Himanshu Jain

10.1k total citations · 2 hit papers
23 papers, 6.8k citations indexed

About

Himanshu Jain is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Software. According to data from OpenAlex, Himanshu Jain has authored 23 papers receiving a total of 6.8k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 11 papers in Computational Theory and Mathematics and 7 papers in Software. Recurrent topics in Himanshu Jain's work include Formal Methods in Verification (8 papers), Software Testing and Debugging Techniques (6 papers) and Text and Document Classification Technologies (5 papers). Himanshu Jain is often cited by papers focused on Formal Methods in Verification (8 papers), Software Testing and Debugging Techniques (6 papers) and Text and Document Classification Technologies (5 papers). Himanshu Jain collaborates with scholars based in India, United States and Switzerland. Himanshu Jain's co-authors include Kalyanmoy Deb, Manik Varma, Yashoteja Prabhu, Prateek Jain, Purushottam Kar, Kush Bhatia, Edmund M. Clarke, Daniel Kroening, Venkatesh Balasubramanian and Natasha Sharygina and has published in prestigious journals such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems and Microscopy Research and Technique.

In The Last Decade

Himanshu Jain

22 papers receiving 6.6k citations

Hit Papers

An Evolutionary Many-Objective Optimization Algorithm Usi... 2013 2026 2017 2021 2013 2013 1000 2.0k 3.0k 4.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Himanshu Jain India 11 3.4k 3.2k 831 800 717 23 6.8k
David Corne United Kingdom 31 3.6k 1.0× 4.0k 1.2× 654 0.8× 660 0.8× 1.3k 1.8× 161 7.8k
Tapabrata Ray Australia 40 3.3k 1.0× 3.3k 1.0× 905 1.1× 672 0.8× 578 0.8× 237 6.4k
Bernhard Sendhoff Germany 34 4.5k 1.3× 4.3k 1.3× 1.1k 1.3× 403 0.5× 518 0.7× 165 7.7k
Aimin Zhou China 29 4.5k 1.3× 4.7k 1.4× 737 0.9× 424 0.5× 522 0.7× 176 6.9k
David A. Van Veldhuizen United States 9 3.0k 0.9× 2.7k 0.8× 552 0.7× 555 0.7× 710 1.0× 18 5.6k
Carlos M. Fonseca Portugal 29 5.3k 1.5× 4.4k 1.4× 1.1k 1.4× 964 1.2× 1.2k 1.7× 77 9.6k
Ye Tian China 42 6.9k 2.0× 6.6k 2.0× 1.3k 1.6× 566 0.7× 768 1.1× 140 9.5k
N. Srinivas India 3 2.2k 0.7× 1.7k 0.5× 404 0.5× 892 1.1× 717 1.0× 14 5.5k
Boyang Qu China 43 4.9k 1.4× 6.6k 2.0× 446 0.5× 1.2k 1.6× 606 0.8× 168 9.5k

Countries citing papers authored by Himanshu Jain

Since Specialization
Citations

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

Fields of papers citing papers by Himanshu Jain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Himanshu Jain

This figure shows the co-authorship network connecting the top 25 collaborators of Himanshu Jain. A scholar is included among the top collaborators of Himanshu Jain 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 Himanshu Jain. Himanshu Jain 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.
Jain, Himanshu, et al.. (2025). Epidural Steroid Injections for Lumbar Disc Herniation and Lumbar Degenerative Disc Disease. Journal of Orthopaedic Case Reports. 15(10). 288–294.
2.
Jain, Himanshu, et al.. (2021). Integrated solution for optimal generation operation efficiency through dynamic economic dispatch on Software Technological Park of India. Materials Today Proceedings. 47. 6078–6081. 1 indexed citations
3.
Jain, Himanshu, et al.. (2019). Slice. 528–536. 63 indexed citations
4.
Dahiya, Kunal, Anshul Mittal, Kushal Dave, et al.. (2019). DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases. 1 indexed citations
5.
Ballal, Nidambur Vasudev, et al.. (2018). Evaluation of SmearOFF, maleic acid and two EDTA preparations in smear layer removal from root canal dentin. Acta Odontologica Scandinavica. 77(1). 28–32. 17 indexed citations
6.
Jain, Himanshu & Nidambur Vasudev Ballal. (2018). Comparison between the use of thermoplasticized gutta‐percha and a polydimethyl siloxane‐based material in filling internal resorptive cavities using spiral computed tomography. Microscopy Research and Technique. 82(2). 149–152. 7 indexed citations
7.
8.
Jain, Himanshu, et al.. (2017). Human Shape from Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks. Apollo (University of Cambridge). 5504–5514. 60 indexed citations
9.
Jain, Himanshu. (2016). A Web Based Application for Sentiment Analysis. International Journal of Education and Management Engineering. 7(1). 25–35. 1 indexed citations
10.
Jain, Himanshu, Yashoteja Prabhu, & Manik Varma. (2016). Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications. 935–944. 156 indexed citations
11.
Bhatia, Kush, Himanshu Jain, Purushottam Kar, Manik Varma, & Prateek Jain. (2015). Sparse local embeddings for extreme multi-label classification. Neural Information Processing Systems. 28. 730–738. 176 indexed citations
12.
Jain, Himanshu & Kalyanmoy Deb. (2013). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach. IEEE Transactions on Evolutionary Computation. 18(4). 602–622. 1487 indexed citations breakdown →
13.
Deb, Kalyanmoy & Himanshu Jain. (2013). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation. 18(4). 577–601. 4618 indexed citations breakdown →
14.
Jain, Himanshu, P. Radha Krishna, & Kamalakar Karlapalem. (2013). e-contract enactment system for effective e-governance. 344–345. 1 indexed citations
15.
Jain, Himanshu & Edmund M. Clarke. (2009). Efficient SAT solving for non-clausal formulas using DPLL, graphs, and watched cuts. 563–568. 7 indexed citations
16.
Jacoby, R., et al.. (2009). Solver technology for system-level to RTL equivalence checking. 196–201. 22 indexed citations
17.
Jain, Himanshu, Daniel Kroening, Natasha Sharygina, & E. M. Clarke. (2008). Word-Level Predicate-Abstraction and Refinement Techniques for Verifying RTL Verilog. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 27(2). 366–379. 12 indexed citations
18.
Clarke, Edmund M. & Himanshu Jain. (2008). Verification using satisfiability checking, predicate abstraction, and craig interpolation. 6 indexed citations
19.
Jain, Himanshu, Daniel Kroening, Natasha Sharygina, & Edmund M. Clarke. (2005). Word level predicate abstraction and refinement for verifying RTL verilog. 445–445. 50 indexed citations
20.
Jain, Himanshu, Daniel Kroening, Natasha Sharygina, & E. M. Clarke. (2005). Word level predicate abstraction and refinement for verifying RTL Verilog. 139. 445–450. 3 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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