Zachary Nicolaou is a theoretical and computational physicist focusing on interdisciplinary phenomena in material and network systems. His research focuses on the emergence of complexity in matter, with particular emphasis on nonlinear dynamics, synchronization, pattern formation, and network dynamics in condensed matter systems and fluid mechanics. He utilizes and develops classical tools from applied mathematics and novel machine learning methods to discover and characterize the mechanisms behind novel complex dynamical phenomena. He is interested in applications and methodological developments of machine learning approaches, including data-driven system identification, Koopman, Boltzmann, and Liouville-von Neumann spectral-based methods, and neural networks for nonlinear coordinate transformations for the design of mechanical metamaterials and the characterization of disordered and glassy systems. Other topics of interest include symmetry concepts (chimera states, dimension reduction methods, and converse symmetry breaking), Floquet systems, topological acoustic band theories and band-gap design, and associated nonlinear phenomena such as gap solitons and novel resonances and bifurcations, as well as the kinetics of granular materials, glassy dynamics, and topological defects in driven and dissipative systems.