Dan Wilson

2.1k total citations
92 papers, 1.5k citations indexed

About

Dan Wilson is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Cognitive Neuroscience. According to data from OpenAlex, Dan Wilson has authored 92 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Statistical and Nonlinear Physics, 22 papers in Computer Networks and Communications and 21 papers in Cognitive Neuroscience. Recurrent topics in Dan Wilson's work include Nonlinear Dynamics and Pattern Formation (22 papers), Neural dynamics and brain function (20 papers) and Model Reduction and Neural Networks (17 papers). Dan Wilson is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (22 papers), Neural dynamics and brain function (20 papers) and Model Reduction and Neural Networks (17 papers). Dan Wilson collaborates with scholars based in United States, United Kingdom and Ghana. Dan Wilson's co-authors include Jeff Moehlis, Bard Ermentrout, Théoden I. Netoff, Abbey B. Holt, Youngmin Park, Maxwell Shinn, Fidelis C.K. Ocloo, Stanley J. Kays, Ray F. Severson and Jim McKenna and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Dan Wilson

83 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dan Wilson United States 22 406 393 356 277 183 92 1.5k
David Paydarfar United States 23 136 0.3× 109 0.3× 397 1.1× 233 0.8× 85 0.5× 86 1.8k
Enrico Amico United States 24 367 0.9× 118 0.3× 1.4k 4.0× 283 1.0× 151 0.8× 62 3.1k
James W. Sleigh New Zealand 35 199 0.5× 67 0.2× 1.3k 3.7× 562 2.0× 273 1.5× 105 3.6k
Hans Braun Germany 30 1.4k 3.5× 940 2.4× 1.4k 4.0× 471 1.7× 12 0.1× 121 3.4k
Shirin Panahi Iran 27 636 1.6× 402 1.0× 278 0.8× 51 0.2× 14 0.1× 79 2.2k
Shahriar Gharibzadeh Iran 18 119 0.3× 35 0.1× 317 0.9× 144 0.5× 127 0.7× 145 1.1k
M. Moser Austria 30 32 0.1× 93 0.2× 283 0.8× 45 0.2× 33 0.2× 182 3.2k
Wolfgang Kruse Germany 24 310 0.8× 253 0.6× 2.5k 7.0× 1.0k 3.7× 78 0.4× 50 3.8k
Megan E. Jewett United States 19 104 0.3× 102 0.3× 1.2k 3.4× 350 1.3× 14 0.1× 22 3.5k
J. E. Skinner United States 16 192 0.5× 83 0.2× 566 1.6× 206 0.7× 20 0.1× 34 1.4k

Countries citing papers authored by Dan Wilson

Since Specialization
Citations

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

Fields of papers citing papers by Dan Wilson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Wilson

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Wilson. A scholar is included among the top collaborators of Dan Wilson 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 Dan Wilson. Dan Wilson 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.
Wilson, Dan. (2025). Data-driven model identification near a supercritical Hopf bifurcation using phase-based approaches. Physica D Nonlinear Phenomena. 476. 134635–134635.
2.
Ellis, Hugh Logan, R. L. Eyres, Julie Whitney, et al.. (2025). What can we learn from 68 000 clinical frailty scale scores? Evaluating the utility of frailty assessment in emergency departments. Age and Ageing. 54(4). 7 indexed citations
3.
Ellis, Hugh Logan, Julie Whitney, Dan Wilson, et al.. (2025). Can laboratory test-based frailty indices contribute to frailty screening in emergency departments?. Age and Ageing. 54(7). 1 indexed citations
4.
Wilson, Dan & Kai Sun. (2024). Reduced Order Characterization of Nonlinear Oscillations Using an Adaptive Phase-Amplitude Coordinate Framework. SIAM Journal on Applied Dynamical Systems. 23(1). 470–504.
5.
Wilson, Dan. (2023). A direct method approach for data-driven inference of high accuracy adaptive phase-isostable reduced order models. Physica D Nonlinear Phenomena. 446. 133675–133675. 5 indexed citations
6.
Wilson, Dan. (2023). A Reduced Order Modeling Framework for Strongly Perturbed Nonlinear Dynamical Systems Near Arbitrary Trajectory Sets. SIAM Journal on Applied Dynamical Systems. 22(2). 603–634.
7.
Wilson, Dan, et al.. (2022). Control of coupled neural oscillations using near-periodic inputs. Chaos An Interdisciplinary Journal of Nonlinear Science. 32(3). 33130–33130. 6 indexed citations
8.
Wilson, Dan. (2022). Data-driven identification of dynamical models using adaptive parameter sets. Chaos An Interdisciplinary Journal of Nonlinear Science. 32(2). 23118–23118. 8 indexed citations
9.
Sadovnik, Amir, et al.. (2022). Data-driven inference of low-order isostable-coordinate-based dynamical models using neural networks. Nonlinear Dynamics. 111(3). 2501–2519. 3 indexed citations
10.
Park, Youngmin & Dan Wilson. (2021). High-Order Accuracy Computation of Coupling Functions for Strongly Coupled Oscillators. SIAM Journal on Applied Dynamical Systems. 20(3). 1464–1484. 8 indexed citations
11.
Wilson, Dan. (2021). Optimal Control of Oscillation Timing and Entrainment Using Large Magnitude Inputs: An Adaptive Phase-Amplitude-Coordinate-Based Approach. SIAM Journal on Applied Dynamical Systems. 20(4). 1814–1843. 12 indexed citations
12.
Wilson, Dan, et al.. (2021). Exploiting circadian memory to hasten recovery from circadian misalignment. Chaos An Interdisciplinary Journal of Nonlinear Science. 31(7). 73130–73130. 4 indexed citations
13.
Wilson, Dan. (2020). Stabilization of Weakly Unstable Fixed Points as a Common Dynamical Mechanism of High-Frequency Electrical Stimulation. Scientific Reports. 10(1). 5922–5922. 4 indexed citations
14.
Wilson, Dan. (2020). A data-driven phase and isostable reduced modeling framework for oscillatory dynamical systems. Chaos An Interdisciplinary Journal of Nonlinear Science. 30(1). 13121–13121. 24 indexed citations
15.
Wilson, Dan. (2020). Optimal open-loop desynchronization of neural oscillator populations. Journal of Mathematical Biology. 81(1). 25–64. 9 indexed citations
16.
Sadler, Euan, Jane Sandall, Nick Sevdalis, & Dan Wilson. (2018). The contribution of implementation science to improving the design and evaluation of integrated care programmes for older people with frailty. Journal of Integrated Care. 27(3). 232–240. 15 indexed citations
17.
Wilson, Dan, et al.. (2018). Synchronization of heterogeneous oscillator populations in response to weak and strong coupling. Chaos An Interdisciplinary Journal of Nonlinear Science. 28(12). 123114–123114. 18 indexed citations
18.
Wilson, Dan, et al.. (2018). Phase reduction and phase-based optimal control for biological systems: a tutorial. Biological Cybernetics. 113(1-2). 11–46. 68 indexed citations
19.
Wilson, Dan & Bard Ermentrout. (2017). Greater accuracy and broadened applicability of phase reduction using isostable coordinates. Journal of Mathematical Biology. 76(1-2). 37–66. 57 indexed citations
20.
Ocloo, Fidelis C.K., et al.. (2012). Effects of irradiation on the cowpea weevil (Callosobruchus maculates F.) and moisture sorption isotherm of cowpea seed (Vigna unguiculata L. Walp). SHILAP Revista de lepidopterología. 10 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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