Summary
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/
Publications
Journals
Seshadri P, Duncan A, Simpson D, et al., 2020, Spatial flow-field approximation using few thermodynamic measurements Part II: Uncertainty assessments, Journal of Turbomachinery
Seshadri P, Simpson D, Thorne G, et al., 2020, Spatial flow-field approximation using few thermodynamic measurements Part I: formulation and area averaging, Journal of Turbomachinery, ISSN:0889-504X
Gorham J, Duncan A, Vollmer S, et al., Measuring sample quality with diffusions, Annals of Applied Probability, ISSN:1050-5164
Conference
Barp A, Briol FX, Duncan A, et al., 2019, Minimum Stein discrepancy estimators, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Neural Information Processing Systems Foundation, Inc.