Leonid Zhukov

1.9k total citations
50 papers, 1.0k citations indexed

About

Leonid Zhukov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Leonid Zhukov has authored 50 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 12 papers in Computer Vision and Pattern Recognition and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Leonid Zhukov's work include Advanced MRI Techniques and Applications (7 papers), Blind Source Separation Techniques (6 papers) and Advanced Neuroimaging Techniques and Applications (6 papers). Leonid Zhukov is often cited by papers focused on Advanced MRI Techniques and Applications (7 papers), Blind Source Separation Techniques (6 papers) and Advanced Neuroimaging Techniques and Applications (6 papers). Leonid Zhukov collaborates with scholars based in United States, Russia and United Kingdom. Leonid Zhukov's co-authors include Alan H. Barr, David M. Weinstein, David F. Gleich, Chris R. Johnson, Ilya Makarov, Pavel Berkhin, David E. Breen, M. É. Raǐkh, Christopher R. Johnson and Ken Museth and has published in prestigious journals such as Physical Review Letters, Physical review. B, Condensed matter and NeuroImage.

In The Last Decade

Leonid Zhukov

47 papers receiving 983 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Leonid Zhukov United States 18 277 193 185 161 145 50 1.0k
Mukund Balasubramanian United States 12 210 0.8× 260 1.3× 222 1.2× 284 1.8× 76 0.5× 27 951
Pablo Padilla Spain 25 106 0.4× 144 0.7× 221 1.2× 239 1.5× 73 0.5× 174 2.1k
Lei Yuan China 18 106 0.4× 90 0.5× 494 2.7× 336 2.1× 70 0.5× 79 1.6k
Parastoo Sadeghi Australia 24 108 0.4× 91 0.5× 182 1.0× 279 1.7× 194 1.3× 185 2.4k
Saeed Setayeshi Iran 22 141 0.5× 73 0.4× 364 2.0× 200 1.2× 115 0.8× 151 1.9k
Amardeep Singh India 20 117 0.4× 211 1.1× 378 2.0× 226 1.4× 290 2.0× 63 1.6k
Miguel Figueroa Chile 18 140 0.5× 97 0.5× 299 1.6× 164 1.0× 56 0.4× 92 1.3k
Lin Yuan China 17 79 0.3× 96 0.5× 178 1.0× 705 4.4× 105 0.7× 82 1.1k
Xiaoxia Yin Australia 19 388 1.4× 69 0.4× 247 1.3× 170 1.1× 57 0.4× 80 1.2k

Countries citing papers authored by Leonid Zhukov

Since Specialization
Citations

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

Fields of papers citing papers by Leonid Zhukov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonid Zhukov

This figure shows the co-authorship network connecting the top 25 collaborators of Leonid Zhukov. A scholar is included among the top collaborators of Leonid Zhukov 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 Leonid Zhukov. Leonid Zhukov 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.
Mukherjee, S., et al.. (2025). Foundation models in drug discovery: Phenomenal growth today, transformative potential tomorrow?. Drug Discovery Today. 30(12). 104518–104518.
2.
Zhukov, Leonid, et al.. (2023). SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes. Artificial Intelligence. 324. 104012–104012. 8 indexed citations
3.
Теруков, Е. И., et al.. (2023). Anomaly detection in electroluminescence images of heterojunction solar cells. Solar Energy. 259. 130–136. 28 indexed citations
4.
Budennyy, Semen, et al.. (2023). New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements. Scientific Reports. 13(1). 12817–12817. 1 indexed citations
5.
Budennyy, Semen, et al.. (2022). eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI. Doklady Mathematics. 106(S1). S118–S128. 80 indexed citations
6.
Eremin, Roman A., et al.. (2022). Hybrid DFT/Data-Driven Approach for Searching for New Quasicrystal Approximants in Sc-X (X = Rh, Pd, Ir, Pt) Systems. Crystal Growth & Design. 22(7). 4570–4581. 7 indexed citations
7.
Gleich, David F. & Leonid Zhukov. (2005). Scalable Computing for Power Law Graphs: Experience with Parallel PageRank. 10 indexed citations
8.
Gleich, David F., et al.. (2004). SVD Subspace Projections for Term Suggestion Ranking and Clustering. 16(384). 1–8. 5 indexed citations
9.
Uitert, Robert Van, Christopher R. Johnson, & Leonid Zhukov. (2004). Influence of Head Tissue Conductivity in Forward and Inverse Magnetoencephalographic Simulations Using Realistic Head Models. IEEE Transactions on Biomedical Engineering. 51(12). 2129–2137. 30 indexed citations
10.
Zhukov, Leonid, et al.. (2003). Level Set Modeling and Segmentation of DT-MRI Brain Data. 7 indexed citations
11.
Zhukov, Leonid. (2003). Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data. Journal of Electronic Imaging. 12(1). 125–125. 61 indexed citations
12.
Guskov, Igor & Leonid Zhukov. (2002). Direct pattern tracking on flexible geometry. Digital Library (University of West Bohemia). 203–208. 12 indexed citations
13.
Zhukov, Leonid & Alan H. Barr. (2002). Oriented tensor reconstruction: tracing neural pathways from diffusion tensor MRI. IEEE Visualization. 387–394. 58 indexed citations
14.
Museth, Ken, David E. Breen, Leonid Zhukov, & Ross Whitaker. (2002). Level set segmentation from multiple non-uniform volume datasets. IEEE Visualization. 179–186. 12 indexed citations
15.
Johnson, Christopher R., Martin Berzins, & Leonid Zhukov. (2001). SCIRun: Application to Atmospheric Dispersion Problems Using Unstructured Meshes. Journal of Materials Science Materials in Medicine. 19(7). 2535–40. 1 indexed citations
16.
Zhukov, Leonid, Ken Museth, David E. Breen, & Ross Whitaker. (2001). <title>3D modeling and segmentation of diffusion weighted MRI data</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4319. 401–412. 1 indexed citations
17.
Zhukov, Leonid, David M. Weinstein, & Chris R. Johnson. (2000). Reciprocity basis for EEG source imaging. NeuroImage. 11(5). S598–S598.
18.
Weinstein, David M., et al.. (2000). Lead-field Bases for Electroencephalography Source Imaging. Annals of Biomedical Engineering. 28(9). 1059–1065. 92 indexed citations
19.
Zhukov, Leonid, David M. Weinstein, & Chris R. Johnson. (2000). Statistical Analysis For FEM EEG Source Localization in Realistic Head Models. 1 indexed citations
20.
Барабанов, А. Ф., L. A. Maksimov, & Leonid Zhukov. (1993). Hole spectrum of adjacent CuO2 planes in the two-band model and the non-monotonic Tc dependence on the external parameters. Physica C Superconductivity. 212(3-4). 375–380. 3 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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