Digital twins workshop
Thursday 15 Jul 2021
Time: 10:00 - 14:30
Bio
Andrew Duncan is Director of Science & Innovation (Fundamental Research in DS and AI) at The Alan Turing Institute.
He is a senior lecturer in Statistics at Imperial College London. He obtained a PhD in Applied Mathematics at Warwick before working in Mathematical Modelling at Oxford and then Computational Statistics at Imperial College. Until recently, he held a joint position as Principal/Lead Scientist position at Improbable Defence and Enterprise Insights where he led research initiatives at the intersection of digital twins and AI, with applications to problems in government, national security, energy and logistics and supply chains.
Andrew's research has spanned broadly across fundamental data science, machine learning and AI, seeking to leverage theory and tools from probability theory and partial differential equations to answer challenging questions in statistical machine learning and AI. He also has a keen interest in industrial collaboration.
Over the last decade, his team has collaborated across a diverse range of UK companies and institutions, working with them to harness principled Stats/ML/AI methods to address their most challenging problems.
Research interests
Andrew's interests lie generally within the intersection between computation, analysis and probability, with a particular focus on applications in biology and chemistry, particularly systems involving stochasticity and/or multiple scales. These include the analysis and construction of MCMC-based methods for sampling from probability distributions, coarse graining of stochastic models involving multiple scales, classical and stochastic homogenisation of PDEs and SDEs, and the Bayesian formulation of inverse problems.
