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Uncertainty pervades all areas of the natural sciences, engineering, and operations research. Such uncertainty may arise due to noisy measurements, model ambiguity, or unknown parameters. Moreover, optimal decision making under uncertainty is crucial to arrive at reliable solutions, which are resilient to catastrophe. Despite the inherent need to do so, including uncertainty in our models in the form of random parameters leads to a veritable explosion in the dimension of the resulting optimization or control problems; especially for models of complex engineering systems. Recent advances in the fields of uncertainty quantification and machine learning present exciting opportunities to help combat this curse of dimensionality. This workshop is intended is to join researchers in optimization under uncertainty, uncertainty quantification, and machine learning, whose work stands to benefit from cutting-edge machine learning techniques for intricate, data-driven models of real-world phenomena.