Dhagash Mehta

1.7k total citations
77 papers, 879 citations indexed

About

Dhagash Mehta is a scholar working on Nuclear and High Energy Physics, Computational Theory and Mathematics and Condensed Matter Physics. According to data from OpenAlex, Dhagash Mehta has authored 77 papers receiving a total of 879 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Nuclear and High Energy Physics, 13 papers in Computational Theory and Mathematics and 10 papers in Condensed Matter Physics. Recurrent topics in Dhagash Mehta's work include Particle physics theoretical and experimental studies (14 papers), Quantum Chromodynamics and Particle Interactions (14 papers) and Black Holes and Theoretical Physics (13 papers). Dhagash Mehta is often cited by papers focused on Particle physics theoretical and experimental studies (14 papers), Quantum Chromodynamics and Particle Interactions (14 papers) and Black Holes and Theoretical Physics (13 papers). Dhagash Mehta collaborates with scholars based in United States, United Kingdom and Australia. Dhagash Mehta's co-authors include Michael Kästner, Jonathan D. Hauenstein, David J. Wales, Hung D. Nguyen, Konstantin Turitsyn, Jon-Ivar Skullerud, M. Maniatis, Soumalya Sarkar, Yang‐Hui He and Markus Rummel and has published in prestigious journals such as Physical Review Letters, The Journal of Chemical Physics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Dhagash Mehta

65 papers receiving 861 citations

Peers

Dhagash Mehta
R. Roy Canada
Lorenzo Sadun United States
Eli Berger Israel
Peter D. Hislop United States
J A Oteo Spain
Leonard Gross United States
Richard Stong United States
Lev Shchur Russia
R. Vilela Mendes Portugal
Audrey Terras United States
R. Roy Canada
Dhagash Mehta
Citations per year, relative to Dhagash Mehta Dhagash Mehta (= 1×) peers R. Roy

Countries citing papers authored by Dhagash Mehta

Since Specialization
Citations

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

Fields of papers citing papers by Dhagash Mehta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dhagash Mehta

This figure shows the co-authorship network connecting the top 25 collaborators of Dhagash Mehta. A scholar is included among the top collaborators of Dhagash Mehta 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 Dhagash Mehta. Dhagash Mehta 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.
Lee, Yongjae, Chanyeol Choi, Chung-Chi Chen, et al.. (2025). Advances in Financial AI: Innovations, Risk, and Responsibility in the Era of LLMs. 6912–6915.
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Zhu, Fengbin, Yunshan Ma, Fuli Feng, et al.. (2025). FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI. 4184–4187.
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Tóth, Máté, et al.. (2024). Company Similarity Using Large Language Models. 1–9. 1 indexed citations
9.
Mehta, Dhagash, et al.. (2023). Quantifying Outlierness of Funds from their Categories using Supervised Similarity. 655–663. 3 indexed citations
10.
Chen, Tianran, et al.. (2018). Counting Equilibria of the Kuramoto Model Using Birationally Invariant Intersection Index. 2(4). 489–507. 17 indexed citations
11.
Mehta, Dhagash, Xiaojun Zhao, Edgar A. Bernal, & David J. Wales. (2018). Loss surface of XOR artificial neural networks. Physical review. E. 97(5). 52307–52307. 9 indexed citations
12.
Mehta, Dhagash, et al.. (2015). Energy landscape of the finite-size mean-field 2-spin spherical model and topology trivialization. Physical Review E. 91(2). 22133–22133. 5 indexed citations
13.
Fister, Leonard, et al.. (2013). Phase transitions and gluodynamics in 2-colour matter at high \ndensity. Maynooth University ePrints and eTheses Archive (Maynooth University). 42 indexed citations
14.
Mehta, Dhagash, Daniel A. Stariolo, & Michael Kästner. (2013). Energy landscape of the finite-size spherical three-spin glass model. Physical Review E. 87(5). 52143–52143. 17 indexed citations
15.
Mehta, Dhagash, et al.. (2013). Finding all flux vacua in an explicit example. Journal of High Energy Physics. 2013(6). 28 indexed citations
16.
Huijse, Liza, et al.. (2012). Maynooth University ePrints and eTheses Archive (Maynooth University). 17 indexed citations
17.
Mehta, Dhagash, et al.. (2012). Sign problem for supersymmetric Yang-Mills theories on the lattice. 78–78. 3 indexed citations
18.
Maniatis, M. & Dhagash Mehta. (2012). Minimizing Higgs potentials via numerical polynomial homotopy continuation. The European Physical Journal Plus. 127(8). 25 indexed citations
19.
Kästner, Michael & Dhagash Mehta. (2011). Phase Transitions Detached from Stationary Points of the Energy Landscape. Physical Review Letters. 107(16). 160602–160602. 34 indexed citations
20.
Mehta, Dhagash, et al.. (2009). ’t Hooft-Polyakov monopoles in latticeSU(N)+adjoint Higgstheory. Physical review. D. Particles, fields, gravitation, and cosmology. 80(6). 6 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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