A recent trend in algorithmic research is the use of machine-learned information from prior data to obtain
beyond worst-case performance. This has been particularly successful in algorithms under uncertainty, where (noisy) machine-learned predictions have been used to overcome classical lower bounds in online algorithms. This workshop will bring together leading researchers in learning-augmented algorithms and algorithms under uncertainty to discuss recent results as well as set the agenda for the near-future in this emerging area of research.