Milan Paluš

6.2k total citations · 1 hit paper
95 papers, 4.4k citations indexed

About

Milan Paluš is a scholar working on Economics and Econometrics, Cognitive Neuroscience and Statistical and Nonlinear Physics. According to data from OpenAlex, Milan Paluš has authored 95 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Economics and Econometrics, 36 papers in Cognitive Neuroscience and 35 papers in Statistical and Nonlinear Physics. Recurrent topics in Milan Paluš's work include Complex Systems and Time Series Analysis (38 papers), Neural dynamics and brain function (33 papers) and Chaos control and synchronization (24 papers). Milan Paluš is often cited by papers focused on Complex Systems and Time Series Analysis (38 papers), Neural dynamics and brain function (33 papers) and Chaos control and synchronization (24 papers). Milan Paluš collaborates with scholars based in Czechia, United States and United Kingdom. Milan Paluš's co-authors include Martin Vejmelka, Joydeep Bhattacharya, Kateřina Hlaváčková‐Schindler, Dagmar Novotná, Jaroslav Hlinka, Aneta Stefanovska, David Hartman, Vladimı́r Komárek, Z Hrnčíř and Ivan Dvořák and has published in prestigious journals such as Physical Review Letters, Nature Communications and Journal of Neuroscience.

In The Last Decade

Milan Paluš

91 papers receiving 4.3k citations

Hit Papers

Causality detection based on information-theoretic approa... 2007 2026 2013 2019 2007 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Milan Paluš Czechia 35 1.8k 1.1k 1.1k 740 661 95 4.4k
Jianbo Gao United States 35 887 0.5× 1.7k 1.5× 1.4k 1.3× 449 0.6× 757 1.1× 127 4.3k
M. Carmen Romano United Kingdom 27 1.0k 0.6× 1.4k 1.2× 1.0k 0.9× 282 0.4× 934 1.4× 67 4.8k
Marko Thiel Germany 16 870 0.5× 1.1k 0.9× 833 0.8× 247 0.3× 726 1.1× 40 3.6k
Andrew M. Fraser United States 13 765 0.4× 1.7k 1.5× 1.2k 1.1× 273 0.4× 804 1.2× 22 4.6k
Timothy Sauer United States 38 1.4k 0.8× 3.5k 3.1× 1.3k 1.2× 599 0.8× 1.7k 2.6× 113 7.6k
Wen‐wen Tung United States 26 478 0.3× 836 0.7× 727 0.7× 790 1.1× 259 0.4× 59 2.8k
Reik V. Donner Germany 37 524 0.3× 1.4k 1.3× 1.7k 1.6× 1.4k 1.9× 671 1.0× 129 5.0k
B. Galdrikian United States 10 1.1k 0.6× 1.1k 0.9× 1.1k 1.0× 203 0.3× 386 0.6× 15 3.3k
Martin Casdagli United States 14 728 0.4× 1.9k 1.7× 1.3k 1.2× 325 0.4× 763 1.2× 16 4.1k
Matthew B. Kennel United States 18 714 0.4× 1.7k 1.5× 1.1k 1.0× 199 0.3× 716 1.1× 29 3.9k

