  Cyber-physical systems (CPS), in most instances, represent systems of systems
with an informationally decentralized structure such as emerging mobility
systems, networked control systems, sustainable manufacturing, smart power
grids, power systems, mobility markets, social media platforms, cooperation of
robots, and internet of things. To optimize the operation of such systems, we
typically assume an ideal model. Such model-based control approaches cannot
effectively facilitate optimal solutions with performance guarantees due to the
discrepancy between the model and the actual CPS. On the other hand, in most
CPS there is a large volume of data with a dynamic nature which is added to the
system gradually in real time and not altogether in advance. Thus, traditional
supervised learning approaches cannot always facilitate robust solutions using
data derived offline. By contrast, applying reinforcement learning approaches
directly to the actual CPS might impose significant implications on the safety
and robust operation of the system. The overarching goal of the Information and
Decision Science (IDS) Lab is to investigate how to circumvent these challenges
by developing data-driven system approaches at the intersection of learning and
control. The emphasis is on how to improve energy efficiency and reduce
greenhouse gas emissions in applications related to emerging mobility systems,
e.g., connected and automated vehicles (CAVs), shared mobility, sociotechnical
systems, and smart cities, and thus contribute to the health of the planet.
