Yunro (Roy) Chung

Asst Professor
Faculty
DTPHX Campus
Mailcode
9020
Faculty Member
Faculty
DTPHX Campus
Mailcode
9020

Biography

Yunro (Roy) Chung is an assistant professor at the Arizona State University (ASU) with a joint appointment in the College of Health Solutions and Biodesign Center for Personalized Diagnostics (CPD). He is a biostatistician working with an interdisciplinary team at CPD. One of his research interests is to use statistics and machine learning algorithms to discover novel biomarkers using Nucleic Acid-Programmable Protein Array (NAPPA) that lead to better screening and early diagnosis of disease. He is also interested in developing statistical and computational methods for heterogeneous cancer survival data.

He received his PhD in Biostatistics from the University of North Carolina at Chapel Hill and his MS and BS in Statistics from Chung-Ang University (South Korea). He provided statistical consulting, power analysis and data analysis for many projects including clinical trials, laboratory experiments and genomics researches. Prior to joining ASU, he was a postdoctoral research fellow at the Fred Hutchinson Cancer Research Center, where he studied an active surveillance study for prostate cancer.

News

DARPA grants ASU up to $38.8 million to create epigenetic tool for fight against weapons of mass destruction

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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.

Courses

Fall 2019
Course NumberCourse Title
HCD 300Biostatistics
CHS 394Special Topics
BMI 484Internship
BMI 560Teachng Biomedical Informatics
BMI 584Internship
BMI 590Reading and Conference
BMI 593Applied Project
BMI 790Reading and Conference
BMI 792Research
Summer 2019
Course NumberCourse Title
BMI 484Internship
BMI 584Internship
BMI 792Research
Spring 2019
Course NumberCourse Title
BMI 211Modeling Biomedical Decisions
BMI 593Applied Project