Yuansi Chen, Assistant Professor of Statistical Science at Duke University, has been awarded a prestigious National Science Foundation Faculty Early Career Development (CAREER) Award. The award supports outstanding young faculty members in their efforts to build a successful research enterprise.
For the next five years, the NSF CAREER award will support Chen's work on the analysis of Markov Chain Monte Carlo (MCMC) sampling algorithms that arise in high dimensional Bayesian statistical inference problems. Drawing samples from a distribution is a core computational challenge in fields such as Bayesian statistics, machine learning, statistical physics, and many other areas involving stochastic models. While many MCMC algorithms are widely used since the foundational work of Metropolis et al. in 1953, many convergence properties of algorithms used in practice are not well understood. Practitioners in Bayesian statistics are often faced with a series of key challenges to be addressed rigorously: the choice of algorithm hyper-parameters, the estimated computational cost and the choice of the best algorithm, etc. This project will address research problems centered around these pressing issues by laying the theoretical foundations in high dimension geometry and probability, and by providing rigorous analysis of MCMC algorithms on standardized scenarios. Additionally, the outcome will also contain precise guidelines with code examples to speed up and improve existing software implementation, meeting the urgent needs for scalable high dimensional inference.
Statistical Science
