Abstract
Air traffic control (ATC) and air traffic management (ATM) operate across multiple timescales and decision layers, yet both are fundamentally shaped by uncertainty arising from weather, capacity disruptions, and human decision-making. In this talk, I present two complementary research projects, one focused on air traffic control (ATC) and the other on air traffic management (ATM), that together offer a unified perspective on how uncertainty can be explicitly modeled and managed across tactical ATC operations and strategic ATM planning. At the tactical ATC level, we study pathfinder operations during convective weather and develop a decision-theoretic framework that captures stochastic airspace availability, flight acceptance behavior, and pathfinder sequencing. We show that the proposed models yield rich insights into system behavior and inform the design of operational decision support tools. At the strategic ATM level, we address uncertainty in airport ground delay programs through a distributionally robust optimization framework that hedges against capacity mis-specification and demonstrates strong out-of-sample performance using data from the US National Airspace System. Together, these results show how explicit uncertainty modeling across ATC and ATM decision layers can improve robustness and operational performance.
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 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.
