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Welcome to Youngjin Cho’s Homepage

I am a Ph.D. candidate in Statistics at Virginia Tech. My research interests include:

  • Smoothing Splines: I conduct research on smoothing spline ANOVA models, specifically concentrating on (1) developing models for survival data and (2) exploring statistical inference, including effect-wise hypothesis testing.
  • Functional Data Analysis: I take an application-driven approach to functional datasets. A key focus is battery discharge curve data, representing longitudinal functional data with a heterogeneous domain.
  • Survival Analysis: I focus on the accurate computation of partial likelihood, including computational, methodological, and theoretical development, with a particular emphasis on its application to heavily tied data.
  • High-Dimensional Statistics: I work on variable selection for datasets with multivariate responses.
  • Collaborative Research: I am also involved in collaborative projects in social science.

To contact me, the best way is to email youngjin@vt.edu. For more details, see my Curriculum Vitae. You can also visit my Google Scholar Profile.

Youngjin Cho

Education

  • Ph.D. Candidate in Statistics, Virginia Tech (2020 - )
    Advisor: Dr. Pang Du (Professor, Virginia Tech)
    Co-advisor: Dr. Yili Hong (Professor, Virginia Tech)

  • M.S. in Statistics, Sungkyunkwan University (2020)
    Advisor: Dr. Seyoung Park (Associate Professor, Yonsei University)

  • B.Ec. in Statistics, Sungkyunkwan University (2017)

Publications

Journal Articles

  • Cho, Y., Hong, Y., and Du, P. (2025), An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions, Computational Statistics & Data Analysis, Vol.208, 108161. [doi] [pdf]
  • Cho, Y., Do, Q., Du, P., and Hong, Y. (2024), Reliability Study of Battery Lives: A Functional Degradation Analysis Approach, Annals of Applied Statistics, Vol.18, No.4, 3185-3204. [doi] [pdf]
  • Cho, Y. and Park, S. (2022), Multivariate Response Regression with Low-Rank and Generalized Sparsity, Journal of the Korean Statistical Society, Vol.51, 847-867. [doi]

Papers in Progress / Under Review

  • Effect-Wise Local and Global Inference in Smoothing Spline ANOVA.
  • Cho, Y. and Du, P. (2025+), Smoothing Spline Competing Risk Cox Model.
  • Lin, Z., Cho, Y., Du, P., and Hong, Y. (2025+), A New Approach for Computing Partial Likelihood for Cox Model Under Competing Risks Using Poisson Multinomial Distribution.
  • Sim, E., Cho, Y., and Jeong, S. (2025+), Introducing the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to Quantitatively Examine Intersectional Workplace Inequities: A Methodological Study.
  • Cho, Y., Lee, E., and Park, S. (2025+), Low-rank and Sparse Smoothed Quantile Regression Models for Multiple Responses.