Xia Wang

Xia Wang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, specializing in Bayesian methodology, categorical data analysis, and scalable modeling of high-dimensional data. She holds dual Ph.D.s in Statistics (2009) and Economics (2007) from the University of Connecticut, followed by postdoctoral training at the National Institute of Statistical Sciences (NISS) from 2009 to 2011.

Education:

• Ph.D. in Statistics, University of Connecticut, 2009
• Ph.D. in Economics, University of Connecticut, 2007

Research Interests:

• Bayesian methodology and computation
• Categorical data analysis
• Scalable modeling for complex data
• Applications in genomics, proteomics, climate science, and biomedical studies

Grants & Collaborations:

• NIH Grant (R01MH119814): Collaborator in studying stress resilience via neurocircuit mechanisms (2019–2024)

Students & Advising:

• Supervised 5 doctoral students (e.g., Leo Duan, Dan Li, Weizhe Su) and multiple undergraduates
• Research focuses on Bayesian nonparametric methods, survival analysis, and machine learning applications

Labs & Teams: Active collaborations with the CCHMC (Cincinnati Children’s Hospital Medical Center) and NISS, with interdisciplinary projects in proteomics, climate modeling, and healthcare analytics.