Deep learning has emerged as a powerful technique for achieving state-of-the-art results in various domains and applications. However, one of the key assumptions underlying traditional supervised learning approaches is that training and testing data are drawn from the same distribution. In real-world scenarios, this assumption may not hold true. There can be a significant distribution shift between the source domain, on which the data has been trained, and the target domains where the model is expected to generalize. Even a slight departure from a network’s training domain can cause it to make spurious predictions and significantly hurt its performance. As part of this new research pillar we develop and/or evaluate novel algorithms for deep unsupervised domain adaptation or domain generalization and demonstrate their validity on a variety of  tasks harnessing physiological time series and medical images.