Research

The brain is a network with structure at many scales. My work asks which parts of that structure are stable, individual, and informative. Threads include functional connectome fingerprinting, morphospaces that describe the range of network configurations, persistent homology and Riemannian geometry applied to brain sub-circuits, and information-theoretic accounts of communication in brain networks. A newer direction asks whether brain-inspired sub-circuits make better reservoir computers, such as Echo State Networks.

The goals are to link imaging-genetic associations with brain transcriptome data, to build systems that analyze disease biomarkers and cognitive data from biobanks at scale, to combine automated machine learning, knowledge graphs and curated databases to speed up discovery in Alzheimer’s disease and related dementias, and to turn electronic health records and the literature into actionable insight. Recent work includes few-shot diagnosis with tabular LLMs, LLM-and-knowledge-graph question answering, and vision-language models for 3D CT and MRI.

With colleagues, I study how cognitive diversity, motivation and grit relate to academic performance in multidisciplinary engineering courses, and how those findings can shape course design. This work grew out of my graduate certificate in Teaching and Learning in Engineering at Purdue and my teaching at the Naval Academy.