Carlos D. Correa

26 papers receiving 432 citations

Peers

Carlos D. Correa
Comparison fields: 5 of 78
  • Computer Vision and Pattern Recognition 346
  • Computer Graphics and Computer-Aided Design 143
  • Artificial Intelligence 108
  • Signal Processing 69
  • Computational Mechanics 57
Replace H. Jänicke with:
H. Jänicke Germany
Raphael Fuchs Switzerland
Alexander Wiebel Germany
P. Amburn United States
Ken Joy United States
Kwan‐Liu Ma United States
Koji Koyamada Japan
C. Correa United States
Stephen Brooks Canada
Randi J. Rost United States
Carlos D. Correa relative to H. Jänicke Germany H. Jänicke's profile →
Citations per field
00.5×4.3×
H. Jänicke · 1×
Citations per year

Countries citing papers authored by Carlos D. Correa

Since Specialization
Citations

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

Fields of papers citing papers by Carlos D. Correa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Carlos D. Correa

This figure shows the co-authorship network connecting the top 25 collaborators of Carlos D. Correa. A scholar is included among the top collaborators of Carlos D. Correa 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 Carlos D. Correa. Carlos D. Correa 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 9
2 29
3 28
4 19
5 7
6 9
7 59
8 45
9 3
10 81
11 4
12 8
13 41
14 4
15 1
16 1
17 25
18 10
19 3
20
A Flexible Architecture to Support Awareness in Heterogeneous Collaborative Environments
6

About Carlos D. Correa

Carlos D. Correa is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Human-Computer Interaction, having authored 26 papers that have together received 453 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (9 papers), Data Visualization and Analytics (8 papers) and 3D Shape Modeling and Analysis (7 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (143 citations), Computer Vision and Pattern Recognition (346 citations) and Signal Processing (69 citations). Carlos D. Correa has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Kwan‐Liu Ma, Peter Lindström, Deborah Silver, Min Chen, Nelson Max, Ivan Marsic, Tarik Crnovrsanin, Hyo-Jin Kim, Min Chen and Yubo Zhang. Their work appears in journals such as ACM Transactions on Graphics, IEEE Transactions on Visualization and Computer Graphics and Computer Supported Cooperative Work (CSCW).

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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