Gaurav Nanda

1.1k total citations
58 papers, 731 citations indexed

About

Gaurav Nanda is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Social Psychology. According to data from OpenAlex, Gaurav Nanda has authored 58 papers receiving a total of 731 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Electrical and Electronic Engineering and 5 papers in Social Psychology. Recurrent topics in Gaurav Nanda's work include Electric Motor Design and Analysis (5 papers), Advanced Text Analysis Techniques (5 papers) and Online Learning and Analytics (4 papers). Gaurav Nanda is often cited by papers focused on Electric Motor Design and Analysis (5 papers), Advanced Text Analysis Techniques (5 papers) and Online Learning and Analytics (4 papers). Gaurav Nanda collaborates with scholars based in United States, India and Netherlands. Gaurav Nanda's co-authors include Kenji Watanabe, Narayan C. Kar, Takashi Taniguchi, Lieven M. K. Vandersypen, Srijit Goswami, S. Goswami, M. Diez, T. M. Klapwijk, V. E. Calado and Anton Akhmerov and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nano Letters and Nature Nanotechnology.

In The Last Decade

Gaurav Nanda

49 papers receiving 711 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gaurav Nanda United States 13 243 217 203 89 57 58 731
P. V. Varde India 9 75 0.3× 257 1.2× 448 2.2× 296 3.3× 21 0.4× 34 905
Sumitra Singh India 14 110 0.5× 118 0.5× 318 1.6× 90 1.0× 80 1.4× 49 654
Kang Zhang China 16 227 0.9× 173 0.8× 278 1.4× 17 0.2× 65 1.1× 62 737
Serge Weber France 17 74 0.3× 541 2.5× 474 2.3× 55 0.6× 33 0.6× 117 1.1k
V.V. Rao India 19 102 0.4× 412 1.9× 401 2.0× 363 4.1× 27 0.5× 96 1.5k
Yong Tian China 12 70 0.3× 153 0.7× 158 0.8× 42 0.5× 31 0.5× 33 462
Chang‐Ju Lee South Korea 13 30 0.1× 184 0.8× 206 1.0× 70 0.8× 17 0.3× 66 647
Ryo Taguchi Japan 11 64 0.3× 104 0.5× 90 0.4× 47 0.5× 75 1.3× 67 420
Zhiwei Cui China 16 198 0.8× 346 1.6× 547 2.7× 29 0.3× 33 0.6× 51 1.2k
Jaehyun Lee South Korea 16 85 0.3× 199 0.9× 680 3.3× 47 0.5× 26 0.5× 104 906

Countries citing papers authored by Gaurav Nanda

Since Specialization
Citations

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

Fields of papers citing papers by Gaurav Nanda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gaurav Nanda

This figure shows the co-authorship network connecting the top 25 collaborators of Gaurav Nanda. A scholar is included among the top collaborators of Gaurav Nanda 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 Gaurav Nanda. Gaurav Nanda 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.
Nanda, Gaurav, et al.. (2025). Barriers and Facilitators to Integrating AI and XR Technologies for Aircraft Inspection. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 69(1). 774–780.
2.
Parida, Ratri, et al.. (2025). A machine learning based semi-automated framework for house of quality analysis. Computers & Industrial Engineering. 208. 111371–111371.
3.
Nanda, Gaurav, et al.. (2024). Evaluating the Performance of Topic Modeling Techniques with Human Validation to Support Qualitative Analysis. Big Data and Cognitive Computing. 8(10). 132–132. 5 indexed citations
4.
5.
Nanda, Gaurav, et al.. (2024). Probabilistic Ensemble Framework for Injury Narrative Classification. SHILAP Revista de lepidopterología. 5(3). 1684–1694.
6.
Nanda, Gaurav, et al.. (2024). Comparing human text classification performance and explainability with large language and machine learning models using eye-tracking. Scientific Reports. 14(1). 14295–14295. 2 indexed citations
7.
Nanda, Gaurav, et al.. (2024). Work-in-Progress: Using Latent Dirichlet Allocation to uncover themes in student comments from peer evaluations of teamwork. Papers on Engineering Education Repository (American Society for Engineering Education). 2 indexed citations
8.
Nanda, Gaurav, et al.. (2023). Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research. SHILAP Revista de lepidopterología. 5(2). 473–490. 8 indexed citations
9.
Nanda, Gaurav, et al.. (2023). Analyzing Cognitive Load Associated with Manual Text Classification Task Using Eye Tracking. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 67(1). 193–198. 1 indexed citations
10.
Khasawneh, Amro, Kapil Chalil Madathil, Kevin Taaffe, et al.. (2022). Dynamic simulation of social media challenge participation to examine intervention strategies. Journal of Computational Social Science. 5(2). 1637–1662. 1 indexed citations
11.
Nanda, Gaurav, et al.. (2022). Supervised Machine Learning Models to Assess Impact of Building Parameters on Energy Efficiency. 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). 1–7. 1 indexed citations
12.
Nanda, Gaurav, et al.. (2021). Analyzing Large Collections of Open-Ended Feedback From MOOC Learners Using LDA Topic Modeling and Qualitative Analysis. IEEE Transactions on Learning Technologies. 14(2). 146–160. 47 indexed citations
13.
Douglas, Kerrie, et al.. (2020). A Case Study of Discussion Forums in Two Programming MOOCs on Different Platforms. 3 indexed citations
14.
Nanda, Gaurav & Kerrie Douglas. (2019). Machine Learning Based Decision Support System for Categorizing MOOC Discussion Forum Posts.. Educational Data Mining. 1 indexed citations
15.
Nanda, Gaurav, et al.. (2018). Understanding Learners' Opinion about Participation Certificates in Online Courses Using Topic Modeling.. Educational Data Mining. 5 indexed citations
16.
Nanda, Gaurav, Gregor Hlawacek, Srijit Goswami, et al.. (2017). Electronic transport in helium-ion-beam etched encapsulated graphene nanoribbons. Carbon. 119. 419–425. 23 indexed citations
17.
Nanda, Gaurav, Kirsten Vallmuur, & Mark R. Lehto. (2017). Improving autocoding performance of rare categories in injury classification: Is more training data or filtering the solution?. Accident Analysis & Prevention. 110. 115–127. 11 indexed citations
18.
Nanda, Gaurav, et al.. (2016). Bayesian decision support for coding occupational injury data. Journal of Safety Research. 57. 71–82. 22 indexed citations
19.
Calado, V. E., S. Goswami, Gaurav Nanda, et al.. (2015). Ballistic Josephson junctions in edge-contacted graphene. Nature Nanotechnology. 10(9). 761–764. 185 indexed citations
20.
Nanda, Gaurav, et al.. (2005). Implications of Carbon Tax on Generation Expansion Plan & GHG Emission: A Case Study on Indian Power Sector. International Journal of Emerging Electric Power Systems. 3(1). 5 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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