معرفی
Ran Dai, PhD, MS, is an Assistant Professor in the Department of Biostatistics at the University of Nebraska Medical Center (UNMC) College of Public Health. She joined UNMC in 2020. Her research focuses on high-dimensional statistical inference, nonparametric methods, shape-constrained regression, and machine learning applications in clinical trials, drug development, and public health. She has expertise in false discovery rate (FDR) control and collaborates on projects ranging from biostatistics to geology.
Education:
- PhD in Statistics, University of Chicago (2020)
- MS in Statistics, University of Chicago (2016)
- PhD in Medicinal Chemistry, University of Minnesota (2015)
- BS in Pharmaceutical Sciences, Peking University (2009)
Research Interests: Dr. Dai’s work emphasizes statistical methodologies for high-dimensional data analysis, particularly in healthcare and environmental contexts. She applies machine learning to understand complex biological systems and public health trends. Recent projects include modeling post-transplant diabetes risks, analyzing electronic health records disparities, and exploring bioturbation in geological data.
Articles Trends: Her recent publications highlight advancements in statistical methods for causal inference, mediator selection, and FDR control. Notably, she has investigated the impact of vaccinations and breakthrough infections in solid organ transplant recipients during the pandemic. Her work bridges theoretical statistics and applied problems in medicine and environmental science.
Advising & Grants: While no advisees are listed, Dr. Dai collaborates with multidisciplinary teams on NIH-funded projects and contributes to initiatives like the National COVID Cohort Collaborative (N3C). Her expertise in statistical design supports translational research and policy decisions.
Labs & Teams: She is affiliated with the UNMC Department of Biostatistics, contributing to collaborative efforts in precision medicine and public health data science.
