معرفی
Hang Joon Kim is a Professor in the Division of Statistics and Data Science within the Department of Mathematical Sciences at the University of Cincinnati's College of Arts and Sciences. He joined the university in 2015 as an Assistant Professor, was promoted to Associate Professor in 2021, and became a full Professor in 2025. His research spans multiple areas of statistics with applications in various scientific domains.
Dr. Kim received his educational training from prestigious institutions:
- Ph.D. in Statistics from The Ohio State University, Columbus, OH
- M.S. in Applied Statistics from Yonsei University, Seoul, Korea
- B.A. in Applied Statistics from Yonsei University, Seoul, Korea
- B.A. in Business from Yonsei University, Seoul, Korea
Dr. Kim's research focuses on advanced statistical methodologies with particular emphasis on Semiparametric Bayesian modeling, Causal inference, Meta analysis, Statistical genomics, Missing data, Survey sampling, Data privacy, and Synthetic data generation. His work bridges theoretical statistics with practical applications in biomedical research, official statistics, and data science. He has developed innovative approaches for handling complex data structures while maintaining statistical rigor and practical utility.
His recent publications demonstrate a strong trend toward integrating multiple data sources while addressing privacy concerns. The 15 most recent articles show increasing focus on Bayesian methods for causal inference, synthetic data generation for privacy protection, and statistical approaches for genomic studies, reflecting his leadership at the intersection of statistical theory and real-world applications.
Dr. Kim has received numerous prestigious awards and honors:
- Industrial Service Medal of Honor from the President of the Republic of Korea (Sep 2024)
- A&S Rising Star Award from the College of Arts and Sciences, University of Cincinnati (Apr 2018)
- Summer at Census Scholar from the U.S. Census Bureau (June 2017)
- KISS Career Development Award from the Korean International Statistical Society (Aug 2015)
- American Statistical Association Student Paper Award (Aug 2012)
As an advisor, Dr. Kim has successfully mentored multiple PhD students to completion, with graduates securing academic and industry positions. His current research is supported by significant grants including an NSF-funded project on "Robust and efficient Bayesian inference for misspecified and underspecified models" (2024-2027) and a National Research Foundation of Korea project on "Statistically synthetic data generation" (2025-2028). He has served as PI on multiple substantial research projects totaling over $400,000 in funding.
Dr. Kim leads research efforts in statistical methodology development, particularly in Bayesian modeling and data privacy. He has developed several R packages including synMicrodata for synthetic data generation, GGPA for genomic analysis, clusterMI for clustering with missing data, and DPImputeCont for imputation of continuous data. His work has practical applications in official statistics, biomedical research, and data science.
