S. Dey

589 total citations
21 papers, 443 citations indexed

About

S. Dey is a scholar working on Biomedical Engineering, Civil and Structural Engineering and Materials Chemistry. According to data from OpenAlex, S. Dey has authored 21 papers receiving a total of 443 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Biomedical Engineering, 8 papers in Civil and Structural Engineering and 8 papers in Materials Chemistry. Recurrent topics in S. Dey's work include Graphene research and applications (7 papers), Composite Structure Analysis and Optimization (6 papers) and Acoustic Wave Phenomena Research (6 papers). S. Dey is often cited by papers focused on Graphene research and applications (7 papers), Composite Structure Analysis and Optimization (6 papers) and Acoustic Wave Phenomena Research (6 papers). S. Dey collaborates with scholars based in India, United Kingdom and Germany. S. Dey's co-authors include T. Mukhopadhyay, Susmita Naskar, Sondipon Adhikari, Kritesh Kumar Gupta, P. K. Karsh, Hamed Haddad Khodaparast, Lintu Roy, Biswajit Basu, Subrata Chakraborty and Subrata Barman and has published in prestigious journals such as Scientific Reports, Applied Surface Science and Composite Structures.

In The Last Decade

S. Dey

19 papers receiving 433 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Dey India 14 201 201 135 123 100 21 443
Dimitrios Savvas Greece 10 129 0.6× 300 1.5× 148 1.1× 114 0.9× 81 0.8× 13 474
Juan Ma China 12 183 0.9× 272 1.4× 185 1.4× 36 0.3× 118 1.2× 36 440
Abdul Manan United Kingdom 9 136 0.7× 128 0.6× 102 0.8× 65 0.5× 50 0.5× 16 326
Theodore F. Johnson United States 11 186 0.9× 203 1.0× 167 1.2× 60 0.5× 126 1.3× 31 497
T.-P. Chang Taiwan 14 281 1.4× 219 1.1× 61 0.5× 45 0.4× 120 1.2× 30 467
Zhen Lu China 10 94 0.5× 66 0.3× 51 0.4× 79 0.6× 515 5.2× 37 726
W. H. Zhang China 13 368 1.8× 326 1.6× 36 0.3× 83 0.7× 135 1.4× 32 634
Francesco Trentadue Italy 13 435 2.2× 76 0.4× 84 0.6× 16 0.1× 122 1.2× 51 536
S.Y. Han China 12 135 0.7× 250 1.2× 12 0.1× 80 0.7× 122 1.2× 31 385

Countries citing papers authored by S. Dey

Since Specialization
Citations

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

Fields of papers citing papers by S. Dey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Dey

This figure shows the co-authorship network connecting the top 25 collaborators of S. Dey. A scholar is included among the top collaborators of S. Dey 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 S. Dey. S. Dey 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.
Dey, S., et al.. (2025). VexIR2Vec : An Architecture-Neutral Embedding Framework for Binary Similarity. ACM Transactions on Software Engineering and Methodology. 34(8). 1–54. 1 indexed citations
2.
Singh, Abhishek Kumar, P. K. Karsh, & S. Dey. (2025). Uncertainty frequency analysis of composite shells with cutout. Journal of Mechanical Science and Technology.
3.
4.
Gupta, Kritesh Kumar, Surajit Das Barman, S. Dey, Susmita Naskar, & T. Mukhopadhyay. (2024). On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics. Scientific Reports. 14(1). 16795–16795. 15 indexed citations
5.
Gupta, Kritesh Kumar, Subrata Barman, S. Dey, & T. Mukhopadhyay. (2024). Explainable machine learning assisted molecular-level insights for enhanced specific stiffness exploiting the large compositional space of AlCoCrFeNi high entropy alloys. Machine Learning Science and Technology. 5(2). 25082–25082. 14 indexed citations
6.
Gupta, Kritesh Kumar, et al.. (2022). Probing the stochastic fracture behavior of twisted bilayer graphene: Efficient ANN based molecular dynamics simulations for complete probabilistic characterization. Materials Today Communications. 32. 103932–103932. 19 indexed citations
7.
Dey, S., et al.. (2022). Packet Processing Algorithm Identification using Program Embeddings. 76–82. 4 indexed citations
8.
Mukhopadhyay, T., et al.. (2022). Probability-based unified sensitivity analysis for multi-objective performances of composite laminates: A surrogate-assisted approach. Composite Structures. 294. 115559–115559. 15 indexed citations
9.
10.
Gupta, Kritesh Kumar, et al.. (2021). Sparse machine learning assisted deep computational insights on the mechanical properties of graphene with intrinsic defects and doping. Journal of Physics and Chemistry of Solids. 155. 110111–110111. 37 indexed citations
11.
Gupta, Kritesh Kumar, et al.. (2021). Compound influence of topological defects and heteroatomic inclusions on the mechanical properties of SWCNTs. Materials Today Communications. 26. 102021–102021. 20 indexed citations
12.
Gupta, Kritesh Kumar, T. Mukhopadhyay, Lintu Roy, & S. Dey. (2021). Hybrid machine-learning-assisted quantification of the compound internal and external uncertainties of graphene: towards inclusive analysis and design. Materials Advances. 3(2). 1160–1181. 13 indexed citations
13.
Mukhopadhyay, T., et al.. (2020). Probing the multi-physical probabilistic dynamics of a novel functional class of hybrid composite shells. Composite Structures. 262. 113294–113294. 17 indexed citations
14.
Mukhopadhyay, T., et al.. (2020). Machine learning based stochastic dynamic analysis of functionally graded shells. Composite Structures. 237. 111870–111870. 54 indexed citations
15.
Gupta, Kritesh Kumar, et al.. (2020). Effect of spatial distribution of nanopores on mechanical properties of mono layer graphene. IOP Conference Series Materials Science and Engineering. 872(1). 12187–12187. 2 indexed citations
16.
Mukhopadhyay, T., et al.. (2020). Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms. Archives of Computational Methods in Engineering. 28(3). 1731–1760. 36 indexed citations
17.
Gupta, Kritesh Kumar, et al.. (2020). Effect of silicon dopant on mechanical properties of monolayer graphene. IOP Conference Series Materials Science and Engineering. 872(1). 12188–12188. 3 indexed citations
18.
Karsh, P. K., et al.. (2019). Stochastic impact responses analysis of functionally graded plates. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 41(11). 15 indexed citations
19.
Mukhopadhyay, T., Susmita Naskar, S. Dey, & Sondipon Adhikari. (2016). On quantifying the effect of noise in surrogate based stochastic free vibration analysis of laminated composite shallow shells. Composite Structures. 140. 798–805. 63 indexed citations
20.
Dey, S., T. Mukhopadhyay, Hamed Haddad Khodaparast, & Sondipon Adhikari. (2015). Fuzzy uncertainty propagation in composites using Gram–Schmidt polynomial chaos expansion. Applied Mathematical Modelling. 40(7-8). 4412–4428. 73 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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