Projects
DECOVID
Using detailed, frequently updated health data in a secure database, providing up to date information about patient care during the COVID-19 pandemic
Identifying potential disease causing variants in genetic data
Developing an algorithm to identify disease causing variants in protein-coding regions of genes
Machine learning and large cryogenic electron microscopy data sets
Applying machine learning techniques to identify interacting protein molecules in large cryogenic electron microscopy (cryo-EM) data
AutoAI: Machine learning systems design
Improving the way AI is deployed, monitored and maintained through a systems design approach
Machine learning in Julia
Developing a machine learning toolbox for the Julia programming language, emphasising ease of use, reproducibility, high performance, rapid development and advanced interoperability.
People
Professor Kerry Kirwan
Board Member
Derek Snow
Research Associate
Dr Scott Hosking
Senior Research Fellow
Maxine Mackintosh
Research Associate
Konstantinos Zinelis
Enrichment Student
Feargus Pendlebury
Enrichment Student
Jin Wang
Enrichment Student
Dr Dani Arribas-Bel
ESRC/Turing Fellow
Dr Luisa Orsini
Turing FellowEvents
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
Data-centric materials science and engineering
Tuesday 14 May 2019 - Wednesday 15 May 2019
Time: 09:00 - 17:00
Tony Hey
Philip Withers
Louise Wright
Catherine Powell
Chris Taylor
Keith Butler
Prof. Timothy Cootes
Dr. Luke Mason
Dr. Robin Pinning
Prof. Vassil Alexandrov
Dr. Hector Basoalto
Joshua Franz Einsle
Mark Girolami
Din-Houn Lau
Andrew Duncan
Pranay Seshadri
Nick Higham
Jianxin Pan
Tim Dodwell
Patrick Rubin-Delanchy
David Hogg
Turing Lecture: Machine learning and big scientific data benchmarks
Tuesday 02 Oct 2018
Time: 18:00 - 19:30
AI for traffic control
Friday 24 Aug 2018
Time: 10:00 - 14:30
Articles
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
AIrsenal - the difficult second season
Converting a struggling Fantasy Football team into an open source success.
Friday 25 Oct 2019
Turing JADE RAP Call June 2019
Details The Alan Turing Institute is offering access to The Joint Academic Data Science...
Friday 07 Jun 2019
AIrsenal
A machine learning manager for Fantasy Premier League.
Thursday 09 May 2019
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
Publications and software
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
While much attention has been given to the problem of estimating the effect of...
Bica, I., Jordon, J., & van der Schaar, M. (2020). Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks. arXiv preprint arXiv:2002.12326.
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
The estimation of treatment effects is a pervasive problem in medicine. Existing methods for...
Bica, I., Alaa, A. M., & van der Schaar, M. (2020). Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders. International Conference on Machine Learning (ICML) 2020.
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating
A new approach in stochastic optimization via the use of stochastic gradient Langevin dynamics...
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating. Sabanis, Sotirios; Zhang, Ying. arXiv:2007.01672 [q-fin.PM]
Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms
Artificial neural networks (ANNs) are typically highly nonlinear systems which are finely tuned via...
Taming neural networks with TUSLA : Non-convex learning via adaptive stochastic gradient Langevin algorithms. Lovas, Attila; Lytras, Iosif; Rásonyi, Miklós; Sabanis, Sotirios. arXiv:2006.14514 [cs.LG]
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization. Akyildiz, Ömer Deniz; Sabanis, Sotirios. arXiv:2002.05465 [math.OC]
Data Study Group Final Report: WWF
Smart monitoring for conservation areas
Data Study Group team. (2020, June 5). Data Study Group Final Report: WWF. Zenodo. http://doi.org/10.5281/zenodo.3878457
Data Study Group Network Final Report: Woolfson Laboratory
Machine learning for protein folding
Data Study Group team. (2020, June 5). Data Study Group Network Final Report - Woolfson Laboratory. Zenodo. http://doi.org/10.5281/zenodo.3877119
Data Study Group Final Report: Telenor
Green Radio: Dynamic power saving configuration for mobile networks
Data Study Group team. (2020). Data Study Group Final Report: Telenor. http://doi.org/10.5281/zenodo.3786852
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
Identifying when to give treatments to patients and how to select among multiple treatments...
Bica, I., Alaa, A. M., Jordon, J., & van der Schaar, M. (2020). Estimating counterfactual treatment outcomes over time through adversarially balanced representations. International Conference on Learning Representations (ICLR) 2020.
Interest groups
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?
Machine learning by systems, for systems
How can mutually-influential, and mutually-beneficial relationships between machine learning and systems be enabled?
Sampling algorithms for data analytics
How do we design algorithms that can be used across different, related disciplines?
