-|- CV -|- Google Scholar -|- UW-SoC -|- UW Math -|-
Network science applications across the biological, physical and social sciences and engineering
Data algorithms and theory using graphs, hypergraphs, simplicial complexes, and temporal, multiplex and multilayer networks
Nonlinear science including multiscale, structural-dynamical mechanisms for self-organizing complex systems
Spectral perturbation theory, random matrix theory, bifurcation theory, information theory, applied topology and geometry
Neural computing for artificial and biological neuronal networks
MI Sejunti, M Butler, Y Hu and D Taylor (2026) Predictability of human movements across industry sectors using multilayer networks. Preprint at arXiv:2607.28494.
G Mikaberidze, B Mikaberidze and D Taylor (2026) GradNet: A gradient-based framework for optimal network science. Preprint at arxiv:2603.09197.
C Purple, PS Skardal and D Taylor (2026) Hodge decomposition guides the optimization of synchronization over simplicial complexes. Preprint at arXiv:2601.04326.
G Mikaberidze and D Taylor (2025) Emergent topology of optimal networks for synchrony. Preprint at arXiv:2509.18279.
M Butler, A Khan, F Afrifa, Y Hu, and D Taylor (2026) Multilayer networks characterize human-mobility patterns by industry sector for the 2021 Texas winter storm. NPJ Complexity 3, 15.
MQ Le and D Taylor (2025) Persistent homology with k-nearest-neighbor filtrations reveals topological convergence of PageRank. Foundations of Data Science 7(2), 536-567.
I Aguiar, D Taylor J Ugander (2024) A factor model of multilayer network interdependence. Journal of Machine Learning Research 25(282), 1-54.
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]
BU Kilic and D Taylor (2022) Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes. Communications Physics 5, 278.
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 19(1), 113–147.
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.