The overarching goal of the IDS Lab is to develop rigorous theories and data-driven system approaches at the intersection of learning and control to enable systems—whether vehicles, robots, or large-scale infrastructures—to operate autonomously and achieve near-optimal performance while safely adapting to and interacting with dynamic environments. Our work integrates decision-theoretic foundations with learning-based methods to endow engineered systems with the capability to reason, learn, and act in real time. While these methodologies are broadly applicable, my principal domain of application has been emerging mobility systems, including connected and automated vehicles (CAVs), shared autonomous mobility, and smart-city infrastructures.

In the IDS Lab, we believe that the diversity of age, experience, race, ethnicity, religion, and gender can significantly contribute to the formation of a dynamic intellectual group. We are dedicated to fostering a diverse and inclusive environment characterized by mutual respect and a steadfast rejection of discrimination, prejudice, and intolerance.