1. Research Area

Research in an academic environment provides a unique opportunity to solve problems and vision the technology of the future. Our areas of expertise are centered around image processing, computer vision and machine learning, with particular interests in shape analysis for medical image processing with big data. Generally, We also have good knowledge of computational geometry as well as computer vision and machine learning.

2. Research Overview

Our primary research work is to develop computational tools that help to understand the biological mechanism of AD especially with regarding to tau propagation using neuroimaging data. Immediately we apply our distributed patch-based mapping on different brain regions to detect changes in tau pathology as well as in neurodegeneration at the different Braak stages of AD using large-scale datasets of MRI and PET from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Furthermore, for the analysis of connectivity changes due to tau pathology, we develop novel computational tools for the systemic examination of fiber pathways involved in the propagation of tau pathology. Particularly we believe that the short association fibers, U-fibers, in the superficial white matter (SWM) play a critical role in the early stage of tau propagation, and thus devise novel geodesic models on cortices as guided by the surface projection of fiber directions for accurate reconstruction of U-fibers.

3. Research Achievements

National Research Foundation of Korea, Young Researcher Program 2020-2025, Surface based analysis of brain magnetic resonance (MR) images.