Leveraging Subject Matter Expertise to Optimize Machine Learning Techniques for Air and Space Applications
Date of Award
Doctor of Philosophy (PhD)
Department of Mathematics and Statistics
Aihua W. Wood, PhD
We develop new machine learning and statistical methods that are tailored for Air and Space applications through the incorporation of subject matter expertise. In particular, we focus on three separate research thrusts that each represents a different type of subject matter knowledge, modeling approach, and application. In our first thrust, we incorporate knowledge of natural phenomena to design a neural network algorithm for localizing point defects in transmission electron microscopy (TEM) images of crystalline materials. In our second research thrust, we use Bayesian feature selection and regression to analyze the relationship between fighter pilot attributes and flight mishap rates. We use the Bayesian approach to incorporate findings from prior qualitative studies on flight mishaps. In our last research thrust, we utilize data generated from Air Force evaluation processes. We use structural causal models to represent our knowledge of Air Force evaluation processes and present a framework for using regression to identify and estimate causal relationships.
DTIC Accession Number
Cho, Philip Y., "Leveraging Subject Matter Expertise to Optimize Machine Learning Techniques for Air and Space Applications" (2022). Theses and Dissertations. 5534.