Zhexian Li is a researcher in systems and control whose work focuses on making future transportation systems safer, more reliable, and more efficient. He is particularly interested in advanced air mobility—the emerging field of on-demand air transportation, often described as “air taxis.” As aviation moves toward greater autonomy, one of the biggest challenges is ensuring that these systems can still make sound decisions when things go wrong, such as power loss, communication failures, or unexpected disruptions.
Drawing on his training in dynamics, control theory, and mobility systems, Zhexian develops methods that combine data-driven learning with physics-based models of system behavior. His goal is to create decision-making tools that do not simply perform well under ideal conditions, but that remain dependable under uncertainty. In particular, he uses machine learning and uncertainty quantification to help autonomous transportation systems recognize what they do not know, adapt to incomplete or noisy information, and maintain safe operation.
Through MIDAS, Zhexian aims to deepen his training in artificial intelligence and data science so he can build learning-enabled transportation systems with stronger guarantees of safety, resilience, and efficiency. His research bridges theory and application, connecting advances in AI to real-world mobility challenges at both the vehicle level and the network level. By doing so, he hopes to support the development of next-generation transportation systems that are not only innovative, but also trustworthy and robust enough for everyday public use.