Mu Mu

77 papers receiving 1.5k citations

Peers

Mu Mu
Comparison fields: 5 of 129
  • Cellular and Molecular Neuroscience 479
  • Computer Vision and Pattern Recognition 422
  • Computer Networks and Communications 340
  • Radiology, Nuclear Medicine and Imaging 264
  • Molecular Biology 261
Replace Vangelis Sakkalis with:
Vangelis Sakkalis Greece
Shan Yu China
Yuan-Kai Wang Taiwan
Tao Lian China
William R. Harris United States
Kazem Taghva United States
Carlo Blundo Italy
Susan Landau United States
Mu Mu relative to Vangelis Sakkalis Greece Vangelis Sakkalis's profile →
Citations per field
00.5×4.0×
Vangelis Sakkalis · 1×
Citations per year

Countries citing papers authored by Mu Mu

Since Specialization
Citations

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

Fields of papers citing papers by Mu Mu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mu Mu

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 7
3 0
4 49
5 1
6 41
7
STEER: Exploring the dynamic relationship between social information and networked media through experimentation
0
8
Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders
7
9
STEER: A Social Telemedia Environment for Experimental Research
0
10
Application-level fairness
4
11 139
12 21
13 72
14 53
15 2
16 130
17 26
18 65
19 5
20 25

About Mu Mu

Mu Mu is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Computer Networks and Communications, having authored 84 papers that have together received 1.5k indexed citations. Recurring topics across this work include Image and Video Quality Assessment (28 papers), Multimedia Communication and Technology (19 papers) and Peer-to-Peer Network Technologies (11 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (479 citations), Computer Vision and Pattern Recognition (422 citations) and Signal Processing (166 citations). Mu Mu has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Hank F. Kung, Mei‐Ping Kung, Nicholas Race, Matthew Broadbent, Catherine Hou, Paul D. Acton, Shunichi Oya, Yehia Elkhatib, Panagiotis Georgopoulos and Michael J. Siciliano. Their work appears in journals such as Journal of Medicinal Chemistry, Journal of Pharmacology and Experimental Therapeutics and Journal of the Atmospheric Sciences.

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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