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/
Yatsyshin P, Kalliadasis S, Duncan AB, 2022, Physics-constrained Bayesian inference of state functions in classical density-functional theory, Journal of Chemical Physics, Vol:156, ISSN:0021-9606, Pages:074105-1-074105-10
et al., 2021, Polynomial ridge flowfield estimation, Physics of Fluids, Vol:33, ISSN:1070-6631
Cockayne J, Duncan A, 2021, Probabilistic gradients for fast calibration of differential equation models, Siam/asa Journal on Uncertainty Quantification, Vol:9, 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