Countries citing papers authored by Milan Paluš

Since Specialization
Citations

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

Fields of papers citing papers by Milan Paluš

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Milan Paluš

This figure shows the co-authorship network connecting the top 25 collaborators of Milan Paluš. A scholar is included among the top collaborators of Milan Paluš 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 Milan Paluš. Milan Paluš 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.
Manshour, Pouya, et al.. (2026). Identifying the net information flow direction in mutually coupled non-identical chaotic oscillators. Chaos An Interdisciplinary Journal of Nonlinear Science. 36(2).
2.
Vlachos, Ioannis, Dimitris Kugiumtzis, & Milan Paluš. (2024). Causality from phases of high-dimensional nonlinear systems. Information Sciences. 697. 121761–121761.
3.
Latif, Yasir, et al.. (2024). Cross-scale causal information flow from the El Niño–Southern Oscillation to precipitation in eastern China. Earth System Dynamics. 15(6). 1509–1526. 1 indexed citations
4.
Sahimi, Muhammad, Pouya Manshour, Milan Paluš, et al.. (2024). Characterizing time-resolved stochasticity in non-stationary time series. Chaos Solitons & Fractals. 185. 115069–115069. 4 indexed citations
5.
Manshour, Pouya, Constantinos Papadimitriou, Georgios Balasis, & Milan Paluš. (2024). Causal Inference in the Outer Radiation Belt: Evidence for Local Acceleration. Geophysical Research Letters. 51(15). 4 indexed citations
6.
López‐Madrona, Víctor J., et al.. (2024). Lead/Lag directionality is not generally equivalent to causality in nonlinear systems: Comparison of phase slope index and conditional mutual information. NeuroImage. 292. 120610–120610. 3 indexed citations
7.
Manshour, Pouya, et al.. (2022). Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series. Scientific Reports. 12(1). 14170–14170. 9 indexed citations
8.
Manshour, Pouya, Georgios Balasis, Giuseppe Consolini, Constantinos Papadimitriou, & Milan Paluš. (2021). Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System. Entropy. 23(4). 390–390. 27 indexed citations
9.
Hlinka, Jaroslav, David Hartman, Martin Vejmelka, & Milan Paluš. (2012). Non-linear contributions to interactions in climate networks: sources, relevance, nonstationarity. ASEP. 14274.
10.
Paluš, Milan, et al.. (2012). Comparison of coherence and phase synchronization of the human sleep electroencephalogram. Clinical Neurophysiology. 123(9). 1821–1830. 25 indexed citations
11.
Hlinka, Jaroslav, Milan Paluš, Martin Vejmelka, Dante Mantini, & Maurizio Corbetta. (2010). Functional connectivity in resting-state fMRI: Is linear correlation sufficient?. NeuroImage. 54(3). 2218–2225. 141 indexed citations
13.
Teplan, Michal, et al.. (2009). Phase Synchronization in Human EEG During Audio-Visual Stimulation. Electromagnetic Biology and Medicine. 28(1). 80–84. 2 indexed citations
14.
Vejmelka, Martin & Milan Paluš. (2009). Detecting nonlinear oscillations in broadband signals. Chaos An Interdisciplinary Journal of Nonlinear Science. 19(1). 15114–15114. 4 indexed citations
15.
Bob, Petr, et al.. (2008). EEG phase synchronization in patients with paranoid schizophrenia. Neuroscience Letters. 447(1). 73–77. 59 indexed citations
16.
Paluš, Milan, et al.. (2001). Synchronization and information flow in EEGs of epileptic patients. IEEE Engineering in Medicine and Biology Magazine. 20(5). 65–71. 44 indexed citations
17.
Greig, Alison, Gavin C. Cawley, Kryštof Eben, et al.. (2000). Air Pollution Episodes: Modelling Tools For Improved Smog Management(APPETISE). WIT Transactions on Ecology and the Environment. 42. 89–98. 6 indexed citations
18.
Hoyer, Dirk, Daniel T. Kaplan, Milan Paluš, Bernd Pompe, & Henrik Seidel. (1998). New systems-analytical approaches to nonlinear coordination. IEEE Engineering in Medicine and Biology Magazine. 17(6). 58–61. 7 indexed citations
19.
Paluš, Milan. (1996). Nonlinearity in normal human EEG: cycles, temporal asymmetry, nonstationarity and randomness, not chaos. Biological Cybernetics. 75(5). 389–396. 150 indexed citations
20.
Matouŝek, M, et al.. (1995). Global Dimensional Complexity of the EEG in Healthy Volunteers. Neuropsychobiology. 31(1). 47–52. 20 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026