Andi Q. Wang

Andi Q. Wang is an Assistant Professor in the Department of Statistics at the University of Warwick. He holds roles including First Year Tutor and Programme Manager of APTS (with Paul Chleboun). His research focuses on computational statistics and applied probability, particularly MCMC methods, including subgeometric convergence and nonreversible methods. He previously conducted postdoctoral research at the University of Bristol and completed his DPhil at the University of Oxford under Professors David Steinsaltz and Gareth Roberts, with a prior MMath from the University of Cambridge.

Education:

• 2015-2019: DPhil (PhD) in Statistics, University of Oxford (OxWaSP CDT)
• 2011-2015: MMath (Master of Mathematics), University of Cambridge

Research Interests:

• Monte Carlo methods, Bayesian inference, quasi-stationarity
• Scalable and adaptive computational methods
• Nonreversible processes and MCMC convergence theory

His work emphasizes theoretical foundations of MCMC, including convergence analysis and algorithmic innovation.

Key Research Trends: His recent articles (2023-2025) explore explicit convergence bounds for Metropolis chains, weak Poincaré inequalities, and adaptive sampling techniques. These reflect a focus on advancing MCMC methodology through rigorous probabilistic analysis and algorithmic optimization.

Teaching & Engagement:

• Teaches ST921/ST912: Comparisons of Markov Chains
• Active in academic service, including organizing seminars and workshops