Unsupervised learning is often used to uncover clusters in data. However, different kinds of noise may impede the discovery of useful patterns from real-world time-series data. In this talk, I'll present recent work that mitigates the interference of interval censoring in the task of clustering for disease phenotyping. I'll discuss SubLign: a deep generative, continuous-time model of time-series data that clusters time-series while correcting for censorship time. I'll highlight conditions under which clusters and the amount of delayed entry may be identified from data under a noiseless model and showcase its utility for clustering time-series data that arise in healthcare.