Dr. Kim’s research focuses on computational genomics, population genetics, and statistical machine learning. She is interested in developing statistical machine learning tools for analyzing large-scale genomic data and investigating biological systems of various organisms and disease processes using these tools.
Jun Ho Yoon
Highlighted PublicationsDoubly mixed-effects Gaussian process regression
J. Yoon, D. Jeong, S. Kim. Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. (to appear)
EiGLasso for scalable sparse Kronecker-sum inverse covariance estimation
J. Yoon, S. Kim. arXiv:2105.09872, 2021. [preprint] (to appear in Journal of Machine Learning Research.
Learning gene networks underlying clinical phenotypes using SNP perturbation
C. McCarter, J. Howrylak, S. Kim. PLoS Computational Biology, 2020. [pdf]
EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices
J. Yoon, S. Kim. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), 2020. [pdf]
Multi-level Gaussian graphical models conditional on covariates
G. Kim, S. Kim. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [pdf]