Sergei Gleyzer

2.5k total citations
26 papers, 95 citations indexed

About

Sergei Gleyzer is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Sergei Gleyzer has authored 26 papers receiving a total of 95 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Nuclear and High Energy Physics, 9 papers in Artificial Intelligence and 5 papers in Computer Networks and Communications. Recurrent topics in Sergei Gleyzer's work include Particle physics theoretical and experimental studies (17 papers), Particle Detector Development and Performance (14 papers) and Computational Physics and Python Applications (6 papers). Sergei Gleyzer is often cited by papers focused on Particle physics theoretical and experimental studies (17 papers), Particle Detector Development and Performance (14 papers) and Computational Physics and Python Applications (6 papers). Sergei Gleyzer collaborates with scholars based in United States, India and Germany. Sergei Gleyzer's co-authors include M. Paulini, Michael Benjamin Andrews, H. Prosper, Barnabás Póczos, M. Narain, B. Burkle, E. Usai, D. Acosta, J. Alison and Cassandra Hall and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Astrophysical Journal and Physical review. D.

In The Last Decade

Sergei Gleyzer

21 papers receiving 90 citations

Peers

Sergei Gleyzer

NHEP

RNMI

CNC

M. Pettee United States

S. Giagu Italy

Daniel Guest United States

F. Canelli Switzerland

Sitian Qian China

C. Li China

M. Feickert United States

Sung Hak Lim United States

N. Chernyavskaya Switzerland

R. Kansal United States

M. Pettee United States

Sergei Gleyzer

58 ×0.9

NHEP

33 ×1.1

8 ×0.7

5 ×0.5

RNMI

2 ×0.3

CNC

Citations per year, relative to Sergei Gleyzer Sergei Gleyzer (= 1×) peers M. Pettee

Countries citing papers authored by Sergei Gleyzer

Since Specialization

Citations

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

Fields of papers citing papers by Sergei Gleyzer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergei Gleyzer

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

All Works

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20 of 20 papers shown

Rai, Umesh, et al.. (2024). OCR Approaches for Humanities: Applications of Artificial Intelligence/Machine Learning on Transcription and Transliteration of Historical Documents. 1(1-2). 85–112. 1 indexed citations

Gleyzer, Sergei, et al.. (2024). A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks. Axioms. 13(3). 160–160. 4 indexed citations

Gleyzer, Sergei, et al.. (2024). Quantum Vision Transformers for Quark–Gluon Classification. Axioms. 13(5). 323–323. 2 indexed citations

Gleyzer, Sergei, et al.. (2024). ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks. Axioms. 13(3). 188–188. 2 indexed citations

Meier, Stefan, et al.. (2024). Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors. Machine Learning Science and Technology. 5(2). 25019–25019. 1 indexed citations

Toomey, Michael W., et al.. (2024). DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data. Machine Learning Science and Technology. 5(3). 35076–35076. 1 indexed citations

Gleyzer, Sergei, et al.. (2023). SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning. Machine Learning Science and Technology. 4(1). 15007–15007. 10 indexed citations

Hall, Cassandra, et al.. (2023). Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning. The Astrophysical Journal. 947(2). 60–60. 2 indexed citations

Alexander, Stephon, et al.. (2023). Domain Adaptation for Simulation-based Dark Matter Searches with Strong Gravitational Lensing. The Astrophysical Journal. 954(1). 28–28. 3 indexed citations

10.

Andrews, Michael Benjamin, B. Burkle, Yu Chen, et al.. (2022). End-to-end jet classification of boosted top quarks with the CMS open data. Physical review. D. 105(5). 2 indexed citations

11.

Hall, Cassandra, et al.. (2022). Locating Hidden Exoplanets in ALMA Data Using Machine Learning. The Astrophysical Journal. 941(2). 192–192. 5 indexed citations

12.

Hariri, A., et al.. (2021). Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics. SHILAP Revista de lepidopterología. 251. 3051–3051. 2 indexed citations

13.

Andrews, Michael Benjamin, J. Alison, S. An, et al.. (2020). End-to-end jet classification of quarks and gluons with the CMS Open Data. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 977. 164304–164304. 18 indexed citations

14.

Gleyzer, Sergei, et al.. (2019). New Machine Learning Developments in ROOT/TMVA. SHILAP Revista de lepidopterología. 214. 6014–6014. 1 indexed citations

15.

Andrews, Michael Benjamin, M. Paulini, Sergei Gleyzer, & Barnabás Póczos. (2019). Exploring End-to-end Deep Learning Applications for Event Classification at CMS. SHILAP Revista de lepidopterología. 214. 6031–6031. 3 indexed citations

16.

Andrews, Michael Benjamin, M. Paulini, Sergei Gleyzer, & Barnabás Póczos. (2018). End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC. arXiv (Cornell University). 5 indexed citations

17.

Acosta, D., A. Brinkerhoff, E. L. Busch, et al.. (2018). Boosted Decision Trees in the Level-1 Muon Endcap Trigger at CMS. Journal of Physics Conference Series. 1085. 42042–42042. 15 indexed citations

18.

Low, J. F., D. Acosta, A. Brinkerhoff, et al.. (2018). Boosted Decision Trees in the CMS Level-1 Endcap Muon Trigger. CERN Document Server (European Organization for Nuclear Research). 143–143.

19.

Bagoly, A., A. J. Bevan, A. Carnes, et al.. (2017). Machine Learning Developments in ROOT. Journal of Physics Conference Series. 898. 72046–72046. 1 indexed citations

20.

Gleyzer, Sergei, et al.. (2016). Development of Machine Learning Tools in ROOT. Journal of Physics Conference Series. 762. 12043–12043.

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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