Pradeep Kumar Jayaraman

556 total citations
20 papers, 289 citations indexed

About

Pradeep Kumar Jayaraman is a scholar working on Computational Mechanics, Computer Graphics and Computer-Aided Design and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pradeep Kumar Jayaraman has authored 20 papers receiving a total of 289 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computational Mechanics, 11 papers in Computer Graphics and Computer-Aided Design and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pradeep Kumar Jayaraman's work include Computer Graphics and Visualization Techniques (11 papers), 3D Shape Modeling and Analysis (11 papers) and Generative Adversarial Networks and Image Synthesis (4 papers). Pradeep Kumar Jayaraman is often cited by papers focused on Computer Graphics and Visualization Techniques (11 papers), 3D Shape Modeling and Analysis (11 papers) and Generative Adversarial Networks and Image Synthesis (4 papers). Pradeep Kumar Jayaraman collaborates with scholars based in Singapore, United States and Canada. Pradeep Kumar Jayaraman's co-authors include Chi‐Wing Fu, Karl D. D. Willis, Joseph G. Lambourne, Jianmin Zheng, Peng Song, Daniel Cohen‐Or, Hang Chu, Takashi Maekawa, Yewen Pu and Tong‐Yee Lee and has published in prestigious journals such as ACM Transactions on Graphics, IEEE Transactions on Visualization and Computer Graphics and Structural and Multidisciplinary Optimization.

In The Last Decade

Pradeep Kumar Jayaraman

18 papers receiving 277 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pradeep Kumar Jayaraman Singapore 10 142 133 89 78 44 20 289
Yu‐Wei Zhang China 12 134 0.9× 189 1.4× 150 1.7× 66 0.8× 45 1.0× 31 390
Joseph G. Lambourne United States 7 209 1.5× 177 1.3× 103 1.2× 100 1.3× 61 1.4× 11 349
Bianca Falcidieno Italy 10 184 1.3× 60 0.5× 86 1.0× 235 3.0× 28 0.6× 34 420
S.N. Gottschlich United States 7 17 0.1× 160 1.2× 30 0.3× 133 1.7× 64 1.5× 22 343
I. C. Braid United Kingdom 7 188 1.3× 70 0.5× 125 1.4× 241 3.1× 17 0.4× 11 375
Guoxin Zhang China 6 26 0.2× 232 1.7× 15 0.2× 44 0.6× 24 0.5× 9 331
Jan Wolter United States 10 43 0.3× 116 0.9× 31 0.3× 188 2.4× 10 0.2× 23 376
Mengyuan Yan United States 5 82 0.6× 170 1.3× 18 0.2× 13 0.2× 22 0.5× 10 281
Guoxiong Hu China 8 10 0.1× 198 1.5× 11 0.1× 32 0.4× 20 0.5× 16 289

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-authorship network of co-authors of Pradeep Kumar Jayaraman

This figure shows the co-authorship network connecting the top 25 collaborators of Pradeep Kumar Jayaraman. A scholar is included among the top collaborators of Pradeep Kumar Jayaraman 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 Pradeep Kumar Jayaraman. Pradeep Kumar Jayaraman 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
1.
Cheong, Hyunmin, et al.. (2025). Deep Generative Model for Mechanical System Configuration Design. Proceedings of the AAAI Conference on Artificial Intelligence. 39(16). 16496–16504. 1 indexed citations
2.
Cheong, Hyunmin, et al.. (2024). Optimal design of frame structures with mixed categorical and continuous design variables using the Gumbel–Softmax method. Structural and Multidisciplinary Optimization. 67(3). 4 indexed citations
3.
Lambourne, Joseph G., et al.. (2024). BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry. ACM Transactions on Graphics. 43(4). 1–14. 17 indexed citations
4.
Bian, Shijie, Pradeep Kumar Jayaraman, Karl D. D. Willis, et al.. (2023). HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided Design. Journal of Computing and Information Science in Engineering. 24(1). 7 indexed citations
5.
Morris, Nigel, Pradeep Kumar Jayaraman, & Adrian Butscher. (2022). BeNTO: Beam Network Topology Optimization. Computer-Aided Design. 156. 103439–103439.
6.
Willis, Karl D. D., Pradeep Kumar Jayaraman, Hang Chu, et al.. (2022). JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15828–15839. 36 indexed citations
7.
Jayaraman, Pradeep Kumar, et al.. (2022). Neon: A Multi-GPU Programming Model for Grid-based Computations. 817–827.
8.
Jayaraman, Pradeep Kumar, et al.. (2021). Truncated octree and its applications. The Visual Computer. 38(4). 1167–1179. 1 indexed citations
9.
Willis, Karl D. D., Pradeep Kumar Jayaraman, Joseph G. Lambourne, Hang Chu, & Yewen Pu. (2021). Engineering Sketch Generation for Computer-Aided Design. 2105–2114. 35 indexed citations
10.
Meltzer, Peter C., et al.. (2021). UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 7 12 june. 9670–9679. 2 indexed citations
11.
Jayaraman, Pradeep Kumar, et al.. (2020). UV-Net: Learning from Curve-Networks and Solids.. 5 indexed citations
12.
Jayaraman, Pradeep Kumar, et al.. (2020). Parallel Point Cloud Compression Using Truncated Octree. DR-NTU (Nanyang Technological University). 19. 1–8. 4 indexed citations
13.
Kikuchi, Ryosuke, et al.. (2018). Embedding QR codes onto B-spline surfaces for 3D printing. Computer-Aided Design. 102. 215–223. 32 indexed citations
14.
Jayaraman, Pradeep Kumar, et al.. (2018). An image processing approach to feature-preserving B-spline surface fairing. Computer-Aided Design. 99. 1–10. 14 indexed citations
15.
Jayaraman, Pradeep Kumar, Chi‐Wing Fu, Jianmin Zheng, Xueting Liu, & Tien‐Tsin Wong. (2017). Globally Consistent Wrinkle-Aware Shading of Line Drawings. IEEE Transactions on Visualization and Computer Graphics. 24(7). 2103–2117. 11 indexed citations
16.
Huang, Shiyang, et al.. (2016). Interactive High-Relief Reconstruction for Organic and Double-Sided Objects from a Photo. IEEE Transactions on Visualization and Computer Graphics. 23(7). 1796–1808. 23 indexed citations
17.
Fu, Chi‐Wing, et al.. (2015). Computational interlocking furniture assembly. ACM Transactions on Graphics. 34(4). 1–11. 69 indexed citations
18.
Jayaraman, Pradeep Kumar & Chi‐Wing Fu. (2014). Interactive Line Drawing Recognition and Vectorization with Commodity Camera. 447–456. 3 indexed citations
19.
Jayaraman, Pradeep Kumar, et al.. (2014). 2.5D Cartoon Hair Modeling and Manipulation. IEEE Transactions on Visualization and Computer Graphics. 21(3). 304–314. 12 indexed citations
20.
Newman, Richard E., et al.. (2005). Security analysis of and proposal for image-based authentication. 1556. 141–144. 13 indexed citations

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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