Vladimir Lončar

790 total citations
30 papers, 376 citations indexed

About

Vladimir Lončar is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Vladimir Lončar has authored 30 papers receiving a total of 376 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Nuclear and High Energy Physics, 11 papers in Artificial Intelligence and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Vladimir Lončar's work include Particle Detector Development and Performance (11 papers), Particle physics theoretical and experimental studies (10 papers) and Cold Atom Physics and Bose-Einstein Condensates (4 papers). Vladimir Lončar is often cited by papers focused on Particle Detector Development and Performance (11 papers), Particle physics theoretical and experimental studies (10 papers) and Cold Atom Physics and Bose-Einstein Condensates (4 papers). Vladimir Lončar collaborates with scholars based in United States, Switzerland and Serbia. Vladimir Lončar's co-authors include Antun Balaž, Sadhan K. Adhikari, Paulsamy Muruganandam, M. Pierini, J. Ngadiuba, J. Duarte, Z. Wu, S. Summers, Luis E. Young-S. and Dylan Rankin and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computer Physics Communications and Nature Machine Intelligence.

In The Last Decade

Vladimir Lončar

29 papers receiving 372 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vladimir Lončar United States 10 118 97 82 67 53 30 376
Ankur Agrawal United States 9 180 1.5× 222 2.3× 66 0.8× 27 0.4× 108 2.0× 20 439
T. Kacprzak Poland 10 70 0.6× 58 0.6× 39 0.5× 47 0.7× 339 6.4× 30 556
Gabriel Perdue United States 10 92 0.8× 154 1.6× 65 0.8× 37 0.6× 42 0.8× 30 290
Oliver Sander Germany 11 74 0.6× 118 1.2× 30 0.4× 18 0.3× 153 2.9× 84 466
J S Reeve United Kingdom 9 39 0.3× 37 0.4× 16 0.2× 22 0.3× 90 1.7× 38 323
Kohei Inoue Japan 12 42 0.4× 61 0.6× 371 4.5× 136 2.0× 37 0.7× 112 739
Song Cheng China 9 282 2.4× 265 2.7× 10 0.1× 29 0.4× 29 0.5× 26 468
Christian Schmid Germany 13 37 0.3× 20 0.2× 203 2.5× 11 0.2× 61 1.2× 47 580
Ramona Wolf Germany 8 217 1.8× 451 4.6× 16 0.2× 17 0.3× 76 1.4× 13 576
Josef Weinbub Austria 12 256 2.2× 116 1.2× 12 0.1× 13 0.2× 230 4.3× 78 598

Countries citing papers authored by Vladimir Lončar

Since Specialization
Citations

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

Fields of papers citing papers by Vladimir Lončar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vladimir Lončar

This figure shows the co-authorship network connecting the top 25 collaborators of Vladimir Lončar. A scholar is included among the top collaborators of Vladimir Lončar 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 Vladimir Lončar. Vladimir Lončar 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.
Que, Zhiqiang, et al.. (2025). da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs. ACM Transactions on Reconfigurable Technology and Systems. 19(1). 1–27. 1 indexed citations
2.
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
3.
Baldi, Tommaso Lisini, J. Ngadiuba, Nhan Viet Tran, et al.. (2024). Reliable edge machine learning hardware for scientific applications. eScholarship (California Digital Library). 1–5. 1 indexed citations
4.
Tsoi, Ho Fung, Vladimir Lončar, Ekaterina Govorkova, et al.. (2024). Symbolic Regression on FPGAs for Fast Machine Learning Inference. SHILAP Revista de lepidopterología. 295. 9036–9036. 9 indexed citations
5.
Brown, Christopher Edward, Aaron Bundock, M. Komm, et al.. (2023). Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System. Journal of Physics Conference Series. 2438(1). 12106–12106. 1 indexed citations
6.
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
7.
Lončar, Vladimir, et al.. (2023). Adapting Skip Connections for Resource-Efficient FPGA Inference. 229–229. 2 indexed citations
8.
Khoda, E. E., Dylan Rankin, R. Teixeira De Lima, et al.. (2023). Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. Machine Learning Science and Technology. 4(2). 25004–25004. 8 indexed citations
9.
Lončar, Vladimir, et al.. (2023). FPGA Resource-aware Structured Pruning for Real-Time Neural Networks. 282–283. 3 indexed citations
10.
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
11.
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
12.
Lončar, Vladimir, M. Pierini, S. Summers, et al.. (2022). Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. Machine Learning Science and Technology. 3(4). 45011–45011. 17 indexed citations
13.
Tarafdar, Naif, Giuseppe Di Guglielmo, Philip Harris, et al.. (2021). AIgean : An Open Framework for Deploying Machine Learning on Heterogeneous Clusters. ACM Transactions on Reconfigurable Technology and Systems. 15(3). 1–32. 7 indexed citations
14.
Aarrestad, T. K., et al.. (2021). Jet Single Shot Detection. SHILAP Revista de lepidopterología. 251. 4027–4027. 1 indexed citations
15.
Zhuang, Hao, T. K. Aarrestad, Vladimir Lončar, et al.. (2020). Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml. arXiv (Cornell University). 15 indexed citations
16.
Carminati, Federico, Vladimir Lončar, T. Q. Nguyen, et al.. (2020). Generative Adversarial Networks for fast simulation. Journal of Physics Conference Series. 1525(1). 12064–12064. 6 indexed citations
17.
Guglielmo, Giuseppe Di, J. Duarte, P. Harris, et al.. (2020). Fast inference of Boosted Decision Trees in FPGAs for particle physics. Journal of Instrumentation. 15(5). P05026–P05026. 50 indexed citations
18.
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
19.
Vlimant, Jean-Roch, F. Pantaleo, M. Pierini, et al.. (2019). Large-Scale Distributed Training Applied to Generative Adversarial Networks for Calorimeter Simulation. SHILAP Revista de lepidopterología. 214. 6025–6025. 3 indexed citations
20.
Young-S., Luis E., Paulsamy Muruganandam, Sadhan K. Adhikari, et al.. (2017). OpenMP GNU and Intel Fortran programs for solving the time-dependent Gross–Pitaevskii equation. Computer Physics Communications. 220. 503–506. 28 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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