Yunro Chung

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 

Google Scholar

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. Chungs 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.

Courses

Fall 2020
Course Number Course Title
BMI 484 Internship
BMI 515 App Biostats Med & Informatics
BMI 560 Teachng Biomedical Informatics
BMI 584 Internship
BMI 590 Reading and Conference
BMI 593 Applied Project
BMI 790 Reading and Conference
BMI 792 Research
Summer 2020
Course Number Course Title
BMI 482 Capstone I
BMI 483 Capstone II
BMI 584 Internship
BMI 792 Research
Spring 2020
Course Number Course Title
BMI 211 Modeling Biomedical Decisions
BMI 482 Capstone I
BMI 483 Capstone II
BMI 584 Internship
BMI 593 Applied Project
BMI 790 Reading and Conference
BMI 792 Research
Fall 2019
Course Number Course Title
HCD 300 Biostatistics
CHS 394 Special Topics
BMI 484 Internship
BMI 560 Teachng Biomedical Informatics
BMI 584 Internship
BMI 590 Reading and Conference
BMI 593 Applied Project
BMI 790 Reading and Conference
BMI 792 Research
Summer 2019
Course Number Course Title
BMI 484 Internship
BMI 584 Internship
BMI 792 Research
Spring 2019
Course Number Course Title
BMI 211 Modeling Biomedical Decisions
BMI 593 Applied Project