I am a lecturer in statistics and data-centric engineering in the Statistics section at Imperial College London. I am also a group leader for the Data Centric Engineering Programme at the Alan Turing Institute. My research interests like at the interface of applied probability, computational statistics and machine learning, with a particular focus on industrial applications. I've worked on application areas ranging from cellular biology, chemical engineering, predictive health management for complex engineering systems, aerospace and energy.
My personal web-page can be found here: http://wwwf.imperial.ac.uk/~aduncan/
et al., Measuring sample quality with diffusions, Annals of Applied Probability, ISSN:1050-5164
et al., 2018, Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains, Statistics & Probability Letters, Vol:136, ISSN:0167-7152, Pages:148-154
Duncan AB, Nusken N, Pavliotis GA, 2017, Using perturbed underdamped langevin dynamics to efficiently sample from probability distributions, Journal of Statistical Physics, Vol:169, ISSN:1572-9613, Pages:1098-1131
Bierkens J, Duncan A, 2017, LIMIT THEOREMS FOR THE ZIG-ZAG PROCESS, Advances in Applied Probability, Vol:49, ISSN:0001-8678, Pages:791-825