-|- CV -|- Google Scholar -|- UW-SoC -|- UW Math -|-
I develop data-science methods and theory using generalizations of graphs including temporal, multiplex and multilayer networks, and simplicial complexes.
My methods include spectral theory, perturbation theory, random matrix theory, bifurcation theory, information theory, latent geometry, and homology.
I develop theory for structural/dynamical mechanisms for multiscale phenomena including self-organization in complex systems.
I study neurocomputation including mathematically guided designs for artificial neural networks and neural coding theory for biological neuronal networks.
I tackle domain-driven questions through interdisciplinary collaborations with experts from the biological, physical, social, and computer sciences.
M Butler, A Khan, F Afrifa, Y Hu, and D Taylor Multilayer networks characterize human-mobility patterns by industry sector for the 2021 Texas winter storm [arXiv]
MQ Le and D Taylor (2024) 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.
J Kazimer, MD Domenico, PJ Mucha and D Taylor (2024) Ranking edges by their impact on the spectral complexity of information diffusion over networks. Multiscale Modeling & Simulation 22(3), 925-955.
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.