Caroline Crockett awarded Rackham Predoctoral Fellowship for research bridging two fields
Crockett’s dissertation will integrate two fields: image processing & machine learning and engineering education research
Caroline Crockett, doctoral candidate in electrical and computer engineering (ECE), has been awarded a Rackham Predoctoral Fellowship to support her dissertation which will contribute to two fields: image processing & machine learning and engineering education research. She is the first ECE student to pursue this unique path.
Her proposed dissertation title is “How Students Understand Signals and Systems and Applying Signals and Systems to Image Reconstruction.” She is advised by Jeffrey Fessler, William L. Root Collegiate Professor of Electrical Engineering and Computer Science, and Prof. Cindy Finelli.
“I am proposing a medical image reconstruction method that takes advantage of machine learning to improve image quality, but which is still explainable,” said Crockett. “The method could ultimately decrease radiation exposure for patients while providing doctors with high-quality images to properly diagnose and treat many diseases.”
She believes that by placing a premium on being able to describe how the method works, radiologists will be more likely to trust and therefore adopt the method.
A second focus of her dissertation is to improve how signals and systems concepts are taught to undergraduate students. She will investigate which signals and systems concepts are understandable to undergraduate students, what factors predict understanding, and how those factors influence understanding.
“Signals and systems are the foundation of signal processing and machine learning,” said Crockett. “These fields are growing exponentially and affecting many aspects of our lives.” Her goal is to better prepare graduates in this area to make significant contributions to the field.
Crockett loves to teach. As an undergraduate student at the University of Virginia, she assisted with a variety of electrical engineering courses, and at Michigan, was a graduate student instructor (GSI) for the graduate-level course, Matrix Methods for Signal Processing, Data Analysis and Machine Learning. She received high marks as a GSI, and incorporated multiple evidence-based teaching practices into her discussion sections.
Along with her doctoral degree in ECE, she will receive a certificate in Engineering Education Research (EER).
Crockett received the Innovative Signal Analysis (ISA) Fellowship, and a Rackham Graduate Student Research Grant for her work in EER. Her publication record began as an undergraduate student, and includes five journal and conference publications, and two presentations at the annual American Society for Engineering Education conference. She is a member of IEEE and ASEE.