M. Hushchyn

2.4k total citations
15 papers, 50 citations indexed

About

M. Hushchyn is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Astronomy and Astrophysics. According to data from OpenAlex, M. Hushchyn has authored 15 papers receiving a total of 50 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Nuclear and High Energy Physics, 4 papers in Computer Networks and Communications and 3 papers in Astronomy and Astrophysics. Recurrent topics in M. Hushchyn's work include Particle physics theoretical and experimental studies (6 papers), Particle Detector Development and Performance (6 papers) and High-Energy Particle Collisions Research (3 papers). M. Hushchyn is often cited by papers focused on Particle physics theoretical and experimental studies (6 papers), Particle Detector Development and Performance (6 papers) and High-Energy Particle Collisions Research (3 papers). M. Hushchyn collaborates with scholars based in Russia, United States and Switzerland. M. Hushchyn's co-authors include A. Ustyuzhanin, N. Kazeev, Д. Деркач, Konstantin Malanchev, Cécile Germain, Jean-Roch Vlimant, Steven Farrell, A. Sapronov, A. Ryzhikov and D. Rousseau and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Astronomy and Astrophysics.

In The Last Decade

M. Hushchyn

13 papers receiving 43 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
M. Hushchyn Russia	5	14	12	10	9	5	15	50
G. B. Cerati United States	5	31 2.2×	25 2.1×	3 0.3×	2 0.2×	10 2.0×	17	69
Z. Hampel-Arias United States	4	11 0.8×	29 2.4×	7 0.7×	4 0.4×	39 7.8×	10	63
Y. Coadou France	4	53 3.8×	12 1.0×	5 0.5×	7 0.8×	1 0.2×	9	72
F. Ratnikov Russia	6	61 4.4×	18 1.5×	2 0.2×	4 0.4×	5 1.0×	28	90
A. Tsyganov Russia	4	7 0.5×	25 2.1×	6 0.6×	9 1.0×	1 0.2×	28	50
Alexander Spiridonov Russia	4	37 2.6×	6 0.5×	3 0.3×	2 0.2×	7 1.4×	10	61
Kushal Tirumala Israel	3	7 0.5×	12 1.0×	4 0.4×	27 3.0×	4 0.8×	3	46
N. V. Tran United States	2	23 1.6×	22 1.8×	5 0.5×	1 0.1×	10 2.0×	4	55
P. Harris United States	2	23 1.6×	24 2.0×	5 0.5×	1 0.1×	11 2.2×	2	57
C. Biscarat France	4	24 1.7×	15 1.3×		3 0.3×	3 0.6×	6	42

Countries citing papers authored by M. Hushchyn

Since Specialization

Citations

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

Fields of papers citing papers by M. Hushchyn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Hushchyn

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

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

Ryzhikov, A., et al.. (2025). Performance Modeling of Data Storage Systems Using Generative Models. IEEE Access. 13. 49643–49658.

Malanchev, Konstantin, et al.. (2023). Supernova Light Curves Approximation based on Neural Network Models. Journal of Physics Conference Series. 2438(1). 12128–12128. 3 indexed citations

Ryzhikov, A., et al.. (2023). Robust Neural Particle Identification Models. Journal of Physics Conference Series. 2438(1). 12119–12119.

Malanchev, Konstantin, et al.. (2023). Understanding of the properties of neural network approaches for transient light curve approximations. Astronomy and Astrophysics. 677. A16–A16. 3 indexed citations

Ryzhikov, A., et al.. (2023). Latent Stochastic Differential Equations for Change Point Detection. IEEE Access. 11. 104700–104711. 3 indexed citations

Hushchyn, M., et al.. (2023). Stokes Inversion Techniques with Neural Networks: Analysis of Uncertainty in Parameter Estimation. Solar Physics. 298(8). 4 indexed citations

Hushchyn, M. & A. Ustyuzhanin. (2020). Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation. arXiv (Cornell University). 16 indexed citations

Деркач, Д., M. Hushchyn, & N. Kazeev. (2019). Machine Learning based Global Particle Identification Algorithms at the LHCb Experiment. SHILAP Revista de lepidopterología. 214. 6011–6011. 4 indexed citations

Kiehn, M., P. Calafiura, Steven Farrell, et al.. (2019). The TrackML high-energy physics tracking challenge on Kaggle. SHILAP Revista de lepidopterología. 214. 6037–6037. 4 indexed citations

10.

Hushchyn, M., A. Sapronov, & A. Ustyuzhanin. (2019). Machine Learning Algorithms for Automatic Anomalies Detection in Data Storage Systems Operation. Russian Agency for Digital Standardization. 19(2). 23–32. 2 indexed citations

11.

Деркач, Д., M. Hushchyn, Tatiana Likhomanenko, et al.. (2018). Machine-Learning-based global particle-identification algorithms at the LHCb experiment. Journal of Physics Conference Series. 1085. 42038–42038. 3 indexed citations

12.

Rousseau, D., P. Calafiura, Steven Farrell, et al.. (2018). The TrackML challenge. SPIRE - Sciences Po Institutional REpository. 1–23. 2 indexed citations

13.

Hushchyn, M. & V. Chekalina. (2018). Particle-identification techniques and performance at LHCb in Run 2. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 936. 568–569. 1 indexed citations

14.

Hushchyn, M., A. Ustyuzhanin, Kenenbek Arzymatov, S. Roiser, & A. Baranov. (2017). The LHCb Grid Simulation: Proof of Concept. Journal of Physics Conference Series. 898. 52020–52020. 1 indexed citations

15.

Braun, N., P. Calafiura, Steven Farrell, et al.. (2017). Track reconstruction at LHC as a collaborative data challenge use case with RAMP. SHILAP Revista de lepidopterología. 150. 15–15. 4 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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