I have a passion for understanding how systems work, especially those containing many interacting and interdependent parts. Through my research, I rigorously interrogate complex systems using tools from control theory, optimization, and nonlinear systems. Understanding the information flow and distributed actions of a decentralized systems empowers us to stabilize control its emergent behavior. This empowers the systems to accomplish tasks that are impossible for any single individual. My research emphasizes the collective motion of swarms, such as flocking, formation control, task assignment, and predator avoidance.

My research is at the interface of control theory, complex systems, and optimization. I take a full-stack approach to research; starting with theoretical guarantees, validating through simulation, and following through to hardware implementation. Considering this full spectrum of solutions leads to novel insights that any individual approach may not capture. I have found applications in cyber-physical (e.g., robotic and transportation) as well as virtual (e.g., digital twins and video games) for this research. The former is critical to advancing the frontier of multi-robot and robotic swarm systems; the latter advances our understanding of distributed algorithms, multi-level abstractions, and human-centric design.

I have developed techniques for constraint-driven control, wherein agent behaviors and interactions are embedded as constraints in an optimization problem. By carefully designing the objectives and constraints of each agent, it is possible to extract guarantees on the global system-level behavior. This empowers designers to make useful abstractions of these complex systems, rigorously analyze their performance, and understand their behavior at multiple scales. Pursuing these techniques has led to useful contributions in constrained optimal control, path planning, flocking, navigation, and smart transportation networks.