J. Duarte

89.6k total citations · 1 hit paper
64 papers, 691 citations indexed

About

J. Duarte is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Radiation. According to data from OpenAlex, J. Duarte has authored 64 papers receiving a total of 691 indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Nuclear and High Energy Physics, 18 papers in Artificial Intelligence and 10 papers in Radiation. Recurrent topics in J. Duarte's work include Particle physics theoretical and experimental studies (34 papers), Particle Detector Development and Performance (26 papers) and High-Energy Particle Collisions Research (12 papers). J. Duarte is often cited by papers focused on Particle physics theoretical and experimental studies (34 papers), Particle Detector Development and Performance (26 papers) and High-Energy Particle Collisions Research (12 papers). J. Duarte collaborates with scholars based in United States, Switzerland and Brazil. J. Duarte's co-authors include M. Pierini, Z. Wu, J. Ngadiuba, N. Chernyavskaya, R. Kansal, Vladimir Lončar, M. Spiropulu, Jean-Roch Vlimant, T. Q. Nguyen and Nhan Viet Tran and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of High Energy Physics and Astronomy and Astrophysics.

In The Last Decade

J. Duarte

57 papers receiving 674 citations

Hit Papers

fastmachinelearning/hls4ml: coris 2021 2026 2022 2024 2021 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. fastmachinelearning/hls4ml: coris
    2021 202 citations J. Duarte, Z. Wu et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Duarte United States 13 343 277 125 114 80 64 691
A. Kugel Germany 11 123 0.4× 33 0.1× 52 0.4× 127 1.1× 79 1.0× 62 386
A. Pereira Spain 12 179 0.5× 118 0.4× 57 0.5× 45 0.4× 7 0.1× 50 462
Mohammad Faisal Pakistan 15 86 0.3× 98 0.4× 80 0.6× 156 1.4× 7 0.1× 43 696
Sebastián Sánchez Spain 9 63 0.2× 57 0.2× 34 0.3× 78 0.7× 98 1.2× 75 447
S. Gorbunov Germany 10 53 0.2× 441 1.6× 33 0.3× 31 0.3× 42 0.5× 26 583
Subramania I. Sudharsanan United States 9 26 0.1× 191 0.7× 121 1.0× 93 0.8× 21 0.3× 39 533
Chenhao Ma China 15 130 0.4× 197 0.7× 109 0.9× 37 0.3× 5 0.1× 61 543
Roman Novak Slovenia 10 25 0.1× 144 0.5× 49 0.4× 77 0.7× 9 0.1× 42 322
Christian Plessl Germany 14 35 0.1× 89 0.3× 33 0.3× 262 2.3× 423 5.3× 103 761
J. Stillerman United States 14 351 1.0× 30 0.1× 7 0.1× 67 0.6× 32 0.4× 71 587

Countries citing papers authored by J. Duarte

Since Specialization
Citations

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

Fields of papers citing papers by J. Duarte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Duarte

This figure shows the co-authorship network connecting the top 25 collaborators of J. Duarte. A scholar is included among the top collaborators of J. Duarte 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 J. Duarte. J. Duarte 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.
Stamenkovic, M., Alexander Shmakov, M. J. Fenton, et al.. (2025). Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks. Journal of High Energy Physics. 2025(11).
2.
Geniesse, Caleb, George A. Constantinides, Nhan Viet Tran, et al.. (2025). Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT. 25–35. 2 indexed citations
3.
Pata, Joosep, et al.. (2024). Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors. Communications Physics. 7(1). 4 indexed citations
4.
Que, Zhiqiang, J. Duarte, J. Haller, et al.. (2024). Ultrafast jet classification at the HL-LHC. Machine Learning Science and Technology. 5(3). 35017–35017. 6 indexed citations
5.
Tran, Nhan Viet, et al.. (2024). FKeras: A Sensitivity Analysis Tool for Edge Neural Networks. 1(3). 1–27. 1 indexed citations
6.
Duarte, J.. (2024). Novel machine learning applications at the LHC. CERN Document Server (European Organization for Nuclear Research). 12–12.
7.
Orzari, Breno, N. Chernyavskaya, J. Duarte, et al.. (2023). LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows. Machine Learning Science and Technology. 4(4). 45023–45023. 2 indexed citations
8.
Amaro, Rommie E., J. Duarte, Christopher Irving, et al.. (2023). Voyager – An Innovative Computational Resource for Artificial Intelligence & Machine Learning Applications in Science and Engineering. Practice and Experience in Advanced Research Computing. 278–282. 2 indexed citations
9.
Lončar, Vladimir, et al.. (2023). Tailor : Altering Skip Connections for Resource-Efficient Inference. ACM Transactions on Reconfigurable Technology and Systems. 17(1). 1–23. 4 indexed citations
10.
Lončar, Vladimir, et al.. (2023). Adapting Skip Connections for Resource-Efficient FPGA Inference. 229–229. 2 indexed citations
11.
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
12.
Kansal, R., et al.. (2023). JetNet: A Python package for accessing open datasetsand benchmarking machine learning methods in high energy physics. The Journal of Open Source Software. 8(90). 5789–5789. 3 indexed citations
13.
Govorkova, Ekaterina, T. K. Aarrestad, Vladimir Lončar, et al.. (2022). Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Nature Machine Intelligence. 4(2). 154–161. 50 indexed citations
14.
Chernyavskaya, N., J. Duarte, Dimitrios Gunopulos, et al.. (2022). Particle-based fast jet simulation at the LHC with variational autoencoders. Machine Learning Science and Technology. 3(3). 35003–35003. 16 indexed citations
15.
Govorkova, Ekaterina, T. K. Aarrestad, Vladimir Lončar, et al.. (2022). Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Nature Machine Intelligence. 4(4). 414–414. 1 indexed citations
16.
Nguyen, T. Q., Jean-Roch Vlimant, Olmo Cerri, et al.. (2020). Interaction networks for the identification of boosted H→bb¯ decays. Physical review. D. 102(1). 36 indexed citations
17.
Ngadiuba, J., Vladimir Lončar, M. Pierini, et al.. (2020). Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. DSpace@MIT (Massachusetts Institute of Technology). 44 indexed citations
18.
Nguyen, T. Q., Jean-Roch Vlimant, Olmo Cerri, et al.. (2019). Interaction networks for the identification of boosted Higgs to bb decays. arXiv (Cornell University). 3 indexed citations
19.
Duarte, J., et al.. (2015). Electric charge quantization in S U ( 3 ) c ⊗ S U ( 4 ) L ⊗ U ( 1 ) X models. Nuclear and Particle Physics Proceedings. 267-269. 302–304. 1 indexed citations
20.
Tu, Y., A. Apresyan, J. M. Lawhorn, et al.. (2012). Centrality dependence of dihadron correlations and azimuthal anisotropy harmonics in PbPb collisions at âSNN = 2.76 Tev. eScholarship (California Digital Library). 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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