I am an assistant professor at the Department of Statistics, Sungkyunkwan University. I have a broad research interest in methodological, theoretical, and computational research. Main research directions include High-dimensional Statistics, Machine Learning Research, Optimization, and Quantile-based Inference. The best way to contact me is through email.
Office: 32311, Dasan Hall of Economics, Sungkyunkwan University
Ph.D., Statistics (2016), University of Michigan, Ann Arbor, USA.
B.S. in Mathematics and B.E. in Industrial Engineering (2009), Yonsei University, Seoul, Korea.
Assistant Professor (Sep 2018 - Present): Department of Statistics, Sungkyunkwan University, Seoul, Korea.
Postdoctoral Associate (August, 2016 - July, 2018): Biostatistics Department, Yale University, New Haven, USA.
Supervisor : Hongyu Zhao
Machine Learning Research
Lee, E., Cho, J., and Park, S.* (2021+). Penalized kernel quantile regression for varying coefficient models. Journal of Statistical Planning and Inference. In Press.
Park, S. and Lee, E. (2021). Hypothesis testing of varying coefficients for regional quantiles. Computational Statistics and Data Analysis, Vol. 159, 107204.
Lee, E. and Park, S.^ (2021). Poisson reduced-rank models with sparse loadings. Journal of the Korean Statistical Society, In Press.
Park, S. and Zhao, H. (2021). Integrating multidimensional data for clustering analysis with applications to cancer patient data. Journal of the American Statistical Association, Vol. 116, No.533, 14-26. https://doi.org/10.1080/01621459.2020.1730853
Tang, D., Park, S.^, and Zhao, H. (2020). NITUMID: NMF-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics, Vol. 36, No. 5, 1344-1350.
Park, S.*, Zhao, H. (2019). Sparse principal component analysis with missing observations. Annals of Applied Statistics, Vol.13, No.2, 1016-1042.
Park, S.* and Lee, S. (2019). Linear programming models for portfolio optimization using a benchmark. European Journal of Finance, Vol. 25, 435-457.
Park, S., Lee, E., Lee, S., and Kim,K. (2019). Dantzig type optimization method with applications to portfolio selection, Sustainability, Vol.11, 3216.
Park, S.*, Zhao, H. (2018). Spectral clustering based on learning similarity matrix. Bioinformatics, Vol. 34, No. 12, 2069-2076.
Park, S*., He, X., Zhou, S. (2017). Joint quantile regression with high dimensional covariates. Statistica Sinica, Vol. 27, No. 4, 1619-1638. (Winner of the 2015 Student Paper Competition in the ASA Section on SLDM)
Park, S.*, He, X. (2017). Hypothesis testing for regional quantiles. Journal of Statistical Planning and Inference, Vol. 191, 13-24.
Greenewald, K., Park, S., Giessing, A., Zhou, S. (2017). Time varying matrix- variate graphical models. In Advances in Neural Information Processing Systems, 30 (NIPS 2017)
* Corresponding author
^ Co-first author
Former and Current Students
National Research Foundation of Korea (NRF-2019R1C1C1003805)
Principal Investigator, 2019 - 2022
Sungkyun Research Fund 2018
Principal Investigator, 2018 - 2019
KOFAC-2019 Undergraduate Research Program
Principal Investigator, 2019
Introduction to Statistical Computing - Fall 2018, this class is partially supported by DataCamp. Students will have full access to the entire DataCamp course curriculum for the semester.
Mathematical Statistics (graduate course) - Spring 2019
Modern Statistical Theory (graduate course) - Spring 2019
Large Sample Theory (graduate course) - Spring 2020
Introduction to Statistical Programming - Fall 2019, Spring 2020, Spring 2021, this class is partially supported by DataCamp. Students will have full access to the entire DataCamp course curriculum for the semester.
Statistics and data science - Fall 2019, Fall 2020, this class is partially supported by DataCamp. Students will have full access to the entire DataCamp course curriculum for the semester.