Vlado Menkovski

1.3k total citations
62 papers, 507 citations indexed

About

Vlado Menkovski is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Vlado Menkovski has authored 62 papers receiving a total of 507 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 23 papers in Artificial Intelligence and 13 papers in Signal Processing. Recurrent topics in Vlado Menkovski's work include Image and Video Quality Assessment (20 papers), Video Coding and Compression Technologies (9 papers) and Video Analysis and Summarization (8 papers). Vlado Menkovski is often cited by papers focused on Image and Video Quality Assessment (20 papers), Video Coding and Compression Technologies (9 papers) and Video Analysis and Summarization (8 papers). Vlado Menkovski collaborates with scholars based in Netherlands, Germany and Switzerland. Vlado Menkovski's co-authors include Antonio Liotta, Georgios Exarchakos, Mykola Pechenizkiy, Decebal Constantin Mocanu, Alessandro Corbetta, Federico Toschi, Roberto Benzi, Ivan Wang‐Hei Ho, Ioannis T. Christou and Freek Bos and has published in prestigious journals such as Cancer Research, Scientific Reports and ACS Applied Materials & Interfaces.

In The Last Decade

Vlado Menkovski

55 papers receiving 483 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vlado Menkovski Netherlands 14 278 121 102 101 91 62 507
Dimitris Tsolkas Greece 13 151 0.5× 439 3.6× 30 0.3× 51 0.5× 29 0.3× 54 647
Yong Feng China 13 199 0.7× 75 0.6× 177 1.7× 31 0.3× 35 0.4× 70 548
He Yan United States 12 137 0.5× 414 3.4× 217 2.1× 34 0.3× 38 0.4× 40 556
Chao Huang China 14 360 1.3× 199 1.6× 422 4.1× 18 0.2× 99 1.1× 55 727
Mingming Sun China 15 388 1.4× 71 0.6× 373 3.7× 40 0.4× 44 0.5× 67 787
David J. Parish United Kingdom 13 197 0.7× 334 2.8× 195 1.9× 15 0.1× 147 1.6× 84 623
Miao Hu China 14 257 0.9× 281 2.3× 168 1.6× 58 0.6× 120 1.3× 66 679
Maofu Liu China 16 380 1.4× 14 0.1× 358 3.5× 30 0.3× 38 0.4× 59 839

Countries citing papers authored by Vlado Menkovski

Since Specialization
Citations

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

Fields of papers citing papers by Vlado Menkovski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vlado Menkovski

This figure shows the co-authorship network connecting the top 25 collaborators of Vlado Menkovski. A scholar is included among the top collaborators of Vlado Menkovski 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 Vlado Menkovski. Vlado Menkovski 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.
Menkovski, Vlado, et al.. (2025). MEAN: Mixture-of-Experts Neural Receiver - Architecture and Performance Analysis. ACM Transactions on Embedded Computing Systems.
2.
Vicent‐Luna, José Manuel, et al.. (2025). Symmetry-informed graph neural networks for carbon dioxide isotherm and adsorption prediction in aluminum-substituted zeolites. Journal of Materials Chemistry A. 13(43). 37018–37030.
3.
4.
Menkovski, Vlado, et al.. (2025). Similarity equivariant graph neural networks for homogenization of metamaterials. Computer Methods in Applied Mechanics and Engineering. 439. 117867–117867. 2 indexed citations
5.
Corbetta, Alessandro, et al.. (2025). Discovering interaction mechanisms in crowds via deep generative surrogate experiments. Scientific Reports. 15(1). 10385–10385. 2 indexed citations
6.
Pau, A., et al.. (2025). Plasma state monitoring and disruption characterization using multimodal VAEs. Nuclear Fusion. 65(9). 96012–96012. 1 indexed citations
7.
Menkovski, Vlado, et al.. (2025). Wallpaper Group-Based Mechanical Metamaterials: Dataset Including Mechanical Responses. Scientific Data. 12(1). 1880–1880. 1 indexed citations
8.
Järvinen, A., et al.. (2024). On learning latent dynamics of the AUG plasma state. Physics of Plasmas. 31(3). 3 indexed citations
9.
Vicent‐Luna, José Manuel, et al.. (2024). Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminum-Substituted Zeolites. ACS Applied Materials & Interfaces. 16(41). 56366–56375. 5 indexed citations
10.
Järvinen, A., et al.. (2024). Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG. Physics of Plasmas. 31(3). 2 indexed citations
11.
Wiesen, S., A.E. Jaervinen, A. Ho, et al.. (2024). Data-driven models in fusion exhaust: AI methods and perspectives. Nuclear Fusion. 64(8). 86046–86046. 8 indexed citations
12.
Westerhof, E., et al.. (2023). Fast dynamic 1D simulation of divertor plasmas with neural PDE surrogates. Nuclear Fusion. 63(12). 126012–126012. 12 indexed citations
13.
Fang, Meng, et al.. (2023). NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist. TU/e Research Portal. 1240–1266. 4 indexed citations
14.
Yin, Lu, et al.. (2023). Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 10945–10953. 2 indexed citations
15.
Crommelin, Daan, et al.. (2023). Comparison of neural closure models for discretised PDEs. Computers & Mathematics with Applications. 143. 94–107. 12 indexed citations
16.
Menkovski, Vlado, et al.. (2022). Direction-aggregated Attack for Transferable Adversarial Examples. ACM Journal on Emerging Technologies in Computing Systems. 18(3). 1–22. 10 indexed citations
17.
Menkovski, Vlado, et al.. (2021). Efficient and effective training of sparse recurrent neural networks. Neural Computing and Applications. 33(15). 9625–9636. 22 indexed citations
18.
Menkovski, Vlado, et al.. (2021). Plasma confinement mode classification using a sequence-to-sequence neural network with attention. Nuclear Fusion. 61(4). 46019–46019. 8 indexed citations
19.
Menkovski, Vlado, et al.. (2011). A quality of experience management module. TU/e Research Portal. 4. 13–19. 4 indexed citations
20.
Menkovski, Vlado, Georgios Exarchakos, & Antonio Liotta. (2011). The value of relative quality in video delivery. Journal of Multimedia. 7(3). 151–162. 27 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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