Projects
Environment and sustainability
Using data science and AI to tackle environmental and climate challenges
Causal inference for improved cybersecurity threat detection
Understanding the causal links in the tactics and techniques of cyberattacks
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
Radiation detectors and machine learning
Developing a machine learning approach to discriminating between different types of radiation in cutting edge scintillator detectors
Technical advice for the NHS COVID-19 app
Informing the NHS Test and Trace programme’s understanding of the spread of the virus and helping to optimise the performance of the app
People
Dr Emily Lines
Turing Fellow
Dr Neil Dhir
Postdoctoral Research Associate
Dr Derek Snow
Research Associate
Dr Scott Hosking
Senior Research Fellow
Dr Peter Foster
Research Data Scientist
Dr Grigorios Mingas
Senior Research Data Scientist
Dr Luisa Orsini
Turing Fellow
Dr Ashwini Venkatasubramaniam
Research Associate
Lorenzo Rimella
Enrichment StudentEvents
Uncertainty and risk
Wednesday 17 Mar 2021 - Friday 19 Mar 2021
Time: 14:00 - 17:00
Network Science in Financial Services
Friday 06 Dec 2019
Time: 09:30 - 14:30
Data science for physics and astronomy scoping workshop
Monday 02 Dec 2019 - Tuesday 03 Dec 2019
Time: 10:30 - 16:30
Building a future for the urban analytics blueprint
Wednesday 16 Oct 2019 - Friday 18 Oct 2019
Time: 10:00 - 17:00
Impact stories
Publications and software
Using wearable activity trackers to predict Type-2 Diabetes: A machine learning-based cross-sectional study of the UK Biobank accelerometer cohort
Background: Between 2013 and 2015, the UK Biobank (UKBB) collected accelerometer traces (AXT) using...
Lam B, Catt M, Cassidy S, Bacardit J, Darke P, Butterfield S, Alshabrawy O, Trenell M, Missier P
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
Generalized Bayesian filtering via sequential Monte Carlo
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under...
A. Boustati, Ö. D. Akylidìz, T. Damoulas, and A. M. Johansen (2020). Generalized Bayesian filtering via sequential Monte Carlo. In Advances in Neural Information Processing Systems 33 (NeurIPS). To appear.
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
The future of sleep health: A data-driven revolution in sleep science and medicine
In recent years, there has been a significant expansion in the development and use...
Perez-Pozuelo, I., Zhai, B., Palotti, J. et al. The future of sleep health: a data-driven revolution in sleep science and medicine. npj Digit. Med. 3, 42 (2020). https://doi.org/10.1038/s41746-020-0244-4
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
Multi-Task Time Series Analysis applied to Drug Response Modelling
Time series models such as dynamical systems are frequently fitted to a cohort of...
Bird, A., Williams, C. K. I. and Hawthorne, C. (2019). Multi-Task Time Series Analysis applied to Drug Response Modelling. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2174-2183).
A Deep Learning Framework for Pricing Financial Instruments
We propose an integrated deep learning architecture for the stock movement prediction. Our architecture...
Wu, Qiong; Zhang, Zheng; Pizzoferrato, Andrea; Cucuringu, Mihai; Liu, Zhenming. "A Deep Learning Framework for Pricing Financial Instruments", arXiv:1909.04497 (2019)
Mining the UK Web Archive for Semantic Change Detection
Semantic change detection (i.e., identifying words whose meaning has changed over time) started emerging...
Tsakalidis, A., Bazzi, M., Cucuringu, M., Basile, P., & McGillivray, B. Mining the UK Web Archive for Semantic Change Detection. Proceedings of INTERNATIONAL CONFERENCE RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING, https://doi.org/10.17863/CAM.43534
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?
AI in resource- and data-constrained environments
How can we build effective predictive models on hardware platforms that have limited computational resources and/or when the model training data are by definition always limited in amount?
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?
Machine learning and dynamical systems
How do we analyse dynamical systems on the basis of observed data, rather than attempt to study them analytically?
