In this talk, I will begin with an overview of the challenges of learning time series event detection models in the emerging area of mobile health or mHealth. mHealth technologies, including on-body physiological sensors, have the potential to yield significant insights into health and behavior. They also offer compelling possibilities for informing the delivery of adaptive health interventions. However, the analysis of mHealth data is often subject to an array of complicating factors at the level of both sensor data and event labels. This talk will focus on the problem of learning time series detection models from temporally imprecise event labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy event time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels occur in mHealth research both when observers are tasked with precisely labeling the occurrence of short duration events (such as individual puffs on a cigarette), as well as when study subjects are tasked with self-reporting events retrospectively (such as the start and end times of the last time they smoked).