Speakers: Kangwon Seo, PhD

Kangwon Seo, PhD


Assistant Professor
Industrial and Manufacturing Systems Engineering; Statistics
University of Missouri


Kangwon Seo is an assistant professor with a joint appointment in the Department of Industrial and Manufacturing Systems Engineering and the Department of Statistics at the University of Missouri. His research mainly focuses on reliability data modeling, prediction, experimental design for accelerated life testing, and prognostics and health management. He is also interested in large-scale data analytics to extract meaningful and interesting knowledge from medical images and social media data. The research methodologies are based on the statistical-model based approach by using generalized linear mixed models, Bayesian analysis, simulations and optimization, and the data-driven approach which includes various machine learning techniques.

Tracking Alzheimer’s Disease Progression Path in a Two-Dimensional Map

In this presentation, we introduce an assistant tool for Alzheimer’s disease (AD) diagnosis and prognosis by summarizing multiple complicated features from tomographic neuroimages. This tool provides high diagnostic accuracy and sensitivity in tracking Alzheimer’s disease (AD) progression over time in clinical setting. It is also practically intuitive and explainable to patients and their families. This new visualization tool is derived from the manifold-based nonlinear dimension reduction of brain MRI features. In specific, we investigate the locally linear embedding (LLE) method using a dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI), which includes the longitudinal MRIs from 562 subjects. About 20% of them progressed to the next stage of dementia. Using only the baseline data of cognitively unimpaired and AD subjects, LLE reduces the feature dimension to two and a subject’s AD progression path can be plotted in this low dimensional LLE feature space. In addition, the likelihood of being categorized to AD is indicated by color. This LLE map is a new data visualization tool that can assist in tracking AD progression over time.