Home

Welcome to Youngjin Cho’s Homepage

I am currently a Ph.D. candidate in Statistics at Virginia Tech and have accepted an offer to join the Department of Mathematical Sciences at the University of Nevada, Las Vegas as a tenure-track assistant professor, starting in January 2026. My research interests include Smoothing Splines, Functional Data Analysis, Survival Analysis, and High-Dimensional Statistics. To contact me, you can 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.
  • Cho, Y., Lin, Z., Du, P., and Hong, Y. (2025+), Computing Partial Likelihood in Cox’s Model with Competing Risks and Ties: A New Approach Using the 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.