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Yunro (Roy) Chung

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

Yunro Chung is an assistant professor at the Arizona State University with a joint appointment in the College of Health Solutions and Biodesign Center for Personalized Diagnostics. He joined the Arizona State University in 2018, after graduating from the University of North Carolina at Chapel Hill and completing post-doctoral fellow at the Fred Hutchinson Cancer Research Center, Seattle, WA.

His research is to use statistics and machine learning to discover novel biomarkers that lead to better screening and early diagnosis of disease. His methodological expertise includes clinical trials, survival data analysis, and evaluation of medical diagnostic test. He collaborates with biologists, bioinformaticians and clinicians providing statistical consulting and analysis for NAPPA protein array data as well as laboratory or clinical data. Since 2019, he has been a co-investigator on DARPA project (DARPA grants ASU up to $38.8 million to create epigenetic tool for fight against weapons of mass destruction).

Education
  • Ph.D. Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
  • M.S. Statistics, Chung-Ang University, South Korea
  • B.S. Statistics, Chung-Ang University, South Korea 
Research Interests

I. ROC 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 allows us to identify tons of biomarkers simulataneously. Dr. Chung’s research has been focused on using the receiver operating characteristic (ROC) curve for evaluating such biomarkers in various studies including:

  • cross-sectional study with high-dimensional biomarkers;
  • disease surveillance study with longitudinal biomarkers;
  • two-phase biomarker discovery and validation study.

II. Shape-Restricted Hazard Analysis for Surviva Data Anslysis

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 Cox's proporiontal hazards model under a natural assumption that the hazard function is a monotone function of one of the covariates. His current research projects include estimations of unimodal (or U-shaped) hazard function where the hazard function is monotone increasing and decreasing over a mode.

Spring 2020
Course NumberCourse Title
BMI 211Modeling Biomedical Decisions
BMI 482Capstone I
BMI 483Capstone II
BMI 584Internship
BMI 593Applied Project
BMI 790Reading and Conference
BMI 792Research
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