Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals.

Our research addresses urgent challenges in high confidence testing of automotive systems due to on-going and anticipated introduction of advanced, connected, and autonomous vehicle technologies.  We have pursued the development of tools for maneuver and data optimization to determine test trajectories and scenarios to facilitate vehicle testing.   Our approaches exploit game theoretic traffic interaction modeling to inform in-traffic relevant trajectories, model-free optimization to identify trajectories falsifying time domain specifications, and the development of Smart Bla

The proposal targets novel CPS challenges by aiming to develop a system that provides dynamic assessment of the interaction between sea life and smart nets. The CPS explores challenges in machine learning to continuously assess the response to sensory cues for individual species that must be determined in real time to enable modulation of the sensory cues to determine efficacy per species.

The goal of this project is to leverage and improve upon the capabilities of honey bees as agricultural pollinators by incorporating them into Bio-Cyber Physical sistems. Rapid advances are needed to aid a dwindling agricultural, increase crop yield to sustain the growing population, and provide targeted crop care to limit the need for broad pesticide treatments. These challenges may well be addressed by autonomous mobile robots and sensor networks; unfortunately, agricultural landscapes represent vast, complex, and dynamic environments that complicate long term operation.

Consider a demand response provider that wants to learn a personalized policy for each user, but the platform faces the risk of a user abandoning the platform if she is dissatisfied with the actions of the platform. For example, the platform will want to personalize the thermostat control for the user, but faces the risk that the user unsubscribes forever if they are mistreated. We propose a general thresholded learning model for scenarios like this, and discuss the structure of optimal policies.

The goal of the proposed research is to advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain. The CPS platform to be studied is that of a bipedal robot locomoting over granular ground material with uncertain physical properties (sand, gravel, dirt, etc.).