Dr Andrew Duncan

Dr Andrew Duncan is an Associate Professor in Statistical Machine Learning at the Department of Mathematics, Imperial College London. His research focuses on statistical machine learning, though specific areas are not detailed in available sources. Dr Duncan's work contributes to the academic community through his teaching and research activities at the institution. His professional profile can be accessed via the provided website link.

Andrew B. Duncan is a researcher at Imperial College London specializing in Stochastic Computation, Machine Learning, and Computational Statistics. His work focuses on developing efficient sampling methods for probabilistic models, including nonreversible Langevin samplers and Stein variational gradient descent. Duncan's research also explores variance reduction techniques, diffusion-based quality measures, and scalable Monte Carlo methods. He has contributed to areas such as Bayesian inference, generative models, and statistical analysis of complex systems. His publications address challenges in sampling from probability distributions, noise-induced multistability, and physics-constrained inference. Duncan's research bridges theoretical and applied aspects of computational statistics and machine learning.