Seyoung Park

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.


Contact

  • Email: ishspsy@skku.edu

  • Office: 32311, Dasan Hall of Economics, Sungkyunkwan University


Education

  • 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.


Employment

  • 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

Research Interests

  • High-Dimensional Statistics

  • Machine Learning Research

  • Optimization

  • Quantile Modeling

  • Model Selection


Research Papers

  • 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

  • Won, H., Park, S.* (2021) Mean-shortfall optimization problem with perturbation methods. The Korean Journal of Applied Statistics, Vol. 34, No. 1, 39-68.

  • Tang, D., Park, S.^, and Zhao, H. (2020). NITUMID: NMF-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics, Vol. 36, No. 5, 1344-1350.

  • Park, M., Park, S.* (2020) One-step spectral clustering of weighted variables on single-cell RNA-sequencing data. The Korean Journal of Applied Statistics, Vol. 33, No. 4, 511-526.

  • 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

  • Youngjin Cho

  • Minyoung Park

  • Hyunjin Kim

  • Hayeon Won

  • Sohyeon Kim


Grants

  • 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


Teaching

  • 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.