Yunro Chung

Asst Professor
DTPHX Campus
Faculty Member
DTPHX Campus


Yunro Chung is an assistant professor with a joint appointment in the College of Health Solutions and Biodesign Center for Personalized Diagnostics (CPD) at the Arizona State University (ASU). His research uses statistical analysis and machine learning for biomedical studies, including clinical trials, laboratory experiments and genomics researches. In particular, he collaborates with other researchers at CPD focusing on discovering and validating novel biomarkers that lead to better screening and early diagnosis of disease.

He received his PhD in Biostatistics from the University of North Carolina at Chapel Hill and his MS and BS in Statistics from the Chung-Ang University (South Korea). Prior to joining ASU, he was a postdoctoral research fellow at the Fred Hutchinson Cancer Research Center, where he developed statistical methods for a recent active surveillance study for prostate cancer. He had industrial experience at Novartis oncology, SAS Institute and Korea Food and Drug Administration.

Google Scholar

Research Interests

I. Statistical Analysis for Biomarker Discovery and Validation

Biomarkers play an important role in early detection of disease and clinical decision-making process. In particular, recent advances in genomics, molecular biology and imaging technologies promise to seek potential biomarkers that could be non-invasive, cost-effective and accurate. Dr. Chungs research has been focused on development of statistical methods for evaluating such biomarkers in various studies including:

  • cross-sectional study with high-dimensional biomarkers for personalized medicine;
  • disease surveillance study with longitudinal biomarkers and/or censored time-to-event outcome;
  • two-phase biomarker study in the presence of surrogate biomarkers.

II. Shape-Restricted Hazard Analysis

Isotonic regression is a useful nonparametric technique for fitting a monotone increasing (or decreasing) function. It offers a flexible tool in estimating a monotone regression relationship between response and covariate. His research applies the isotonic regression techniques to survival data under a natural assumption that the hazard function is a monotone function of one of the covariates. Specifically, a monotone function is incorporated to Cox's proportional hazards model, where it captures nonlinear and monotone covariate effects without specifying a baseline hazard function. His current research project includes

  • Unimodal (or U-shaped) hazard function where the hazard function is monotone increasing and decreasing over a mode, i.e. estimation of both unimodal hazard and mode are of interest;
  • Estimation of monotone hazard function in multiple covariates.


Spring 2019
Course NumberCourse Title
BMI 211Modeling Biomedical Decisions