In this dissertation, we apply deep learning tools and techniques to two problems: detecting strong gravitational lenses and modeling the effects of mobility on COVID-19 mortality. In Chapter 1, we give an overview of the problems we investigate and summarize some of the assumptions and results we made in our studies. In Chapter 2, we systematically study the effects of data augmentation, semi-supervised learning algorithms, and unsupervised learning algorithms in the context of training deep learning classifiers with only simulated lenses to detect real lenses. In Chapter 3, we conduct a descriptive data analysis of mobility in Davis during the COVID-19 pandemic. In Chapter 4, we study the causal effects of mobility on COVID-19 mortality across four counties in California, using neural nets to model the effects.