In this talk, we focus on problems related to the simultaneous optimal control of ensembles of dynamical systems (ODEs). These questions arise naturally in several situations in Applied Mathematics, for example when a usual control system (e.g. related to a physical or biomedical model) depends on parameters affected by uncertainty, or when the Cauchy datum is not available with precision due to measurement errors. In this setting, we typically aim at finding a strategy that should be the same for every system of the ensemble, and that minimizes a proper cost. In these cases, the proposed policy should incorporate the uncertainty that affects the system, and typically we seek one that results in a good performance in the most likely scenarios (averaged optimization), or one that guarantees resilience in the least favorable conjuncture (worst-case optimization). Finally, this framework is suitable for studying continuous-time models of neural networks (neural ODEs) and for enhancing adversarial robustness.