Prediction algorithms with a causal interpretation
Monday 10 Feb 2020 - Wednesday 19 Feb 2020
Time: 09:00 - 17:00
Projects
Impact of climate change on agriculture
Building next-generation models to support resilient agricultural policy
Environmental monitoring: blending satellite and surface data
Intelligent fusion of data from satellite and in-situ surface sensors to help understand our changing planet
Multimorbidity phenotyping in cardiovascular disease
Developing statistical machine learning tools to investigate longitudinal multi-disease trajectories
The Turing AI scientist grand challenge
Developing AI systems capable of making Nobel quality scientific discoveries highly autonomously at a level comparable, and possibly superior, to the best human scientists by 2050
Uncertainty quantification for black box models
Developing methods for the quantification of uncertainty in complex numerical models
Synthetic data generation for finance and economics
Generating artificial business, finance, and economic data to enable data sharing, scenario simulation, and model validation
People
Dr Emily Lines
Turing Fellow
Laila Sprejer
Research Associate
Dr Victoria Voldina
Research Associate
Dr Peter Foster
Research Data Scientist
Dr Ashwini Venkatasubramaniam
Research Associate
Lorenzo Rimella
Enrichment Student
Akira Endo
Enrichment Student
Lizhi Zhang
Enrichment Student
Vitaly Zankin
Doctoral StudentEvents
TMCF workshop series: Prediction algorithms with a causal interpretation (online event)
Wednesday 06 May 2020
Time: 11:00 - 16:30
Network Science in Financial Services
Friday 06 Dec 2019
Time: 09:30 - 14:30
Building a future for the urban analytics blueprint
Wednesday 16 Oct 2019 - Friday 18 Oct 2019
Time: 10:00 - 17:00
Effective and efficient gaussian processes
Wednesday 07 Aug 2019
Time: 10:00 - 18:00
Skills-based modelling of economic development paths
Thursday 06 Jun 2019
Time: 13:30 - 17:30
Statistical trends in personalised healthcare
Monday 29 Apr 2019
Time: 10:15 - 18:00
A blueprint for urban analytics research
Thursday 11 Apr 2019 - Friday 12 Apr 2019
Time: 09:30 - 14:00
Data driven modelling of complex systems
Tuesday 08 May 2018 - Thursday 10 May 2018
Time: 10:00 - 18:00
Data sciences for climate and environment
Monday 26 Mar 2018
Time: 11:00 - 19:00
Articles
Demonstrating the impact of the NHS COVID-19 app
Statistical analysis from researchers supporting the development of the NHS COVID-19 app
Tuesday 09 Feb 2021
Updates to the algorithm underlying the NHS COVID-19 app
Using Bayesian statistics to further improve the NHS contact tracing app’s performance
Thursday 29 Oct 2020
A technical roadmap for the UK’s contact tracing app functionality
A successful app is able to appropriately assess risk
Thursday 13 Aug 2020
Impact stories
Publications and software
Bayesian statistics and modelling
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available...
van de Schoot, R., Depaoli, S., King, R. et al. Bayesian statistics and modelling. Nat Rev Methods Primers 1, 1 (2021). https://doi.org/10.1038/s43586-020-00001-2
Predicting Infectiousness for Proactive Contact Tracing
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries...
Bengio, Yoshua, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller et al. "Predicting Infectiousness for Proactive Contact Tracing." ICLR 2020.
Project Odysseus
Won CIUK Student Poster Competition 2020
Patrick O'Hara, James Walsh, Chance Haycock, Edward Thorpe-Woords, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas, James Brandreth, Oscar Giles, Neil Dhir, Theodoros Damoulas, Computing Insight UK 2020.
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
A characterisation of the reconstructed birth–death process through time rescaling
The dynamics of a population exhibiting exponential growth can be modelled as a birth–death...
Theoretical Population Biology (2020) 134: 61-76
Data Study Group Final Report: Roche
Personalised lung cancer treatment modelling using electronic health records and genomics
Data Study Group team. (2020, June 4). Data Study Group Final Report: Roche. Zenodo. http://doi.org/10.5281/zenodo.3876989
Data Study Group Network Final Report: Rothamsted Research
Tackling hidden hunger through soils
Data Study Group team. (2020, April 29). Data Study Group Network Final Report: Rothamsted Research. Zenodo. http://doi.org/10.5281/zenodo.3775489
Data Study Group Final Report: eGym
Improved strength training using smart gym equipment data
Data Study Group team. (2020, April 17). Data Study Group Final Report: eGym. Zenodo. http://doi.org/10.5281/zenodo.3755606
Data Study Group Final Report: Great Ormond Street Hospital
Augmenting clinical decision-making in intensive care
Data Study Group team. (2020, February 17). Data Study Group Final Report: Great Ormond Street Hospital. Zenodo. http://doi.org/10.5281/zenodo.3670726
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?
Omics data generation and analysis group
Developing rigorous and robust data science methods for curation, collection and computation of omic data sets
Trustworthy digital identity
How can we understand, evaluate and advance the trustworthiness of digital identity systems?
Environment and sustainability
What role can data science & AI play in addressing the challenges associated with environmental change?
Programmes
