Date of Award


Degree Type


Degree Name

Master of Science in Aeronautics and Astronautics


Aeronautics and Astronautics

First Advisor

Inseok Hwang

Committee Chair

Inseok Hwang

Committee Member 1

Daniel DLaurentis

Committee Member 2

Dengfeng Sun


As the level of automation within an aircraft increases, the interactions between the pilot and autopilot play a crucial role in its proper operation. Issues with human machine interactions (HMI) have been cited as one of the main causes behind many aviation accidents. Due to the complexity of such interactions, it is challenging to identify all possible situations and develop the necessary contingencies. In this thesis, we propose a data-driven analysis tool to identify potential HMI issues in large-scale Flight Operational Quality Assurance (FOQA) dataset. The proposed tool is developed using a multi-level clustering framework, where a set of basic clustering techniques are combined with a consensus-based approach to group HMI events and create a data-driven model from the FOQA data. The proposed framework is able to effectively compress a large dataset into a small set of representative clusters within a data-driven model, enabling subject matter experts to effectively investigate identified potential HMI issues.