Wearable systems have emerged as a powerful enabling technology in various domain, including health, wellness, and fitness applications. In particular, the growing ubiquity of sensor-equipped wearables such as mobile devises, pedometers, EEGs (electroencephalogram), and smartphones is making it possible to capture information about human behavior in real-time. This growth is leading to increased development and deployment of mobile sensing applications. Despite their enormous potential, however, currently existing wearables are designed for controlled environments, lab settings, and small trials with configuration-specific protocols. Scaling these systems up and extending their applications in real-world, dynamic environments brings about major challenges. We have just began developing robust machine learning and signal processing algorithms and frameworks that aim to address these challenges.