×	22 0.2×	71	712
Xiaohe Cheng	244 0.6×	63 0.5×	156 1.4×	473 5.1×	33 0.4×	63	819

Countries citing papers authored by Dan S. Bloomberg

Since Specialization

Citations

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

Fields of papers citing papers by Dan S. Bloomberg

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan S. Bloomberg

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