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/
Cockayne J, Duncan A, 2021, Probabilistic gradients for fast calibration of differential equation models, Siam/asa Journal on Uncertainty Quantification, ISSN:2166-2525
et al., 2021, The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study., Bmc Medicine, Vol:19, ISSN:1741-7015, Pages:1-12
et al., 2021, Hospital bed capacity and usage across secondary healthcare providers in England during the first wave of the COVID-19 pandemic: a descriptive analysis, Bmj Open, Vol:11, ISSN:2044-6055, Pages:1-9
et al., 2020, Manifold learning for accelerating coarse-grained optimization, Journal of Computational Dynamics, Vol:7, ISSN:2158-2505, Pages:511-536
et al., 2020, The blending region hybrid framework for the simulation of stochastic reaction–diffusion processes, Journal of the Royal Society Interface, Vol:17, ISSN:1742-5689, Pages:1-19