Youngseok Song

Youngseok Song

Assistant Professor in the Department of Statistics

West Virginia University

Biography

Youngseok Song is an assistant professor of the Department of Statistics at West Virginia University (WVU). His research interests are high-dimensional statistic, graphical model, large-scale inferences, network analysis, robust statistics and learning, and statistical genomics. Prior to joining WVU, he was a post-doctoral researcher in Statistical Data Science group at the École polytechnique fédérale de Lausanne (EPFL), working with Prof. Sofia C. Olhede. He received his Ph.D. in Statistics from Colorado State University under the supervision of Prof. Wen Zhou.

Interests
  • Graphical modeling
  • High-dimensional inference
  • Network analysis
  • Robust statistics & robust learning
  • Statistical genomics
Education

  • PhD in Statistics, 2021

    Colorado State University

  • MS in Statistics, 2017

    Colorado State University

  • MS in Mathematics, 2010

    POSTECH

  • BS in Mathematics, 2008

    POSTECH

Academic Position

 
 
 
 
 
Assistant Professor
West Virginia University
Aug 2023 – Present Morgantown, WV, USA
 
 
 
 
 
Collaborateur scientifique / Post-doc
EPFL
Sep 2021 – Jul 2023 Lausanne, Switzerland

Publication

Youngseok Song, Wen Zhou, Wen-Xin Zhou (2023). Large-Scale Inference of Multivariate Regression for Heavy-Tailed and Asymmetric Data. Statistica Sinica.

Cite DOI

E.K. Fischer, Youngseok Song, K.A. Hughes, W. Zhou, K. Hoke (2021). Nonparallel transcriptional divergence during parallel adaptation. Molecular Ecology.

Cite DOI

Software

R packages

  • nethist: Network histogram, a nonparametric graphon estimation

Teaching

West Virginia University

  • STAT 215, Introduction to Probability and Statistics: F23
  • STAT 512, Statistical Methods 2, S24

Colorado State University

  • STAT 301, Introduction to Statistical Methods: S20
  • STAT 307, Introduction to Biostatistics: F18, Sm20
  • STAT 472, Statistical Research: Design, Data & Methods: S19, F19, F20, S21
  • STAA 566, Statistical Consulting: Sm21

For a full list, see CV.

Recent Posts

Cartoon by EPFL & UNIL students about misleading data visualization
I would like to introduce a cartoon created by two students from EPFL and one student from UNIL. This is a part of their semester project for the critical data studies class.
Last updated on Aug 19, 2022 1 min read Data visualization, Science communication
Sprase Matrix objects in R
In this post, we take a look sparse matrix objects in R in terms of object size. If most of entries in a matrix are 0, we call the matrix is sparse.
Last updated on Jun 16, 2022 2 min read R
Profiling R code: microbenchmark vs bench
Wickham (2019) is a nice book if you want to learn more about R programming. In Chapter 24, it covers R codes profiling. For profiling codes, the author uses bench package, which I have never used.
Last updated on Nov 1, 2021 3 min read R
Generating multivariate normal samples
Generating multivariate normal sample is important for simulation studies. MASS::mvrnorm() is one of the commonly used functions to generate such random sample (Venables and Ripley 2002). However, it is often ignored that the function is not the fastest way due to error handling parts.
Last updated on Nov 1, 2021 7 min read R

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