About

I am Shijie Wang, currenlty a fifth-year Ph.D. student in the Department of Statistics at University of South Carolina under supervision of Prof. Ray Bai. My primary research focuses on utilizing deep learning to address, improve and facitilite statistical inference in the following classcial statistics models:

  • Bootstrap uncertainty quantification
  • Nonparametric conditional density estimation and nonparametric multi-quantile estimation
  • Emprical Bayes and nonparameteric maximum likelihood estimation

Education

Ph.D. in Statistics, University of South Carolina, South Carolina, 2019.08 - Present.

  • Dissertation title: New Deep Learning Approaches to Classical Statistical Problems
  • Winner of James D. Lynch Graduate Student Research Award

B.S. in Applied Statistics, Shanghai University of International Business and Economics, Shanghai, 2015.09 - 2019.06.

Publications and Preprints

  • Shin, M., Wang, S. and Liu, J. (2023). Generative Multi-purpose Sampler for Weighted M-estimation. [Paper][R package]

    Journal of Computational and Graphical Statistics, In press.

  • Wang, S., Shin, M., and Bai, R. (2024+). Generative quantile regression with variability penalty. [Preprint][Code]

    Under revision.

  • Wang, S., Chakraborty, S., Qin, Q., and Bai, R. (2024+). A comprehensive deep generative framework for mixing density estimation. [R package]
  • Wang, S., Shin, M., and Bai, R. (2024+). Fast boostrapping nonparametric maximum likelihood for latent mixture models. [R package]

Experience

  • Machine Learning Data Scientist Intern, Bayer AG, Saint Louis, MO, 2023.05 - 2023.08.
  • Deep Learning Research Assistant, University of South Carolina, Columbia, 2022.05 - 2023.05.
  • Machine Learning Data Scientist Intern, Movefin Tech, Shanghai, 2019.03 - 2019.06.