S. Summers

12.5k total citations
15 papers, 173 citations indexed

About

S. Summers is a scholar working on Nuclear and High Energy Physics, Electrical and Electronic Engineering and Radiation. According to data from OpenAlex, S. Summers has authored 15 papers receiving a total of 173 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Nuclear and High Energy Physics, 4 papers in Electrical and Electronic Engineering and 3 papers in Radiation. Recurrent topics in S. Summers's work include Particle Detector Development and Performance (9 papers), Particle physics theoretical and experimental studies (7 papers) and Radiation Detection and Scintillator Technologies (3 papers). S. Summers is often cited by papers focused on Particle Detector Development and Performance (9 papers), Particle physics theoretical and experimental studies (7 papers) and Radiation Detection and Scintillator Technologies (3 papers). S. Summers collaborates with scholars based in Switzerland, United States and United Kingdom. S. Summers's co-authors include Vladimir Lončar, M. Pierini, J. Ngadiuba, Z. Wu, J. Duarte, T. K. Aarrestad, Jerzy W. Grzymala‐Busse, Philip Harris, Dylan Rankin and T. Q. Nguyen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nature Machine Intelligence and Machine Learning Science and Technology.

In The Last Decade

S. Summers

15 papers receiving 166 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
S. Summers Switzerland	7	63	52	44	37	19	15	173
J. Ngadiuba United States	8	99 1.6×	119 2.3×	53 1.2×	47 1.3×	26 1.4×	19	252
U. Fuchs Switzerland	8	15 0.2×	84 1.6×	15 0.3×	26 0.7×	6 0.3×	43	187
Roger Labahn Germany	9	73 1.2×	15 0.3×	46 1.0×	61 1.6×	3 0.2×	20	197
Sheng-Chun Kao United States	8	68 1.1×	15 0.3×	100 2.3×	125 3.4×	118 6.2×	15	265
Florian Neukart Netherlands	6	164 2.6×	12 0.2×	10 0.2×	16 0.4×	6 0.3×	30	237
Coleman Hooper United States	6	92 1.5×	4 0.1×	52 1.2×	92 2.5×	46 2.4×	11	215
Peiyan Dong United States	10	76 1.2×	3 0.1×	69 1.6×	81 2.2×	23 1.2×	25	214
Xiaolong Bai China	9	71 1.1×	22 0.4×	11 0.3×	11 0.3×	3 0.2×	12	175
Michael Abbott United Kingdom	6	101 1.6×	6 0.1×	4 0.1×	44 1.2×	18 0.9×	25	167
P. Leu Switzerland	8	46 0.7×	23 0.4×	6 0.1×	84 2.3×	2 0.1×	10	174

Countries citing papers authored by S. Summers

Since Specialization

Citations

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

Fields of papers citing papers by S. Summers

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Summers

This figure shows the co-authorship network connecting the top 25 collaborators of S. Summers. A scholar is included among the top collaborators of S. Summers 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 S. Summers. S. Summers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

Kakogeorgiou, Ioannis, et al.. (2025). Edge SpAIce: Deep Learning Deployment Pipeline for Onboard Data Reduction on Satellite FPGAs. 1243–1249. 1 indexed citations

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

Summers, S., Ioannis Bestintzanos, & G. Petrucciani. (2024). Reconstructing jets in the Phase-2 upgrade of the CMS Level-1 Trigger with a seeded cone algorithm. SHILAP Revista de lepidopterología. 295. 2024–2024. 1 indexed citations

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

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

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

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

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

Aarrestad, T. K., et al.. (2021). Jet Single Shot Detection. SHILAP Revista de lepidopterología. 251. 4027–4027. 1 indexed citations

10.

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

11.

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

12.

Duarte, J., Song Han, Philip Harris, et al.. (2019). Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. 305–305. 5 indexed citations

13.

Summers, S. & A. Rose. (2019). Kalman Filter track reconstruction on FPGAs for acceleration of the High Level Trigger of the CMS experiment at the HL-LHC. SHILAP Revista de lepidopterología. 214. 1003–1003. 2 indexed citations

14.

Kumavor, Patrick D., et al.. (2001). A 5 gigabit/sec all-optical parallel analog-to-digital converter. qe 15. 182–183. 1 indexed citations

15.

Grzymala‐Busse, Jerzy W., et al.. (1992). The use of machine learning program LERS-LB 2.5 in knowledge acquisition for expert system development in nursing.. PubMed. 9(6). 227–34. 20 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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