Nakul Verma

1.3k total citations
21 papers, 629 citations indexed

About

Nakul Verma is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Nakul Verma has authored 21 papers receiving a total of 629 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Computational Theory and Mathematics. Recurrent topics in Nakul Verma's work include Machine Learning and Algorithms (3 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Topological and Geometric Data Analysis (3 papers). Nakul Verma is often cited by papers focused on Machine Learning and Algorithms (3 papers), Explainable Artificial Intelligence (XAI) (3 papers) and Topological and Geometric Data Analysis (3 papers). Nakul Verma collaborates with scholars based in United States, Canada and Australia. Nakul Verma's co-authors include Sanjoy Dasgupta, Mayank Kabra, Kristin Branson, Yoav Freund, Sundararajan Sellamanickam, Vinod Nair, Dhruv Mahajan, Wendy Guo, Jian‐Zhong Guo and Adam W. Hantman and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Machine Learning Research.

In The Last Decade

Nakul Verma

20 papers receiving 607 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nakul Verma United States	11	202	189	145	75	52	21	629
Chen‐Sen Ouyang Taiwan	14	127 0.6×	252 1.3×	212 1.5×	33 0.4×	44 0.8×	70	670
Novi Quadrianto United Kingdom	15	320 1.6×	473 2.5×	249 1.7×	90 1.2×	13 0.3×	40	1.0k
Zhiyong Yang China	16	367 1.8×	363 1.9×	345 2.4×	51 0.7×	12 0.2×	92	1.1k
Xiaodan Zhang China	19	423 2.1×	423 2.2×	193 1.3×	18 0.2×	21 0.4×	83	1.2k
Sergiu M. Dascalu United States	14	160 0.8×	183 1.0×	69 0.5×	13 0.2×	71 1.4×	140	816
Lan Ma China	11	163 0.8×	75 0.4×	151 1.0×	67 0.9×	6 0.1×	40	458
Francisco J. Martínez-Murcia Spain	21	238 1.2×	264 1.4×	304 2.1×	29 0.4×	9 0.2×	62	1.3k
Guojun Dai China	15	121 0.6×	156 0.8×	457 3.2×	37 0.5×	48 0.9×	48	967
Harri Siirtola Finland	16	341 1.7×	164 0.9×	48 0.3×	20 0.3×	12 0.2×	47	650
Yifan Xu China	13	90 0.4×	207 1.1×	342 2.4×	79 1.1×	18 0.3×	42	748

Countries citing papers authored by Nakul Verma

Since Specialization

Citations

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

Fields of papers citing papers by Nakul Verma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nakul Verma

This figure shows the co-authorship network connecting the top 25 collaborators of Nakul Verma. A scholar is included among the top collaborators of Nakul Verma 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 Nakul Verma. Nakul Verma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Jiannan, et al.. (2022). Complaint-Driven Training Data Debugging at Interactive Speeds. Proceedings of the 2022 International Conference on Management of Data. 369–383. 5 indexed citations

Wang, Jiannan, et al.. (2022). How I stopped worrying about training data bugs and started complaining. 1–5.

Drori, Iddo, Sarah Zhang, Leonard Tang, et al.. (2022). A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level. Proceedings of the National Academy of Sciences. 119(32). e2123433119–e2123433119. 68 indexed citations

Salleb-Aouissi, Ansaf, et al.. (2021). Automated Symbolic Law Discovery: A Computer Vision Approach. Proceedings of the AAAI Conference on Artificial Intelligence. 35(1). 660–668. 5 indexed citations

Cowgill, Bo, et al.. (2020). Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. 679–681. 52 indexed citations

Zhong, Ziyuan, et al.. (2019). Noise-tolerant fair classification. arXiv (Cornell University). 32. 294–305. 2 indexed citations

Sauerbrei, Britton, Jian‐Zhong Guo, Jeremy D. Cohen, et al.. (2019). Cortical pattern generation during dexterous movement is input-driven. Nature. 577(7790). 386–391. 163 indexed citations

Kpotufe, Samory & Nakul Verma. (2017). Time-accuracy tradeoffs in kernel prediction: controlling prediction quality. Journal of Machine Learning Research. 18(44). 1443–1471. 2 indexed citations

Verma, Nakul & Kristin Branson. (2015). Sample complexity of learning mahalanobis distance metrics. Neural Information Processing Systems. 28. 2584–2592. 6 indexed citations

10.

Milosevic, Bojan, Nakul Verma, Sameer Tilak, et al.. (2013). Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 2012. 247–254. 9 indexed citations

11.

Verma, Nakul. (2012). Distance Preserving Embeddings for General n-Dimensional Manifolds.. Journal of Machine Learning Research. 14(1). 2415–2448. 15 indexed citations

12.

Verma, Nakul, Samory Kpotufe, & Sanjoy Dasgupta. (2012). Which Spatial Partition Trees are Adaptive to Intrinsic Dimension?. arXiv (Cornell University). 565–574. 33 indexed citations

13.

Nikzad, Nima, Nakul Verma, Elizabeth Bales, et al.. (2012). CitiSense. 1–8. 62 indexed citations

14.

Dasgupta, Sanjoy & Nakul Verma. (2012). Learning from data with low intrinsic dimension. 2 indexed citations

15.

Verma, Nakul, Dhruv Mahajan, Sundararajan Sellamanickam, & Vinod Nair. (2012). Learning hierarchical similarity metrics. 2280–2287. 70 indexed citations

16.

Nikzad, Nima, Nakul Verma, Piero Zappi, et al.. (2012). Citisense. 23–24. 13 indexed citations

17.

Babenko, Boris, et al.. (2011). Multiple Instance Learning with Manifold Bags. International Conference on Machine Learning. 81–88. 29 indexed citations

18.

Shukla, Satish, et al.. (2011). GENERALIZATON OF FIXED POINT THEOREMS IN PARTIAL CONE METRIC SPACES. 2(4). 1 indexed citations

19.

Freund, Yoav, Sanjoy Dasgupta, Mayank Kabra, & Nakul Verma. (2007). Learning the structure of manifolds using random projections. Neural Information Processing Systems. 20. 473–480. 70 indexed citations

20.

Dasgupta, Sanjoy, Daniel Hsu, & Nakul Verma. (2006). A concentration theorem for projections. arXiv (Cornell University). 114–121. 17 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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