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About Hojin
Hojin Moon is a Professor in the Department of Mathematics and Statistics at California State University, Long Beach, contributing to the Mathematics faculty through his expertise in applied statistics. He holds a Ph.D. in Statistics from the State University of New York at Stony Brook. Prior to joining CSULB in August 2007, Moon served as a Mathematical Statistician at the U.S. Food and Drug Administration from March 2005 to August 2007. His research focuses on statistical learning algorithms for data science, classification by ensembles from random partitions, and discovery and validation of genomic and genetic biomarkers for life sciences. Key areas include biomedical statistics, decision-making algorithms, risk analysis, predictive modeling for fraudulent claims detection, microbial risk assessment, dose-response modeling, and wound score development for diabetic foot ulcers. Moon is actively involved in the Applied Statistics Graduate Program, where he teaches and mentors students, and provides statistical consulting services through the CSULB Graduate Center Statistical Consulting Program as Professor of Applied Statistics.
Moon has published extensively in peer-reviewed journals, with over 1,380 citations as of recent records. Selected key publications include "Prediction of Treatment Recommendations Via Ensemble Machine Learning" (2024, Evolutionary Bioinformatics), "Prognostic Genomic Predictive Biomarkers for Early-Stage Lung Cancer Patients" (2021), "Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine" (2021), "Statistical algorithms for clinical decisions and prevention of genetic-related heart disease" (2020), "A Predictive Modeling for Detecting Fraudulent Automobile Insurance Claims" (2019), "Clinical Applications and Validation of an Innovative Wound Score" (2018), and "Subgroup Analysis Based on Prognostic and Predictive Gene Signatures for Adjuvant Chemotherapy in Early-Stage Non-Small-Cell Lung Cancer Patients" (2017). His work emphasizes ensemble methods for high-dimensional biomedical data, genomic signatures for personalized medicine, particularly in lung cancer and heart disease prevention, and advanced machine learning applications such as clustering, data mining, and high-dimensional data analysis. Moon also serves on university committees, including the Committee on Athletics, and has received research support through programs like ASCEND for projects on genomic biomarkers.
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