Homepage of Tom Diethe

Dr Tom Diethe is Head of The Centre for Artificial Intelligence (Executive Director) at AstraZeneca, Cambridge UK. Tom’s department sits within the Data Science & Artificial Intelligence organisation, which is part of the wider BioPharmaceuticals R&D. The team’s mission is to devise innovative products and solutions using machine learning algorithms that will make the drug discovery pipeline more efficient and aid in a better understanding of biology and medicinal chemistry. Tom is also an Honorary Research Fellow at the University of Bristol.

Tom was previously an Applied Science Manager in Amazon Research, Seattle, USA. - Cambridge, UK: – Core machine learning: Deep Probabilistic Models, Bayesian Neural Networks, Continual Learning, Streaming/Online Machine Learning – Supply Chain Optimization Technologies: Forecasting, Inventory Management, Uncertainty Quantification, Bayesian Optimization, Gaussian processes, Anomaly/Outlier Detection, Time Series Analysis – Alexa Shopping: Natural Language Processing, Privacy Preserving Machine Learning, Differential Privacy - Seattle, USA: – Machine Learning Lead for a life sciences initiative - Deep Learning, Genomics, Proteomics, Computational Biology, Active Learning Tom was formerly a Research Fellow for the £15 million SPHERE Interdisciplinary Research Collaboration (IRC) at the University of Bristol, which is designing a platform for eHealth in a smart-home context. This platform is currently being deployed into homes throughout Bristol. You may have seen the SPHERE House featured on the BBC’s ‘Joy of Data’ documentary or in the wonderful Aardman animated overview of the project. Our video accompanying the paper “Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project” is up on the KDD YouTube channel.

Tom specialises in probabilistic and deep learning methods for machine learning applications in the life sciences. He has a Ph.D. in Machine Learning applied to multivariate signal processing from UCL, and was employed by Microsoft Research Cambridge where he co-authored a book titled “Model-Based Machine Learning”, an early access online version of which is available at . He also has significant industrial experience, with positions at QinetiQ and the British Medical Journal, during which time he has performed application-driven research, and has pioneered large-scale production-ready software engineering projects. He is a fellow of the Royal Statistical Society and a member of the IEEE Signal Processing Society.