One branch of my research considers how to use data and models to improve manufacturing productivity. We look at how to capture streaming data from the factory floor, put it together with models encapsulated in “digital twins”, and use the predictions to better schedule production, plan maintenance, detect anomalies (including cyber-attacks), reconfigure the manufacturing system or supply chain to react to disturbances, and improve quality. We use models based on physics, subject-matter expertise, and historical data. We partner with various companies and industries to understand the needs and opportunities.
Another branch of my research considers how to improve the performance of human-robot teams through modeling and feedback. We run user studies with semi-automated vehicles and robots, and ask humans to work together with the automation to accomplish tasks. We collect physiological and behavioral data, build models of trust and situation awareness, and adapt the automation to enhance the overall team performance. Models are built using historical data and are informed by existing literature.