Scholar
Andrew B. Duncan
Google Scholar ID: 3ZzC72cAAAAJ
Imperial College London
Citations & Impact
Citations
1,366
H-index
19
i10-index
27
Publications
20
Co-authors
0
Publications
Resume
Research Experience
- Associate Professor (Reader) in Statistical Machine Learning, Imperial College London
- Director of Fundamental Research in AI, The Alan Turing Institute
- Senior Research Scientist & Principal Scientist, Improbable Defence & National Security
- Group Leader, Data-Centric Engineering Programme, The Alan Turing Institute
- Lecturer in Probability & Statistics, University of Sussex
Background
- Associate Professor (Reader) in Statistical Machine Learning, Department of Mathematics, Imperial College London
- Research focuses on principled, efficient methods for generative modelling and decision-making under uncertainty, grounded in probability, optimisation, and numerical analysis
- Aims to make generative AI trustworthy, compute-efficient, and scientifically useful—models that reason with uncertainty, honour physics/engineering constraints, and perform well under realistic compute/data budgets
- Current research themes: generative modelling (diffusion/flow-matching/energy-based models, lean LLMs), efficient inference & sampling, scientific automation, and decision-making under strict compute budgets
- Applications in biochemical systems, chemical engineering, and monitoring/control of complex systems in defence, aerospace, supply chains, logistics, and energy