Research

This research focuses on modeling complex air traffic flows and estimating airspace demand and capacity using large-scale aviation data. By leveraging advanced machine and deep learning architectures, we predict 4D trajectories to enhance the efficiency of trajectory-based operations (TBO). In addition, we develop Agentic AI models for airline operations to enable more intelligent and autonomous next-generation air traffic management systems.

To ensure the safe operation of urban air mobility (UAM) and unmanned aircraft systems (UAS), we develop technologies for analyzing and managing complex low-altitude airspace. This research includes dynamic geofencing based on 3D terrain and obstacle data, as well as path planning algorithms that generate optimal flight routes through real-time airspace analysis, contributing to improved low-altitude airspace safety and operational efficiency.

We develop models to proactively identify and mitigate potential collision risks during aircraft operations. This research models complex air & ground collision scenarios, including abnormal maneuvers (“blunders”), and develops real-time conflict detection & resolution (CDR) algorithms that generate optimal avoidance maneuvers to maintain safe separation and enhance operational safety.