I am a senior 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., 2023, Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning, Computer-aided Civil and Infrastructure Engineering, Vol:38, ISSN:1093-9687, Pages:821-848
Duncan AB, Duong MH, Pavliotis GA, 2023, Brownian motion in an N-scale periodic potential, Journal of Statistical Physics, Vol:190, ISSN:0022-4715, Pages:1-34
et al., 2022, Bayesian assessments of aeroengine performance with transfer learning, Data-centric Engineering, Vol:3, ISSN:2632-6736, Pages:e29-1-e29-30
et al., 2022, A probabilistic model for quantifying uncertainty in the failure assessment diagram, Structural Safety, Vol:99, ISSN:0167-4730
Liu X, Duncan A, Gandy A, Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy, Fortieth International Conference on Machine Learning