Profile

Jaeyoung Park is an Assistant Professor in the School of Global Health Management and Informatics. His research focuses on developing novel statistical methodologies to address challenges in conventional approaches for high-dimensional, multi-site data, with particular relevance to health services research and population health. His work aims to bridge methodological innovation and practical application, enabling more reliable and scalable analysis of complex healthcare data.

Dr. Park has an active publication record in peer-reviewed journals and continues to contribute to interdisciplinary research at the intersection of statistics, health informatics, and healthcare management. His scholarly interests include optimization, data-driven decision-making, and advanced statistical modeling, with an emphasis on improving methodological rigor in applied health research.

In addition to his research, Dr. Park is dedicated to teaching and mentoring graduate students, particularly in quantitative methods, optimization, and data analytics within healthcare contexts. He has taught courses in the Master of Healthcare Administration program, emphasizing evidence-based decision-making in healthcare to train the next generation of health analytics professionals.

• Health Informatics
• Statistical methods
• Multi-site data analysis

• PhD in Industrial and Systems Engineering, University of Florida, 2022
• MS in Industrial Engineering, Hongik University, 2018
• BS in Industrial Engineering, Hongik University, 2016

My research focuses on developing novel statistical and optimization methodologies to address limitations of conventional approaches for analyzing high-dimensional, multi-site data, with a particular emphasis on healthcare and population health applications. Modern health data are increasingly complex, heterogeneous, and distributed across institutions, creating challenges such as site-specific bias, limited sample sizes, and violations of standard modeling assumptions. My work aims to provide principled, scalable solutions to these challenges while maintaining interpretability and practical relevance.

A central theme of my research is the development of robust and efficient methods for integrating information across multiple sites without relying on unrealistic homogeneity assumptions. I am especially interested in penalized and constrained optimization frameworks, distributed and federated learning settings, and methods that balance statistical efficiency with data privacy and computational feasibility. These methodological contributions are motivated by real-world problems in health services research, including hospital performance evaluation, quality measurement, and resource allocation.

My research is highly interdisciplinary, drawing from statistics, operations research, and health informatics, and is grounded in close collaboration with domain experts. In parallel with methodological development, I emphasize rigorous theoretical properties and extensive empirical validation using both simulated and real-world healthcare datasets.

Looking ahead, my research agenda aims to advance data-driven decision-making in healthcare systems by creating statistically sound tools that can be readily adopted by practitioners and policymakers, ultimately contributing to more efficient and evidence-based healthcare delivery.

• Machine learning & Deep learning
• Missing data
• Causal inference
• Transfer learning
• Federated learning

• HSA 6911: Scientific Inquiry in the Health Profession
• HSA 5198: Health Care Decision Sciences and Knowledge Management