E. A. Huerta

79.1k total citations
64 papers, 1.7k citations indexed

About

E. A. Huerta is a scholar working on Astronomy and Astrophysics, Statistical and Nonlinear Physics and Geophysics. According to data from OpenAlex, E. A. Huerta has authored 64 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Astronomy and Astrophysics, 8 papers in Statistical and Nonlinear Physics and 7 papers in Geophysics. Recurrent topics in E. A. Huerta's work include Pulsars and Gravitational Waves Research (39 papers), Gamma-ray bursts and supernovae (22 papers) and Astrophysical Phenomena and Observations (20 papers). E. A. Huerta is often cited by papers focused on Pulsars and Gravitational Waves Research (39 papers), Gamma-ray bursts and supernovae (22 papers) and Astrophysical Phenomena and Observations (20 papers). E. A. Huerta collaborates with scholars based in United States, United Kingdom and India. E. A. Huerta's co-authors include Daniel George, J. R. Gair, Wei Wei, P. Kumar, D. Brown, Hongyu Shen, Sean T. McWilliams, Roland Haas, Asad Khan and Nicolás Yunes and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and The Astrophysical Journal.

In The Last Decade

E. A. Huerta

60 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
E. A. Huerta United States 24 1.4k 327 273 187 135 64 1.7k
Luis Tenorio United States 19 521 0.4× 477 1.5× 141 0.5× 230 1.2× 64 0.5× 61 1.6k
L. Sun China 18 574 0.4× 117 0.4× 259 0.9× 262 1.4× 92 0.7× 75 964
P. J. MacNeice United States 28 3.0k 2.2× 164 0.5× 164 0.6× 638 3.4× 102 0.8× 89 4.0k
M. W. Coughlin United States 27 2.1k 1.6× 433 1.3× 151 0.6× 560 3.0× 298 2.2× 133 2.4k
Enrico Camporeale United States 21 1.3k 0.9× 316 1.0× 128 0.5× 241 1.3× 41 0.3× 70 1.6k
Katrin Heitmann United States 34 2.2k 1.6× 94 0.3× 332 1.2× 969 5.2× 76 0.6× 95 3.1k
Daniel Foreman-Mackey United States 27 3.2k 2.4× 84 0.3× 150 0.5× 316 1.7× 79 0.6× 84 3.6k
T. Linde United States 17 1.9k 1.4× 81 0.2× 109 0.4× 226 1.2× 64 0.5× 26 2.5k
G. Crew United States 20 1.6k 1.2× 170 0.5× 310 1.1× 273 1.5× 41 0.3× 53 2.1k
M. P. Hobson United Kingdom 20 688 0.5× 46 0.1× 120 0.4× 512 2.7× 71 0.5× 41 1.5k

Countries citing papers authored by E. A. Huerta

Since Specialization
Citations

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

Fields of papers citing papers by E. A. Huerta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. A. Huerta. 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 E. A. Huerta. The network helps show where E. A. Huerta may publish in the future.

Co-authorship network of co-authors of E. A. Huerta

This figure shows the co-authorship network connecting the top 25 collaborators of E. A. Huerta. A scholar is included among the top collaborators of E. A. Huerta 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 E. A. Huerta. E. A. Huerta 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.
Zheng, Xin, Lin X. Chen, Peter Zapol, et al.. (2025). Photochemical CO2 Reduction by a Postsynthetically Modified Zr-MOF. Inorganic Chemistry. 64(36). 18304–18315.
2.
Park, Hyun, et al.. (2024). APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics. Proceedings of the National Academy of Sciences. 121(27). e2311888121–e2311888121. 5 indexed citations
3.
Huerta, E. A., et al.. (2024). Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers. Machine Learning Science and Technology. 5(2). 25056–25056. 4 indexed citations
4.
Park, Hyun, Ruijie Zhu, E. A. Huerta, et al.. (2024). A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture. Communications Chemistry. 7(1). 21–21. 49 indexed citations
5.
Klarqvist, Marcus D. R., Miao Li, Kibaek Kim, et al.. (2024). Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx. Computational and Structural Biotechnology Journal. 28. 29–39. 4 indexed citations
6.
Hannon, Stephen, Bradley C. Whitmore, Janice Lee, et al.. (2023). Star cluster classification using deep transfer learning with PHANGS-HST. Monthly Notices of the Royal Astronomical Society. 526(2). 2991–3006. 4 indexed citations
7.
Huerta, E. A., et al.. (2023). Magnetohydrodynamics with physics informed neural operators. Machine Learning Science and Technology. 4(3). 35002–35002. 9 indexed citations
8.
Park, Hyun, et al.. (2023). End-to-end AI framework for interpretable prediction of molecular and crystal properties. Machine Learning Science and Technology. 4(2). 25036–25036. 6 indexed citations
9.
Duarte, J., A. Roy, E. A. Huerta, et al.. (2023). FAIR AI models in high energy physics. Machine Learning Science and Technology. 4(4). 45062–45062. 3 indexed citations
10.
Huerta, E. A., et al.. (2023). Applications of physics informed neural operators. Machine Learning Science and Technology. 4(2). 25022–25022. 32 indexed citations
11.
Huerta, E. A., Zhengchun Liu, Ryan Chard, et al.. (2022). FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy. Scientific Data. 9(1). 657–657. 27 indexed citations
12.
Khan, Asad, E. A. Huerta, & Huihuo Zheng. (2022). Interpretable AI forecasting for numerical relativity waveforms of quasicircular, spinning, nonprecessing binary black hole mergers. Physical review. D. 105(2). 11 indexed citations
13.
14.
Huerta, E. A., Asad Khan, Edward Davis, et al.. (2020). Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure. Journal Of Big Data. 7(1). 36 indexed citations
15.
Wei, Wei, et al.. (2020). Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers. Physics Letters B. 812. 136029–136029. 33 indexed citations
16.
Huerta, E. A., et al.. (2020). Artificial neural network subgrid models of 2D compressible magnetohydrodynamic turbulence. Physical review. D. 101(8). 17 indexed citations
17.
Khan, Asad, et al.. (2020). Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers. Physics Letters B. 808. 135628–135628. 13 indexed citations
18.
Khan, Asad, E. A. Huerta, Sibo Wang, et al.. (2019). Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey. Physics Letters B. 795. 248–258. 29 indexed citations
19.
Shen, Hongyu, Daniel George, & E. A. Huerta. (2018). Glitch Classification and Clustering for LIGO with Deep Transfer Learning.. Bulletin of the American Physical Society. 2018. 3 indexed citations
20.
Huerta, E. A. & Jonathan R. Gair. (2009). Influence of conservative corrections on parameter estimation for EMRIs. arXiv (Cornell University). 1 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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