Multiscale Theory of Complex Systems, Networks, Dynamics, 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.
*** I am currently recruiting postdocs, grad students, and undergraduate students for funded positions. Please email if interested. ***
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.