Complex Systems, Networks, Data and Computation

• I develop data-science methods and theory using generalizations of graphs including temporal, multiplex and multilayer networks, hypergraphs, and simplicial complexes.

• My methods include spectral theory, perturbation theory, random matrix theory, bifurcation theory, information theory, latent geometry, and homology.

• I study neurocomputation including mathematically guided designs for artificial neural networks and neural coding theory for biological neuronal networks.

• I develop theory for structural/dynamical mechanisms for multiscale phenomena including self-organization in complex systems.

• I tackle domain-driven questions through interdisciplinary collaborations with experts from the biological, physical, social, and computer sciences.

Announcements

I also have openings for funded graduate and undergraduate students. If interested, email me at first.last@uwyo.edu

Select Papers 

• Z Song and D Taylor (2023) Coupling asymmetry optimizes collective dynamics over multiplex networks. IEEE Transactions on Network Science and Engineering 11(2) 1524 - 1541. [arXiv]

• J Kazimer, MD Domenico, PJ Mucha and D Taylor (2022) Ranking edges by their impact on the spectral complexity of information diffusion over networks. [arXiv]

• MQ Le and D Taylor (2022) Persistent homology with k-nearest-neighbor filtrations reveals topological convergence of PageRank. [arXiv]

• I Aguiar, D Taylor J Ugander (2022) A factor model of multilayer network interdependence. [arXiv]

• BU Kilic and D Taylor (2022) Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes. Communications Physics 5, 278.

• MQ Le and D Taylor (2022) Persistent homology of convection cycles in  network flows. Physical Review E 105, 044311. 

• C Ziegler et al.  (2022) Balanced Hodge Laplacians optimize consensus dynamics over simplicial complexes. Chaos: An Interdisciplinary Journal of Nonlinear Science 23, 023128.

• NB Erichson, D Taylor, Q Wu and MW Mahoney (2021) Noise-response analysis of deep neural networks quantifies robustness and fingerprints structural malware. In Proceedings of the SIAM International Conference on Data Mining, 100-108.  

• D Taylor, MA Porter and PJ Mucha (2021) Tunable eigenvector-based centralities for multiplex and temporal networks. Multiscale Modeling & Simulation: A SIAM Interdisciplinary Journal 19(1), 113–147.

• D Taylor, RS Caceres and PJ Mucha (2017)  Super-resolution community detection for layer-aggregated multilayer networks. Physical Review X 7, 031056.

• D Taylor, S Shai, N Stanley and PJ Mucha (2016) Enhanced detectability of community structure in multilayer networks through layer aggregation. Physical Review Letters 116, 228301.

• D Taylor et. al. (2015) Topological data analysis of contagion maps for examining spreading processes on networks. Nature Communications 6, 7723.

• J Sun, D Taylor and EM Bollt (2015) Causal network inference by optimal causation entropy. SIAM Journal on Applied Dynamical Systems 14(1), 73-106.