Dane Taylor
PhD
Assistant Professor
Research Interests
Network-Data Analytics — methodology development for community detection, ranking systems, network inference, and manifold learning; Dynamics on and of Networks — nonlinear and stochastic systems including social contagions, oscillator synchronization, and network evolution.
Education
PhD, Applied Mathematics, University of Colorado, Boulder
Research Summary
I lead a research group developing theory and methodology for network-based models of data, algorithms and computational infrastructure. My group focuses data-driven models of complex systems, working along the following pursuits:
- Developing of mathematically and statistically rigorous data-science methodology;
- Developing neurocomputation theory for the dynamics of biological neuronal networks;
- Developing theoretical foundations for artificial neural networks including ResNets, NeuralODEs and graph neural networks;
- Developing novel models and theory for consensus dynamics to study collective learning and develop new methods for decentralized ML/AI;
- Developing theory for structural/dynamical mechanisms that facilitate multiscale self-organization.
- Collaborating with domain experts in the biological, physical, social, and medical sciences and engineering to address domain-driven problems.
Selected Publications
- 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 (2020) Multiplex Markov chains: Convection cycles and optimality. Physical Review Research 2, 033164.
- D Taylor, SA Myers, A Clauset, MA Porter and PJ Mucha (2017) Eigenvector-based centrality measures for temporal networks. Multiscale Modeling and Simulation 15(1), 537-574.
- D Taylor, PS Skardal and J Sun (2016) Synchronization of heterogeneous oscillators under network modifications: Perturbation and optimization of the synchrony alignment function. SIAM Journal on Applied Mathematics 76(5), 1984-2008.
- 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.
- PS Skardal, D Taylor and J Sun (2014) Optimal synchronization of complex networks. Physical Review Letters 113, 144101.