In this talk, we try to address some of these challenges through predictive data
analytics methodologies designed for IoT enabled systems. Specifically, we establish non-parametric models that predict the evolution of condition/system monitoring signals through borrowing strength from historical and in-service data. These frameworks leverage on kernel methods, functional component analysis and
Bayesian inference. Further, we discuss how these methods can consistently scale to big data settings. The methodologies are validated using numerical studies and a case study with real world data in the application to cloud-based vehicle health
monitoring service systems.

Raed Al Kontar is an Assistant Professor in Industrial and Operations Engineering department at the University of Michigan. His research focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems.