Assistant Professor Dane Taylor

Dane Taylor, Ph.D., University of Colorado, Boulder Assistant professor, School of Computing and the Department of Mathematics and Statistics Adjunct Professor in Electrical Engineering and Computer Science Engineering Building 4087 dane.taylor@uwyo.edu | +1-307-766-5280 personal website | Google scholar Research interests: Networks, Data Science, Complex Systems, Self-organization, Neurocomputation

Education

Ph.D. Applied Mathematics, University of Colorado, Boulder, 2013
B.S. Electrical Engineering (bioengineering), University of Wyoming, 2008
B.S. Physics, University of Wyoming, 2008

About Dr. Taylor

Prior to joining the University of Wyoming in 2023, Dr. Taylor was an assistant professor at the University at Buffalo, State University of New York (2017-2023) with affiliations in the Department of Mathematics and the Institute for Artificial Intelligence and Data Science. Before that, he was a postdoctoral scholar at the University of North Carolina at Chapel Hill (2015-2017) and the Statistical and Applied Mathematical Sciences Institute (2013-2015). Dr. Taylor has published over 40+ papers on network-based modeling for data and complex systems and is a recognized leader on the application of mathematics to these fields. He been the main organizer for the 2022 Northeast Regional Conference on Complex Systems, the 2020 SIAM Workshop on Network Science, and many minisymposia for conferences including NetSci, the SIAM Conference on Mathematics of Data Science, and the SIAM Conference on Applications of Dynamical Systems. Outside of professional life, Dr. Taylor enjoys hiking, snowboarding and mountain biking.

Research Interests

• Develop data-science methods and theory using generalizations of graphs including temporal, multiplex and multilayer networks, simplicial complexes and hypergraphs.
• Methods include spectral theory, perturbation theory, random matrix theory, bifurcation theory, information theory, latent geometry, and homology theory.
• Study neurocomputation including mathematically guided designs for artificial neural networks and neural coding theory for biological neuronal networks.
• Develop theory for structural/dynamical mechanisms for multiscale phenomena including self-organization in complex systems.
• Tackle domain-driven questions for complex systems through interdisciplinary collaborations with experts from the biological, physical, social, and computer sciences.

Awards

Dr. Taylor’s research is currently supported by two NSF programs: Algorithms for Threat Detection (ATD) and Mathematical Biology. The ATD grant is supporting the development of movement-pattern analysis methods for human mobility data, and the math-bio grant is supporting theory development for epidemic spreading over temporal networks. Prior research support includes awards from the Nakatani Foundation (2020-2021) and Simons Foundation (2018-2024). Other notable awards include the following: a Postdoctoral Award for Research Excellence at UNC (2015); a Course-development Teaching Award from the UNC Office of Undergraduate Research and the Howard Hughes Medical Institute (2014); a Graduate Research Fellowship from the ARCS Foundation (2009-2012); and an Undergraduate Research Fellowship from the Wyoming NASA Space Grant Consortium (2006).

Representative Publications

1. Z Song and D Taylor (2023) Coupling asymmetry optimizes collective dynamics over multiplex networks. IEEE Transactions on Network Science and Engineering, in press.
2. BU Kilic and D Taylor (2022) Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes. Communications Physics 5, 278.
3. NB Erichson, D Taylor, Q Wu and MW Mahoney (2021) Noise-response analysis of deep neural networks quantifies robustness and fingerprints structural malware. SIAM International Conference on Data Mining, 100-108. 
4. 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.
5. D Taylor, JG Restrepo and FG Meyer (2018) Ensemble-based estimates of eigenvector error for empirical covariance matrices. Information and Inference, iay010
6. 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.
7. D Taylor et. al. (2015) Topological data analysis of contagion maps for examining spreading processes on networks. Nature Communications 6, 7723.
8. 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.