Pradeep Kumar Jayaraman

18 papers receiving 290 citations

Peers

Pradeep Kumar Jayaraman
Comparison fields: 5 of 46
  • Computer Graphics and Computer-Aided Design 89
  • Industrial and Manufacturing Engineering 82
  • Geology 44
  • Computational Mechanics 144
  • Computer Vision and Pattern Recognition 134
Replace Joseph G. Lambourne with:
Joseph G. Lambourne United States
Bianca Falcidieno Italy
Yu‐Wei Zhang China
I. C. Braid United Kingdom
Jan Wolter United States
Mengyuan Yan United States
Guoxin Zhang China
David Bourne United States
S.N. Gottschlich United States
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Citations per field
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Citations per year

Countries citing papers authored by Pradeep Kumar Jayaraman

Since Specialization
Citations

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

Fields of papers citing papers by Pradeep Kumar Jayaraman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Pradeep Kumar Jayaraman, 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 Pradeep Kumar Jayaraman Line = papers co-authored together Pradeep Kumar Jayaraman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201570
2 202138
3 202238
4 201833
5 201623
6 202420
7 201814
8 200513
9 201412
10 201711
11 20238
12 20245
13
UV-Net: Learning from Curve-Networks and Solids.
20205
14 20204
15 20143
16 20212
17 20251
18 20211
19 20220
20 20250

About Pradeep Kumar Jayaraman

Pradeep Kumar Jayaraman is a scholar working on Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering and Automotive Engineering, having authored 21 papers that have together received 301 indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (11 papers), Computer Graphics and Visualization Techniques (11 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Advanced Vision and Imaging (3 papers), Manufacturing Process and Optimization (3 papers), Advanced Numerical Analysis Techniques (2 papers), Remote Sensing and LiDAR Applications (2 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (89 citations), Industrial and Manufacturing Engineering (82 citations), Geology (44 citations), Computational Mechanics (144 citations) and Computer Vision and Pattern Recognition (134 citations). Pradeep Kumar Jayaraman has collaborated with scholars based in United States, Singapore and Canada. Frequent co-authors include Chi‐Wing Fu, Joseph G. Lambourne, Karl D. D. Willis, Jianmin Zheng, Peng Song, Daniel Cohen‐Or, Hang Chu, Takashi Maekawa, Yewen Pu and Tong‐Yee Lee. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computer-Aided Design, ACM Transactions on Graphics, Journal of Computing and Information Science in Engineering and The Visual Computer.

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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