Younggeun Kim
Ast. Prof, Department of Statistics & Probability
Location: C423 Wells Hall
Email: kimyo145@msu.edu
Website: https://kyg0910.github.io/
CV: Download CV
Bio
Dr. Young-geun Kim received a triple BS degree in Industrial Engineering, Statistics, and Mathematical Sciences from Seoul National University in 2015. He received a Ph.D. degree in Statistics from Seoul National University in 2021. Before joining MSU, he was a postdoctoral fellow at the Department of Biostatistics and the Department of Psychiatry at Columbia University. His research interests include the theoretical properties of statistical distances and their application to deep learning. His recent works focus on Wasserstein generative models, identifiable representation learning, off-policy policy evaluation, and applications to Adolescent Brain Cognitive Development (ABCD) study and contingency management intervention in substance use disorder treatment.
Courses
- STT 997: Advanced Topics in Statistics
Office Hours
- Tuesday: 3:00PM-5:00PM
Selected Publications
- Kim, Y.-G.*, Ravid, O.*, Zhang, X., Kim, Y., Neria, Y., Lee, S., He, X.‡, and Zhu, X.‡ (2024). Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder. Frontiers in Psychiatry. View Publication
- Kim, Y.-G., Kwon, Y., and Paik, M.C. "Valid oversampling schemes to handle imbalance." Pattern Recognition Letters 125 (2019): 661-667. View Publication
- Kim, Y.-G., Kwon, Y., Chang, H., and Paik, M.C. "Lipschitz continuous autoencoders in application to anomaly detection." International Conference on Artificial Intelligence and Statistics. PMLR, 2020. View Publication
- Kim, Y.-G., Lee, K., and Paik, M.C. "Conditional wasserstein generator." IEEE Transactions on Pattern Analysis and Machine Intelligence 45.6 (2022): 7208-7219. View Publication
- Kim, Y.-G., Liu, Y., and Wei, X. "Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE." International Conference on Artificial Intelligence and Statistics. PMLR, 2023. View Publication