Mi-Ok Kim

Dr. Mi-Ok Kim is a Professor in the Department of Epidemiology and Biostatistics at the University of California San Francisco (UCSF) and serves as the Director of the Biostatistics Core at the Helen Diller Family Comprehensive Cancer Center (HDFCCC). She joined UCSF from Cincinnati Children’s Hospital Medical Center, where she led the Biostatistics Unit for the Cancer and Blood Diseases Institute. Her role involves leading strategic development of shared biostatistical resources and providing expert statistical support across basic, clinical, and population sciences research.

Dr. Kim earned her MS (2000) and PhD (2003) in Statistics from the University of Illinois at Urbana-Champaign. Her research program centers on methodological advancements in biostatistics, including non- and semi-parametric inference, longitudinal and survival data analysis, and causal inference using structured data from registries, network databases, and electronic health records. She has a strong focus on comparative effectiveness research (CER) and patient-centered outcomes research (PCOR), particularly in the context of hierarchical data structures.

Her recent publications reflect a broad interdisciplinary reach, spanning oncology, pediatrics, public health, and machine learning applications in healthcare. Themes include health disparities, clinical trial design, biomarker analysis, and statistical methods for integrating aggregate and individual-level data. She has published in high-impact journals such as JAMA Dermatology, Annals of Applied Statistics, and Pediatrics.

Dr. Kim has received multiple honors and awards, including:

• Fellowship, University of Illinois at Urbana-Champaign (1997)
• First Prize, Capital One Financial Data Challenge Competition (2001)
• Norton Prize, Robert Bohrer Workshop for Student Papers in Statistics (2002)
• Junior Faculty Travel Award, International Conference on Robust Statistics (2004)
• Travel Award, AACR Cancer Biostatistics Workshop (2008)

She has served as Principal Investigator on numerous grants from NIH, NSF, and PCORI, supporting research in areas such as pediatric Crohn’s disease, kidney transplantation, and meta-analysis methods. Her work emphasizes rigorous statistical methodology to reduce bias and improve efficiency in real-world data analysis. She collaborates extensively with multidisciplinary teams across UCSF and beyond, contributing to clinical studies, protocol development, and outcomes research.

Dr. Kim leads a research team focused on optimal handling of complex data structures in treatment selection and outcome analysis, with applications in chronic disease management and cancer care. She is also engaged in developing frameworks for post-market monitoring of machine learning-based medical devices, reflecting her forward-looking approach to biostatistical innovation.