Vishu Gupta

799 total citations
35 papers, 468 citations indexed

About

Vishu Gupta is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Metals and Alloys. According to data from OpenAlex, Vishu Gupta has authored 35 papers receiving a total of 468 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Materials Chemistry, 5 papers in Computational Theory and Mathematics and 4 papers in Metals and Alloys. Recurrent topics in Vishu Gupta's work include Machine Learning in Materials Science (17 papers), X-ray Diffraction in Crystallography (9 papers) and Computational Drug Discovery Methods (5 papers). Vishu Gupta is often cited by papers focused on Machine Learning in Materials Science (17 papers), X-ray Diffraction in Crystallography (9 papers) and Computational Drug Discovery Methods (5 papers). Vishu Gupta collaborates with scholars based in United States, Japan and India. Vishu Gupta's co-authors include Ankit Agrawal, Wei‐keng Liao, Alok Choudhary, Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Priti Pal, Satarudra Prakash Singh, Akhilesh Kumar Singh and Thakur Prasad Yadav and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Vishu Gupta

30 papers receiving 447 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vishu Gupta United States 10 323 76 70 66 46 35 468
Wenzhe Zhao China 13 258 0.8× 79 1.0× 130 1.9× 122 1.8× 76 1.7× 37 861
Xuening Sun China 10 114 0.4× 19 0.3× 56 0.8× 115 1.7× 26 0.6× 27 283
Kaushik Mallik Germany 7 150 0.5× 86 1.1× 64 0.9× 88 1.3× 24 0.5× 17 370
Fang Lü China 12 147 0.5× 46 0.6× 101 1.4× 206 3.1× 40 0.9× 42 486
T. Saravanakumar India 15 78 0.2× 31 0.4× 32 0.5× 119 1.8× 61 1.3× 26 558
Lingyu Zhu China 13 140 0.4× 31 0.4× 126 1.8× 51 0.8× 138 3.0× 47 548
Cheng Yan United States 12 193 0.6× 28 0.4× 72 1.0× 32 0.5× 86 1.9× 31 407
Deqing Wang China 15 213 0.7× 7 0.1× 47 0.7× 203 3.1× 329 7.2× 73 815
Françoise Couenne France 18 76 0.2× 36 0.5× 111 1.6× 39 0.6× 173 3.8× 72 932
Byung Jun Park South Korea 12 101 0.3× 30 0.4× 86 1.2× 204 3.1× 46 1.0× 35 533

Countries citing papers authored by Vishu Gupta

Since Specialization
Citations

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

Fields of papers citing papers by Vishu Gupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vishu Gupta

This figure shows the co-authorship network connecting the top 25 collaborators of Vishu Gupta. A scholar is included among the top collaborators of Vishu Gupta 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 Vishu Gupta. Vishu Gupta 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.
Kang, Mengjia, Nikolay S. Markov, Anna Pawlowski, et al.. (2025). Developing and validating machine learning models to predict next-day extubation. Scientific Reports. 15(1). 27552–27552.
2.
Hasan, Mahmudul, Youjia Li, S. K. Chakrabarty, et al.. (2025). An AI framework for time series microstructure prediction from processing parameters. Scientific Reports. 15(1). 24074–24074.
3.
Gupta, Vishu, Kamal Choudhary, Brian DeCost, et al.. (2024). Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets. npj Computational Materials. 10(1). 44 indexed citations
4.
Wang, Kewei, Mahmudul Hasan, Vishu Gupta, et al.. (2024). Deep Learning Based Inverse Modeling for Materials Design: From Microstructure and Property to Processing. 236–241. 1 indexed citations
5.
Gupta, Vishu, Wei‐keng Liao, Alok Choudhary, & Ankit Agrawal. (2024). Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data. 981–984.
6.
Gupta, Vishu, et al.. (2024). Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks. Journal of Cheminformatics. 16(1). 17–17. 5 indexed citations
7.
Li, Youjia, Vishu Gupta, Kamal Choudhary, et al.. (2024). Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction. Digital Discovery. 4(2). 376–383. 6 indexed citations
8.
Hasan, Mahmudul, Arindam Paul, Vishu Gupta, et al.. (2023). An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems. npj Computational Materials. 9(1). 12 indexed citations
9.
Gupta, Vishu, et al.. (2023). The Efficacy and Safety of an Ayurvedic Petsaffa Formulation in Subjects with Constipation: An Open-Label, Non-Randomized Clinical Study. International Journal of Ayurveda and Pharma Research. 38–42. 1 indexed citations
10.
Gupta, Vishu, Wei‐keng Liao, Alok Choudhary, & Ankit Agrawal. (2023). Evolution of artificial intelligence for application in contemporary materials science. MRS Communications. 13(5). 754–763. 12 indexed citations
11.
Wahl, Carolin B., Alexandra Day, Vishu Gupta, et al.. (2023). Machine Learning Enabled Image Classification for Automated Data Acquisition in the Electron Microscope. Microscopy and Microanalysis. 29(Supplement_1). 1909–1910. 3 indexed citations
12.
Gupta, Vishu, Wei‐keng Liao, Alok Choudhary, & Ankit Agrawal. (2023). Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data. 1–8. 4 indexed citations
13.
Gupta, Vishu, et al.. (2023). A Single Centre Open Label Post Marketing Surveillance Study to Evaluate the Efficacy and Safety of Roop Mantra Cucumber Ayurvedic Medicinal Face Wash. International Journal of Ayurveda and Pharma Research. 13–20. 1 indexed citations
14.
Mahadeva, Rajesh, Mahendra Kumar, Vishu Gupta, et al.. (2023). Water desalination using PSO-ANN techniques: A critical review. SHILAP Revista de lepidopterología. 9. 100128–100128. 7 indexed citations
15.
16.
Gupta, Vishu, Kamal Choudhary, Francesca Tavazza, et al.. (2021). Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nature Communications. 12(1). 6595–6595. 109 indexed citations
17.
Jha, Dipendra, Vishu Gupta, Logan Ward, et al.. (2021). Enabling deeper learning on big data for materials informatics applications. Scientific Reports. 11(1). 4244–4244. 48 indexed citations
18.
Singh, Akhilesh Kumar, Priti Pal, Vinay Gupta, et al.. (2017). Green synthesis, characterization and antimicrobial activity of zinc oxide quantum dots using Eclipta alba. Materials Chemistry and Physics. 203. 40–48. 108 indexed citations
19.
Dalui, Sujit Kumar, et al.. (2006). UNPLEASANT PEDESTRIAN WIND CONDITIONS AROUND BUILDINGS. Asian Journal of Civil Engineering. 7(2). 147–154. 1 indexed citations
20.
Courtney, Tod, Michel Cukier, Vishu Gupta, et al.. (2002). A Configurable CORBA Gateway for Providing Adaptable System Properties. 31(12). 2326–33. 1 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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