Navin Goyal

3.5k total citations
68 papers, 1.0k citations indexed

About

Navin Goyal is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Navin Goyal has authored 68 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computational Theory and Mathematics, 18 papers in Artificial Intelligence and 8 papers in Computer Networks and Communications. Recurrent topics in Navin Goyal's work include Complexity and Algorithms in Graphs (14 papers), Advanced Graph Theory Research (9 papers) and Statistical Methods in Clinical Trials (7 papers). Navin Goyal is often cited by papers focused on Complexity and Algorithms in Graphs (14 papers), Advanced Graph Theory Research (9 papers) and Statistical Methods in Clinical Trials (7 papers). Navin Goyal collaborates with scholars based in United States, United Kingdom and India. Navin Goyal's co-authors include Shipra Agrawal, Joseph Cheriyan, Santosh Vempala, Ruth J. Mayer, Aili L. Lazaar, Michael Saks, Luis Rademacher, Roberto Goméni, Sandra Baldwin and Ian B. Wilkinson and has published in prestigious journals such as Antimicrobial Agents and Chemotherapy, Journal of Pharmacology and Experimental Therapeutics and Neuropsychopharmacology.

In The Last Decade

Navin Goyal

64 papers receiving 989 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Navin Goyal United States 16 242 221 183 127 120 68 1.0k
Rashid Ali India 20 335 1.4× 65 0.3× 66 0.4× 24 0.2× 343 2.9× 121 1.6k
Maria Pini Italy 25 216 0.9× 232 1.0× 135 0.7× 95 0.7× 426 3.5× 88 1.9k
Seunghee Bae South Korea 27 223 0.9× 29 0.1× 454 2.5× 33 0.3× 864 7.2× 127 2.4k
Jan Peleška Germany 17 198 0.8× 99 0.4× 95 0.5× 235 1.9× 84 0.7× 91 1.2k
Guy De Tré Belgium 18 454 1.9× 417 1.9× 240 1.3× 185 1.5× 387 3.2× 173 1.7k
Jinjin Xu China 27 519 2.1× 22 0.1× 100 0.5× 32 0.3× 1.0k 8.6× 63 2.3k
Zhang Qishan China 16 226 0.9× 79 0.4× 102 0.6× 33 0.3× 239 2.0× 153 1.1k
Juan Manuel Moreno Spain 17 180 0.7× 261 1.2× 25 0.1× 106 0.8× 207 1.7× 48 1.3k
Sachin Garg United States 17 114 0.5× 41 0.2× 281 1.5× 15 0.1× 57 0.5× 49 1.0k
Amit Gupta India 24 242 1.0× 33 0.1× 206 1.1× 51 0.4× 497 4.1× 114 1.9k

Countries citing papers authored by Navin Goyal

Since Specialization
Citations

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

Fields of papers citing papers by Navin Goyal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Navin Goyal

This figure shows the co-authorship network connecting the top 25 collaborators of Navin Goyal. A scholar is included among the top collaborators of Navin Goyal 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 Navin Goyal. Navin Goyal 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.
He, Tianxing, et al.. (2025). Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers. Transactions of the Association for Computational Linguistics. 13. 121–141. 3 indexed citations
2.
Chandasana, Hardik, Ann M. Buchanan, Michael J. McKenna, et al.. (2024). A Model‐Based Approach Supporting Abacavir/Dolutegravir/Lamivudine Fixed‐Dose Combination Approval in Children Living with HIV‐1. The Journal of Clinical Pharmacology. 65(1). 18–27.
3.
Thakkar, Nilay, Mindy Magee, Navin Goyal, et al.. (2023). Model‐Based Dose Selection of Fostemsavir for Pediatric Populations With Multidrug‐Resistant HIV‐1 and Relative Bioavailability Assessment in Healthy Adults. Clinical Pharmacology in Drug Development. 12(10). 991–1000.
4.
Goyal, Navin, et al.. (2023). Role of Modeling and Simulation in Preclinical and Clinical Long-Acting Injectable Drug Development. The AAPS Journal. 25(6). 99–99. 8 indexed citations
5.
6.
Vélez, Iván Darío, Tran Tinh Hien, Justin A. Green, et al.. (2021). Tafenoquine exposure assessment, safety, and relapse prevention efficacy in children with Plasmodium vivax malaria: open-label, single-arm, non-comparative, multicentre, pharmacokinetic bridging, phase 2 trial. The Lancet Child & Adolescent Health. 6(2). 86–95. 7 indexed citations
7.
Xu, Xiaoping, Navin Goyal, Melissa H. Costell, et al.. (2020). Identification of a Human Whole Blood–Based Endothelial Cell Impedance Assay for Assessing Clinical Transient Receptor Potential Vanilloid 4 Target Engagement Ex Vivo. Journal of Pharmacology and Experimental Therapeutics. 376(3). 436–443. 1 indexed citations
8.
Chaturvedula, Ayyappa, et al.. (2020). Broader Implications of Modeling and Simulation (M&S) Tools in Pharmacotherapeutic Decisions: A Cautionary Optimism. Frontiers in Pharmacology. 11. 571–571. 1 indexed citations
9.
Goyal, Navin, et al.. (2019). Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature. Conference on Learning Theory. 1519–1561. 1 indexed citations
10.
Goyal, Navin & Manoj Gupta. (2019). Better analysis of binary search tree on decomposable sequences. Theoretical Computer Science. 776. 19–42. 5 indexed citations
11.
12.
Bhattacharyya, Chiranjib, et al.. (2016). Non-negative matrix factorization under heavy noise. International Conference on Machine Learning. 1426–1434. 6 indexed citations
13.
Goyal, Navin, et al.. (2014). . Theory of Computing. 10(1). 237–256. 6 indexed citations
14.
Goyal, Navin & Roberto Goméni. (2012). Exposure–Response modeling of anti-depressant treatments: the confounding role of placebo effect. Journal of Pharmacokinetics and Pharmacodynamics. 40(3). 389–399. 3 indexed citations
15.
Goyal, Navin & Roberto Goméni. (2011). A Novel Metric to Assess the Clinical Utility of a Drug in the Presence of Efficacy and Dropout Information. Clinical Pharmacology & Therapeutics. 91(2). 215–219. 8 indexed citations
16.
Goyal, Navin & Michael Saks. (2010). . Theory of Computing. 6(1). 113–134. 1 indexed citations
17.
Goyal, Navin, Luis Rademacher, & Santosh Vempala. (2009). Expanders via random spanning trees. arXiv (Cornell University). 576–585. 21 indexed citations
18.
Chattopadhyay, Arkadev, Navin Goyal, Pavel Pudlák, & Denis Thérien. (2006). Lower bounds for circuits with MOD_m gates. 41. 709–718. 13 indexed citations
19.
Goyal, Navin & Michael Saks. (2005). Rounds vs queries trade-off in noisy computation. Symposium on Discrete Algorithms. 632–639. 1 indexed citations
20.
Singhal, Pravin C., et al.. (1992). Specific Receptors for Beta-Endorphin on Mesangial Cells. ˜The œNephron journals/Nephron journals. 62(1). 66–70. 4 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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