Qian Qin

Qian Qin is an Assistant Professor in the Department of Statistics at the University of Minnesota (Twin Cities). Their research focuses on Bayesian statistics and computational methods, particularly Markov Chain Monte Carlo (MCMC) algorithms and data augmentation techniques.

Research interests include:

• Convergence analysis of MCMC samplers
• Bayesian robust multivariate linear regression
• Hierarchical structure-based sampling algorithms
• Statistical computing efficiency
• Trans-dimensional MCMC methods
• Random-scan Gibbs samplers

Recent research outputs (2018-2024) demonstrate expertise in improving convergence rates for Bayesian computation. Key collaborations include work with Jones, G. L. and Wang, G.

Current projects include the NSF-funded initiative Large Sample Analysis of MCMC in Bayesian Statistics from a Frequentist Perspective (2021-2025). No formal awards or honors are currently listed in the provided materials.