Dr Tom Diethe is currently the Head of Machine Learning (Senior Director) at AstraZeneca, Cambridge UK. Tom is a member of the Center for AI within Data Sciences & AI, 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.
- Lead for machine learning on the intersection of the life sciences - Deep Learning, Genomics, Proteomics, Computational Biology, Active Learning
- Previous teams (Cambridge, UK):
- Alexa Shopping: Natural Language Processing, Privacy Preserving Machine Learning, Differential Privacy
- Supply Chain Optimization Technologies: Uncertainty Quantification, Bayesian Optimization, Gaussian processes, Anomaly/Outlier Detection, Time Series Analysis, Clustering
- Core machine learning: Deep Probabilistic Models, Bayesian Neural Networks, Continual Learning, Streaming/Online Machine 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, including data fusion, online/sequence/time-series modelling, unsupervised/active/transfer/self-supervised learning approaches. 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 http://www.mbmlbook.com. 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.