Abstract: Nonlinear oscillator networks are of widespread interest in physics, chemistry, biology, engineering, and machine learning. Here, we study the dynamics of nonlinear oscillator networks in two settings. First, we develop a mathematically exact method for controlling the Kuramoto-Sakaguchi model of coupled oscillator networks, capable of steering such networks to a rich variety of dynamical states, such as synchrony, desynchrony, chimera states, and phase-locked attractors of any configuration. Then, we formulate a recurrent neural network for machine learning, related to the Kuramoto-Sakaguchi model, and analyze its input-driven dynamics in terms of a spectral decomposition as the network performs an image sequence prediction task. These results contribute to the theory of nonlinear oscillator networks and to their potential for mathematically grounded applications.