Ensemble data sets appear in many domains. Ensemble visualization is a way to study the underlying generating model of data by analyzing ensembles of solutions or measurements. The objective of ensemble visualization often is to convey characteristics of the typical/central members, outliers, as well as variability in the ensemble. In absence of any information about the generative model, a family of nonparametric methods, known as data depth, provides a quantitative notion of centrality among ensemble members. Data depth methods also form the basis of several ensemble visualization techniques, including the popular Tukey boxplot. In this talk I will describe my work on developing novel data depth-based visualizations, and their advantages over existing visualization techniques, for various types of data—namely, ensembles of 3D isocontours, ensembles of paths on a graph, ensemble data in high dimensional spaces, and graphs.