Publications

Author profile on Google Scholar: link

Peer-reviewed Papers

Chu, B. B., Ko, S., Zhou, J. J., Jensen, A., Zhou, H., Sinsheimer, J. S., Lange, K. (2023). Multivariate Genomewide Association Analysis by Iterative Hard Thresholding. Bioinformatics, 39(4), btad193. [paper] [Julia code]
Ko, S., Chu, B. B., Peterson, D., Okenwa, C., Papp, J. C., Alexander, D. H., Sobel, E. M., Zhou, H., Lange, K. (2023). Unsupervised discovery of ancestry informative markers and genetic admixture proportions in biobank-scale data sets. American Journal of Human Genetics, 109(3), pp. 433-445. [paper][Julia code]
Ko, S., Zhou, H., Zhou, J., and Won, J.-H. (2022). High-Performance Statistical Computing in the Computing Environments of the 2020s. Statistical Science, 37(4), pp. 494–518. [paper] [Python code, PyTorch, dask]
Kim J., Jensen, A., Ko, S., Raghavan, S., Phillips, L. S., Hung, A., Sun, Y., Zhou, H., Reaven, P., Zhou, J. J. (2022), Systematic Heritability and Heritability Enrichment Analysis for Diabetes Complications in UK Biobank and ACCORD Studies, Diabetes, 71(5), pp. 1137–1148.
∗Ko, S., ∗German, C., Jensen, A., Shen, J., Wang, A., Mehrotra, D. V., Sun, Y. V., Sinsheimer, J. S., Zhou, H., Zhou, J. J. (2022), GWAS of longitudinal trajectories at biobank scale, American Journal of Human Genetics, 109(3), pp. 433–445. [Julia code]
Chu, B. B., Sobel, E. M., Wasiolek, R., Ko, S., Sinsheimer, J. S., Zhou, H., Lange, K. (2021), A Fast Data-Driven Method for Genotype Imputation, Phasing, and Local Ancestry Inference: MendelImpute.jl. Bioinformatics, 37(24), pp. 4756–4763.
Ko, S., Li, G. X., Choi, H. and Won, J.-H. (2021). Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx. Briefings in Bioinformatics, 22(6), bbab256. [Julia code]
Kim, J., Shen, J., Wang, A., Mehrotra, D. V., Ko, S., Zhou, J. J., Zhou, H. (2021), VCSEL: Prioritizing SNP-Set by Penalized Variance Component Selection, Annals of Applied Statistics, 15(4), pp. 1652–1672.
Ryu, E. K., Ko, S., Won, J.-H. (2020). Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET. SIAM Journal on Scientific Computing, 42(1), pp. B185–B206. [Matlab code]
Zhou, H., Sinsheimer, J. S., Bates, D. M., Chu, B. B., German, C. A., Ji, S. S., Keys, K. L., Kim, J., Ko, S., Mosher, G. D., and Papp, J. C. (2020). OpenMendel: A cooperative programming project for statistical genetics. Human Genetics, 139(1), pp. 61–71. [project homepage]
Ko, S., Yu, D., Won, J.-H. (2019). Easily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration. Journal of Computational and Grahpical Statistics, 28(4), pp. 821–833. [Python code, TensorFlow, Docker]
Ko, S. and Won, J. H. (2019). Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator. In The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1185–1194.
Racah, E., Ko, S., Sadowski, P., Bhimji, W., Tull, C., Oh, S.-Y., Baldi, P., and Prabhat (2016). Revealing fundamental physics from the Daya Bay Neutrino Experiment using deep neural networks, In 2016 15th IEEE International Conference on Machine Learning and Applications, pp. 892–897.
Ko, S. and Won, J.-H. (2016). Processing large-scale data with Apache Spark. Korean Journal of Applied Statistics, 29(6), pp. 1077–1094.
Ko, S., Jun, G., and Won, J.-H. (2016). HyperConv: Spatio-spectral classification of hyperspectral images with deep convolutional neural networks. Korean Journal of Applied Statistics, 29(5), pp. 859–872. [Python code, Theano]
Kim, J., Kim, S., Ko, S., In, Y. H., Moon, H. G., Ahn, S. K., Kim, M. K., Lee, M., Hwang, J. H., Ju, Y. S., Kim, J. I., Noh, D. Y., Kim, S., Park, J. H., Rhee, H., Kim, S., and Han, W. (2015). Recurrent fusion transcripts detected by whole transcriptome sequencing of 120 primary breast cancer samples. Genes, Chromosomes and Cancer, 54(11), pp. 681–691.
Kim, Y., Kang, Y. S., Lee, N. Y., Kim, K. Y., Hwang, Y. J., Kim, H. W., Rhyu, I. J., Her, S., Jung, M. K., Kim, S., Lee, C. J., Ko, S., Kowall, N. W., Lee, S. B., Lee, J., and Ryu, H. (2015). Uvrag targeting by Mir125a and Mir351 modulates autophagy associated with Ewsr1 deficiency. Autophagy, 11(5), pp. 796–811.

Working Papers and Preprints

Ko, S., Suchard, M., and Holbrook, A. (2024+). Analyzing millions of SARS-CoV-2 cases with spatiotemporal Hawkes processes.
Ko, S., Zhou, H., Sobel, E., and Lange, K. (2024+). Accurate estimation of genetic admixture proportions via haplotype modeling.
Ko, S., Zhou, H., Zhou, J., and Won, J.-H. (2024+). DistStat.jl: Towards unified programming for high-performance statistical computing environments in Julia. Under revision. [Arxiv preprint] [Julia code]

Conference Abstracts

Ko, S., Sobel, E., Zhou, H., and Lange, K., Unsupervised Learning of Ancestry Informative Markers and Genetic Admixture Coefficients From Biobank Data. In Annual Meeting of American Society of Human Genetics 2022, Los Angeles, CA, USA, October 25-29, 2022.
Ko, S., Lee, J., Won, J.-H., and Lee, J., Fast and accurate reconstruction for susceptibility source separation in QSM. In Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, France, June 16-21, 2018.
Bhimji, W., Racah, E., Ko, S., Sadowski, P., Tull, C., and Oh, S.-Y., Exploring Raw HEP Data using Deep Neural Networks at NERSC. In 38th International Conference on High Energy Physics, Chicago, IL, USA, August 3-10, 2017.
Ko, S., Yu, D., and Won, J.-H., A feature-splitting distributed algorithm for generalized linear models under generalized and group lasso penalties. In 9th International Conference of the ERCIM Working Group on Computational and Methodological Statistics, Seville, Spain, December 9-11, 2016.
Lee, H. B., Han, W., Ko, S., Kim, M. S., Lim. S., Lee, K. M., Kang, Y. J., Han, J. H., Kim, Y., Yoo, T. K., Moon, H. G., Noh, D. Y., and Kim, S., Identification of ESR splice variants associated with prognosis in estrogen receptor positive breast cancer. In Thirth-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium, San Antonio, TX, USA, December 8-12, 2015.