Technical Seminar (Prof. Max Z. Li, Aug 7)

Speaker Bio

Max is an Assistant Professor of Aerospace Engineering at the University of Michigan, Ann Arbor. He also has courtesy appointments in Civil and Environmental Engineering as well as Industrial and Operations Engineering. Max received his PhD in Aerospace Engineering from the Massachusetts Institute of Technology (MIT) in 2021. 

He received his MSE in Systems Engineering and BSE in Electrical Engineering and Mathematics, both from the University of Pennsylvania, in 2018. Max’s research and teaching interests include air transportation systems, airport and airline operations, Advanced Air Mobility, networked systems, as well as optimization and control.

Title

Practical Operational Optimization under Uncertainty for Autonomous and Air Transportation Systems

Abstract

Air transportation and autonomous systems must operate under incomplete, uncertain, and continuously evolving information. Whether monitoring hazards with uncrewed aerial vehicles (UAVs) or managing aircraft arrival flows under trajectory-based operations (TBO) concepts, effective decision making requires balancing operational objectives with uncertainty, safety, and limited resources. In this talk, I present a series of optimization frameworks that address these challenges across multiple transportation domains.

First, we begin by discussing an integrated route optimization and path planning framework for UAV hazard monitoring, where uncertain reports are represented as probabilistic regions of interest and online belief updates enable adaptive replanning during mission execution. Building on this idea, the second part presents a coordinated air (UAV) and ground (uncrewed ground vehicles, or UGV) decision-making model that combines Bayesian learning with vehicle routing to jointly optimize exploration, sensing, and hazard mitigation in dynamic environments. Next, I will discuss work that applies optimization to arrival flow management in terminal airspace, developing strategically optimized arrival trajectories while incorporating weather avoidance and formal continuous-time safety verification.