Yuriy Mishchenko

981 total citations
26 papers, 653 citations indexed

About

Yuriy Mishchenko is a scholar working on Cognitive Neuroscience, Nuclear and High Energy Physics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Yuriy Mishchenko has authored 26 papers receiving a total of 653 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cognitive Neuroscience, 8 papers in Nuclear and High Energy Physics and 6 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Yuriy Mishchenko's work include Neural dynamics and brain function (5 papers), Particle physics theoretical and experimental studies (5 papers) and EEG and Brain-Computer Interfaces (5 papers). Yuriy Mishchenko is often cited by papers focused on Neural dynamics and brain function (5 papers), Particle physics theoretical and experimental studies (5 papers) and EEG and Brain-Computer Interfaces (5 papers). Yuriy Mishchenko collaborates with scholars based in United States, Türkiye and Germany. Yuriy Mishchenko's co-authors include Chueng‐Ryong Ji, Hilmi Yanar, Çiğdem İnan Acı, Liam Paninski, Joshua T Vogelstein, Liam Paninski, Anatoly Radyushkin, Antonio Capolupo, Giuseppe Vitiello and B. L. G. Bakker and has published in prestigious journals such as Physics Letters B, Expert Systems with Applications and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Yuriy Mishchenko

24 papers receiving 633 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuriy Mishchenko United States 14 291 176 131 73 70 26 653
Subhaneil Lahiri United States 7 182 0.6× 74 0.4× 159 1.2× 52 0.7× 47 0.7× 7 386
Fernando Montani Argentina 17 445 1.5× 36 0.2× 173 1.3× 68 0.9× 98 1.4× 42 598
Y. Uchikawa Japan 13 245 0.8× 19 0.1× 39 0.3× 117 1.6× 43 0.6× 140 558
Dirk Seidel United Kingdom 18 237 0.8× 267 1.5× 19 0.1× 78 1.1× 53 0.8× 62 1.1k
Louis Tao China 16 480 1.6× 17 0.1× 224 1.7× 137 1.9× 65 0.9× 66 876
Nikita Frolov Russia 21 823 2.8× 21 0.1× 81 0.6× 172 2.4× 117 1.7× 90 1.3k
Marcus K. Benna United States 10 349 1.2× 422 2.4× 133 1.0× 68 0.9× 77 1.1× 18 876
Douglas G. Kelly United States 11 210 0.7× 16 0.1× 49 0.4× 88 1.2× 173 2.5× 22 857
K. Kobayashi Japan 13 354 1.2× 15 0.1× 68 0.5× 89 1.2× 46 0.7× 93 654
D. Badoni Italy 14 337 1.2× 143 0.8× 283 2.2× 453 6.2× 131 1.9× 42 763

Countries citing papers authored by Yuriy Mishchenko

Since Specialization
Citations

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

Fields of papers citing papers by Yuriy Mishchenko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuriy Mishchenko

This figure shows the co-authorship network connecting the top 25 collaborators of Yuriy Mishchenko. A scholar is included among the top collaborators of Yuriy Mishchenko 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 Yuriy Mishchenko. Yuriy Mishchenko 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.
Ji, Chueng‐Ryong & Yuriy Mishchenko. (2021). General Quantum Field Theory of Flavor Mixing and Oscillations. Universe. 7(3). 51–51. 2 indexed citations
2.
Werchniak, Andrew E., et al.. (2021). Exploring the application of synthetic audio in training keyword spotters. 7993–7996. 8 indexed citations
3.
Liu, Hongyi, Yuriy Mishchenko, Thibaud Sénéchal, et al.. (2020). Metadata-Aware End-to-End Keyword Spotting. OpenBU (Boston University). 3 indexed citations
4.
Mishchenko, Yuriy, et al.. (2019). Low-Bit Quantization and Quantization-Aware Training for Small-Footprint Keyword Spotting. 706–711. 16 indexed citations
5.
Acı, Çiğdem İnan, et al.. (2019). Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods. Expert Systems with Applications. 134. 153–166. 78 indexed citations
6.
Mishchenko, Yuriy, et al.. (2018). Developing a Three- to Six-State EEG-Based Brain–Computer Interface for a Virtual Robotic Manipulator Control. IEEE Transactions on Biomedical Engineering. 66(4). 977–987. 28 indexed citations
7.
Yanar, Hilmi, et al.. (2018). A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces. Scientific Data. 5(1). 180211–180211. 119 indexed citations
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10.
Mishchenko, Yuriy. (2013). A fast algorithm for computation of discrete Euclidean distance transform in three or more dimensions on vector processing architectures. Signal Image and Video Processing. 9(1). 19–27. 32 indexed citations
11.
Mishchenko, Yuriy & Liam Paninski. (2012). A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data. Journal of Computational Neuroscience. 33(2). 371–388. 17 indexed citations
12.
Mishchenko, Yuriy. (2011). Reconstruction of complete connectivity matrix for connectomics by sampling neural connectivity with fluorescent synaptic markers. Journal of Neuroscience Methods. 196(2). 289–302. 5 indexed citations
13.
Mishchenko, Yuriy & Liam Paninski. (2011). Efficient methods for sampling spike trains in networks of coupled neurons. The Annals of Applied Statistics. 5(3). 5 indexed citations
14.
Mishchenko, Yuriy, Joshua T Vogelstein, & Liam Paninski. (2011). A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data. The Annals of Applied Statistics. 5(2B). 64 indexed citations
15.
Mishchenko, Yuriy. (2008). Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs. Journal of Neuroscience Methods. 176(2). 276–289. 66 indexed citations
16.
Mishchenko, Yuriy. (2006). Remedy for the fermion sign problem in the diffusion Monte Carlo method for few fermions with antisymmetric diffusion process. Physical Review E. 73(2). 26706–26706. 3 indexed citations
17.
Ji, Chueng‐Ryong, Yuriy Mishchenko, & Anatoly Radyushkin. (2006). Higher Fock-state contributions to the generalized parton distribution of pion. Physical review. D. Particles, fields, gravitation, and cosmology. 73(11). 26 indexed citations
18.
Mishchenko, Yuriy & Chueng‐Ryong Ji. (2005). A NOVEL VARIATIONAL APPROACH FOR QUANTUM FIELD THEORY: EXAMPLE OF STUDY OF THE GROUND STATE AND PHASE TRANSITION IN NONLINEAR SIGMA MODEL. International Journal of Modern Physics A. 20(15). 3488–3494. 2 indexed citations
19.
Ji, Chueng‐Ryong & Yuriy Mishchenko. (2002). General theory of quantum field mixing. Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields. 65(9). 56 indexed citations
20.
Ji, Chueng‐Ryong & Yuriy Mishchenko. (2001). Nonperturbative vacuum effect in the quantum field theory of meson mixing. Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields. 64(7). 31 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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