Daniel Burbano

554 total citations
30 papers, 411 citations indexed

About

Daniel Burbano is a scholar working on Computer Networks and Communications, Statistical and Nonlinear Physics and Cell Biology. According to data from OpenAlex, Daniel Burbano has authored 30 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Networks and Communications, 6 papers in Statistical and Nonlinear Physics and 6 papers in Cell Biology. Recurrent topics in Daniel Burbano's work include Nonlinear Dynamics and Pattern Formation (11 papers), Neural Networks Stability and Synchronization (11 papers) and Distributed Control Multi-Agent Systems (10 papers). Daniel Burbano is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (11 papers), Neural Networks Stability and Synchronization (11 papers) and Distributed Control Multi-Agent Systems (10 papers). Daniel Burbano collaborates with scholars based in United States, Italy and United Kingdom. Daniel Burbano's co-authors include Mario di Bernardo, Maurizio Porfiri, Fabiola Angulo, Giovanni Russo, Randy A. Freeman, Kevin Lynch, Carlo Petrarca, M. de Magistris, Richard D. Braatz and S. H. Chung and has published in prestigious journals such as Automatica, IEEE Transactions on Power Electronics and IEEE Access.

In The Last Decade

Daniel Burbano

28 papers receiving 405 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Burbano United States 10 255 105 83 76 24 30 411
Piotr Kowalczyk United Kingdom 3 95 0.4× 67 0.6× 141 1.7× 20 0.3× 5 0.2× 6 321
Zigen Song China 17 330 1.3× 32 0.3× 400 4.8× 37 0.5× 55 2.3× 43 624
Jonatán Peña Ramírez Mexico 10 241 0.9× 50 0.5× 181 2.2× 34 0.4× 24 1.0× 42 344
Dongxi Li China 11 80 0.3× 17 0.2× 203 2.4× 24 0.3× 12 0.5× 47 404
Asja Jelić Italy 7 85 0.3× 103 1.0× 70 0.8× 2 0.0× 17 0.7× 10 419
Yara Khaluf Belgium 10 92 0.4× 21 0.2× 49 0.6× 6 0.1× 56 2.3× 37 278
Sergey Shchanikov Russia 9 57 0.2× 41 0.4× 80 1.0× 355 4.7× 79 3.3× 37 470
Zahra Aminzare United States 7 66 0.3× 99 0.9× 40 0.5× 18 0.2× 7 0.3× 15 222
Xiaokang Lei China 10 176 0.7× 34 0.3× 24 0.3× 12 0.2× 32 1.3× 52 309
Takayuki Niizato Japan 11 56 0.2× 5 0.0× 65 0.8× 8 0.1× 36 1.5× 35 335

Countries citing papers authored by Daniel Burbano

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Burbano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Burbano

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Burbano. A scholar is included among the top collaborators of Daniel Burbano 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 Daniel Burbano. Daniel Burbano 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.
Burbano, Daniel, et al.. (2024). Learning to hunt: A data-driven stochastic feedback control model of predator–prey interactions. Journal of Theoretical Biology. 599. 112021–112021. 1 indexed citations
2.
Burbano, Daniel, et al.. (2023). Exploring emotional contagion in zebrafish: A virtual-demonstrator study of positive and negative emotions. Behavioural Processes. 213. 104961–104961. 4 indexed citations
3.
Burbano, Daniel, Lorenzo Zino, Sachit Butail, et al.. (2022). Activity-driven network modeling and control of the spread of two concurrent epidemic strains. Applied Network Science. 7(1). 66–66. 3 indexed citations
4.
Burbano, Daniel & Maurizio Porfiri. (2022). Collective response of fish to combined manipulations of illumination and flow. Behavioural Processes. 203. 104767–104767. 13 indexed citations
5.
Burbano, Daniel, et al.. (2022). Educating Youth About Human Impact on Freshwater Ecosystems Using an Online Serious Game. IEEE Transactions on Games. 15(4). 590–602. 4 indexed citations
6.
Burbano, Daniel & Maurizio Porfiri. (2021). Modeling multi-sensory feedback control of zebrafish in a flow. PLoS Computational Biology. 17(1). e1008644–e1008644. 9 indexed citations
7.
Burbano, Daniel, Simone Macrı̀, & Maurizio Porfiri. (2021). Collective Emotional Contagion in Zebrafish. Frontiers in Behavioral Neuroscience. 15. 730372–730372. 9 indexed citations
8.
Burbano, Daniel & Maurizio Porfiri. (2021). Modeling zebrafish geotaxis as a feedback control process. PubMed. 2021. 660–665. 5 indexed citations
9.
Zino, Lorenzo, et al.. (2020). Leader–follower consensus on activity-driven networks. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 476(2233). 20190485–20190485. 9 indexed citations
10.
Burbano, Daniel, et al.. (2020). Empirical Evidence of Upward Social Comparison in a Prisoner’s Dilemma Game. IEEE Access. 8. 52884–52894. 1 indexed citations
11.
Burbano, Daniel, Randy A. Freeman, & Kevin Lynch. (2019). Discovering the topology of complex networks via adaptive estimators. Chaos An Interdisciplinary Journal of Nonlinear Science. 29(8). 83121–83121. 2 indexed citations
12.
Burbano, Daniel, Randy A. Freeman, & Kevin Lynch. (2019). A Distributed Adaptive Observer for Leader-Follower Networks. 2722–2727. 3 indexed citations
13.
Burbano, Daniel & Maurizio Porfiri. (2019). Data-driven modeling of zebrafish behavioral response to acute caffeine administration. Journal of Theoretical Biology. 485. 110054–110054. 12 indexed citations
14.
Burbano, Daniel, Randy A. Freeman, & Kevin Lynch. (2019). Distributed Inference of the Multiplex Network Topology of Complex Systems. IEEE Transactions on Control of Network Systems. 7(1). 278–287. 9 indexed citations
15.
Burbano, Daniel, et al.. (2019). Synchronization in Multiplex Networks of Chua’s Circuits: Theory and Experiments. IEEE Transactions on Circuits and Systems I Regular Papers. 67(3). 927–938. 31 indexed citations
16.
Burbano, Daniel, Giovanni Russo, & Mario di Bernardo. (2018). Pinning Controllability of Complex Network Systems With Noise. IEEE Transactions on Control of Network Systems. 6(2). 874–883. 29 indexed citations
17.
Burbano, Daniel, Giovanni Russo, & Mario di Bernardo. (2017). Pinning Controllability of Complex Stochastic Networks. IFAC-PapersOnLine. 50(1). 8327–8332. 10 indexed citations
18.
Burbano, Daniel & Mario di Bernardo. (2015). Multilayer proportional-integral consensus of heterogeneous multi-agent systems. Bristol Research (University of Bristol). 1. 4854–4859. 4 indexed citations
19.
Hoyos, Fredy E., et al.. (2012). EFFECTS OF QUANTIZATION, DELAY AND INTERNAL RESISTANCES IN DIGITALLY ZAD-CONTROLLED BUCK CONVERTER. International Journal of Bifurcation and Chaos. 22(10). 1250245–1250245. 14 indexed citations
20.
Burbano, Daniel & Fabiola Angulo. (2011). Decreasing quantization effects in a buck converter controlled by GZAD strategy. 78. 1–6.

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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