UNC Greensboro|Directory|Yoonmi Hong

Yoonmi Hong

Visiting Assistant Professor

Computer Science

Email Address: y_hong2@uncg.edu

about

Dr. Yoonmi Hong is an educator and researcher specializing in the intersection of applied mathematics, computational analysis, and machine learning. She earned her Ph.D. in Mathematical Sciences and an M.S. in Applied Mathematics from the Korea Advanced Institute of Science and Technology (KAIST), following a B.S. in Mathematics from Yonsei University. Her research focuses on medical image analysis, neuroimaging, applied harmonic analysis, and machine learning, applying rigorous mathematical modeling to extract insights from complex biomedical data. In the classroom, she teaches courses including Foundations of Computer Science II and Introduction to Computer Programming for Non-majors, guiding students through core computer science principles as well as accessible, practical programming skills.

    1. Y. Hong, E. Cornea, J. B. Girault, R. L. Stephens, M. Bagonis, M. Foster, S. H. Kim, J. C. Prieto, M. A. Styner, and J. H. Gilmore, Structural connectome gradients and their relationship to IQ in childhood, Frontiers in Human Neuroscience, 23 November, 2025.

    2. Y. Hong, O. Azrak, J. J. Wolff, M. R. Swanson, J. T. Elison, G. Gerig, J. R. Pruett Jr., C. Vachet, K. N. Botteron, S. R. Dager, A. M. Estes, H. C. Hazlett, R. Schultz, M. D. Shen, L. Zwaigenbaum, A. Evans, D. L. Collins, V. S. Fonov, E. Cornea, J. B. Girault, M. Foster, S. H. Kim, D. Garic, J. C. Prieto, J. Piven, J. H. Gilmore, and M. Styner, Predicting Cognitive Outcomes by Mapping White Matter Tracts to Surface, MLMI 2025, Daejeon, South Korea, Sep 22, 2025.

    3. Y. He, Y. Hong (Co-corresponding author), and Ye Wu, Spherical-deconvolution Informed Filtering of Tractograms Changes Laterality of Structural Connectome, NeuroImage, 2024.

    4. Y. Hong, E. Cornea, J. B. Girault, M. Bagonis, M. Foster, S. H. Kim, J. C. Prieto, H. Chen, W. Gao, M. A. Styner, and J. H. Gilmore, Structural and functional connectome relationships in early childhood, Developmental Cognitive Neuroscience, 2023.

    5. G. Chen, Y. Hong, K. M. Huynh, and P.-T. Yap, Deep learning prediction of diffusion MRI data with microstructure-sensitive loss function, Medical Image Analysis, 2023.

    6. Y. Hong, S. Ahmad, Y. Wu, S. Liu, and P.-T. Yap, Vox2Surf: Implicit Surface Reconstruction from Volumetric Data, MLMI 2021, Strasbourg, France, Sep 27, 2021.

    7. Y. Hong, J. Kim, G. Chen, W. Lin, P.-T. Yap, and D. Shen, Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks. IEEE Transactions on Medical Imaging, 2019.