Mu Mu

2.0k citations
84 papers · 1.5k indexed · h-index 22

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
  • Signal Processing 166
  • Computer Networks and Communications 340
  • Human-Computer Interaction 70
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

The 25 scholars most cited alongside Mu Mu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mu Mu Line = papers co-authored together Mu Mu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20240
2 20237
3 20230
4 202249
5 20191
6 201841
7
STEER: Exploring the dynamic relationship between social information and networked media through experimentation
20150
8
Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders
20157
9
STEER: A Social Telemedia Environment for Experimental Research
20120
10
Application-level fairness
20084
11 2000139
12 200021
13 200072
14 199953
15 19992
16 1997130
17 199626
18 199565
19 19955
20 199425

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), Peer-to-Peer Network Technologies (11 papers), Video Coding and Compression Technologies (11 papers), Caching and Content Delivery (11 papers), Neuroscience and Neuropharmacology Research (10 papers), Network Traffic and Congestion Control (10 papers) and Neurotransmitter Receptor Influence on Behavior (9 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.

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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