Projects
Disease trajectories following a heart attack
Investigating the use of process mining techniques to understand disease pathways amongst 145 million hospitalisation records
Personalising treatment in type 2 diabetes
Developing and testing a decision support tool for primary care to optimise the selection of glucose-lowering therapy for people with type 2 diabetes
Learning machines – infrastructure for augmented intelligence
Developing infrastructure and methods for supporting continuous use of machine learning algorithms
Machines reading maps
Creating a generalisable machine learning pipeline to process text on maps and catalysing humanities, scientific, and cultural heritage communities to use map text as data
Multimorbidity phenotyping in cardiovascular disease
Developing statistical machine learning tools to investigate longitudinal multi-disease trajectories
The Turing-RSS Health Data Lab
A working partnership between The Alan Turing Institute and Royal Statistical Society, providing independent research and support to the UK Health Security Agency
Programmes
Publications and software
Data Study Group Final Report: University of Strathclyde and Supergen Energy Networks Hub
Using machine learning to predict the onset of blackouts
Data Study Group team. (2022, January 5). Data Study Group Final Report: Entale. Zenodo. https://doi.org/10.5281/zenodo.5820436
Polygonal Unadjusted Langevin Algorithms
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks. Lim, D-Y. & Sabanis, S. 2021. arXiv:2105.13937 [cs.LG]
Aiming beyond the Obvious - Identifying Non-Obvious Cases in Semantic Similarity Datasets
Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose...
Nicole Peinelt, Maria Liakata, Dong Nguyen (2019): Aiming beyond the Obvious - Identifying Non-Obvious Cases in Semantic Similarity Datasets. Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL 2019), pages 2792 - 2798.
tBERT – Topic Models and BERT Joining Forces for Semantic Similarity Detection
Semantic similarity detection is a fundamental task in natural language understanding. Adding topic information...
Nicole Peinelt, Dong Nguyen, Maria Liakata (2020): tBERT - Topic Models and BERT Joining Forces for Semantic Similarity Detection. Proceedings of the 58th Conference of the Association for Computational Linguistics (ACL 2020), pages 7047-7055.
Data Study Group Final Report: Greenvest Solutions
Forecasting wind energy production using satellite data
Data Study Group team. (2021, February 11). Data Study Group Final Report: Greenvest Solutions. Zenodo. https://doi.org/10.5281/zenodo.4534349
Data Study Group Final Report: Dstl – Anthrax and nerve agent detector
Identification of hazardous chemical and biological contamination on surfaces using spectral signatures
Data Study Group team. (2021, February 11). Data Study Group Final Report: Dstl – Anthrax and nerve agent detector. Zenodo. https://doi.org/10.5281/zenodo.4534219
Learning Bijective Feature Maps for Linear ICA
Separating high-dimensional data like images into independent latent factors, i.e independent component analysis (ICA)...
Camuto, A., Willetts, M., Paige, B., Holmes, C., Roberts, S., (2020) Learning Bijective Feature Maps for Linear ICA
Improving VAEs’ Robustness to Adversarial Attack
Variational autoencoders (VAEs) have recently been shown to be vulnerable to adversarial attacks, wherein...
Willetts, M., Camuto, A., Rainforth, T., Roberts, S., Holmes, C., (2021) Improving VAEs’ Robustness to Adversarial Attack. International Conference of Learning Representations (ICLR)
Explicit Regularisation in Gaussian Noise Injections
We study the regularisation induced in neural networks by Gaussian noise injections (GNIs). Though...
Camuto, A., Willetts, M., Şimşekli, U., Roberts, S., & Holmes, C. (2020). Explicit Regularisation in Gaussian Noise Injections. NeurIPS
Articles
AI accurately predicts who will develop dementia in two years
Study shows machine learning can make predictions with 92 per cent accuracy
Thursday 16 Dec 2021
Researchers from The Alan Turing Institute awarded Turing AI Acceleration Fellowships
A number of outstanding researchers from The Alan Turing Institute, the national institute for...
Friday 27 Nov 2020
Why practical data analytics is still so painful... and how we can help
Datadiff Often an analyst is given a number of datasets from successive time periods...
Wednesday 06 May 2020
The Turing teams up with Cervest to help predict climatic events in data scarce regions
Cutting edge methodology designed to allow communities and organisations around the world to understand...
Monday 20 Apr 2020
AI transparency in financial services
Why, what, who and when?
Tuesday 18 Feb 2020
Ethics review recommends cautious, thoughtful and inclusive approach to using machine learning in children’s social care
What Works for Children’s Social Care is pleased to be publishing a review of...
Thursday 30 Jan 2020
Urban mobility innovations to get everyone moving
Metropolitan areas are complex entities with a diverse set of unique mobility challenges that...
Monday 11 Nov 2019
Events
Interpretability, safety, and security in AI
Monday 13 Dec 2021 - Wednesday 15 Dec 2021
Time: 11:00 - 18:00
Atlas of AI
Wednesday 09 Jun 2021
Time: 12:00 - 13:00
The Turing Lectures: Engineering the future of medicine
Tuesday 08 Jun 2021
Time: 15:00 - 16:30
Turing Lecture: Is education AI-ready?
Wednesday 29 Jul 2020
Time: 15:00 - 16:30
Robotics and AI for health and social care
Monday 28 Oct 2019 - Tuesday 29 Oct 2019
Time: 10:00 - 17:30
Towards Autonomous Robotic Systems Conference (TAROS)
Wednesday 03 Jul 2019 - Friday 05 Jul 2019
Time: 09:00 - 18:00
Machine learning and data challenges in ransomware and the cloud
Friday 07 Jun 2019
Time: 09:30 - 17:00
Transforming medicine through AI-enabled healthcare pathways
Monday 03 Jun 2019
Time: 18:00 - 20:00
Mathematics of data
Wednesday 29 May 2019 - Friday 31 May 2019
Time: 10:00 - 17:00
Impact stories
Putting the AI in air traffic control
The UK’s leading air traffic control provider, NATS, has teamed up with the Turing to explore how the industry can evolve with machine learning
Co-designing computing
High-performance computing (HPC) environments such as data centres can be ill-equipped to deal with data science tasks, so Turing researchers collaborated with Intel to co-design better architecture for their HPC systems
Augmenting clinical decision-making
Working with the Cystic Fibrosis Trust, Turing researchers have been developing machine learning methods that could dramatically improve the accuracy of clinical assessments of people with cystic fibrosis
Interest groups
Machine learning for radio frequency applications
How do we generate robust, real-time machine learning algorithms with integrated hardware and software for radio frequency applications?
Computer vision for digital heritage
What are the challenges and opportunities in using computer vision to investigate historical collections digitised as images?
Economic data science
How can new kinds of data allow us to tackle the full complexity of the socio-technical systems we create and inhabit?
Visualization
How can we gain insight into ever-growing datasets and see the reasoning behind automated AI decision-making?
Machine learning in finance
What are the key challenges of adopting artificial intelligence methods in the financial services industry?
Sustainable finance
How can data science techniques be deployed to align financial institutions with the goal of global environmental sustainability?
Privacy-preserving data analysis
How can we enable personal data to be shared without compromising our privacy?
People
Dr Katayoun Farrahi
Turing Fellow
Dr Luca Magri
Group Leader, Physics-informed machine learning and data assimilation
Morgan Briggs
Research Associate for Data Science and Ethics
Professor Kerry Kirwan
Board Member
Dr Derek Snow
Research Associate
Dr Scott Hosking
Senior Research Fellow
Maxine Mackintosh
Programme Lead – Diverse Data, Genomics England