Machine learning has become a useful tool for many data-rich problems. However, its use in cyber-physical systems (CPS) has been severely limited because of its need for large amounts of well-labeled data, often tailored to each deployment scenario. While especially challenging for high-dimensional data, the situation is further exacerbated by the complexity and variability of the physical systems being studied and modeled. For example, smart city applications often require significant data to obtain the required robustness for operations in different weather, time of day, users, and cities, etc. Our research enables data science in real physical systems by reducing reliance on initial labeled data through the integration of physical knowledge, the actuation of sensing systems, and the adaptation of data models. Currently, our work is focusing on:

• Fusing physics-based models with empirical models of the system to create more data
• Actuating physical sensing hardware to improve data quality for optimal learning
• Optimizing data adaptation between different application scenarios using the physical understanding of how data distributions change

This research is informed by real-world applications and deployments using the structure as sensors, and mobile carriers as sensors.