The Turing Lectures: Do you see what I see?
Tuesday 04 May 2021
Time: 16:00 - 17:30
Search
Semi-supervised learning
Machine learning interpretability, explainability and trustability
Wednesday 11 Mar 2020
Time: 18:00 - 20:30
Data science for physics and astronomy scoping workshop
Monday 02 Dec 2019 - Tuesday 03 Dec 2019
Time: 10:30 - 16:30
Towards Autonomous Robotic Systems Conference (TAROS)
Wednesday 03 Jul 2019 - Friday 05 Jul 2019
Time: 09:00 - 18:00
Machine learning meets statistics
Monday 25 Mar 2019 - Tuesday 26 Mar 2019
Time: 09:30 - 16:15
Projects
Practicable data sharing across borders
Allowing data transfer and joint knowledge creation across different geographical and jurisdictional regions
Decision support algorithms for emergency departments
Developing machine learning classifiers to predict departure points in emergency departments in hospitals
Machine learning prediction of plant chemical production
Investigating the modeling and prediction of the biosynthesis of triterpene natural products in plants
Predictive graph analytics and propagation of information in networks
Leveraging graph optimisation tools to understand how news sentiment spreads in a financial network
Digital twins in aeronautics
Developing next generation digital twins using advances in aerodynamics, applied mathematics and computational statistics
Actionable insights from free text police data
Using topic modelling to identify specific crime modus operandi from police free text data
Publications and software
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
An MBO scheme for clustering and semi-supervised clustering of signed networks
We introduce a principled method for the signed clustering problem, where the goal is...
M. Cucuringu, A. Pizzoferrato, Y. van Gennip, An MBO scheme for clustering and semi-supervised clustering of signed networks, (arXiv) (2019)
Anomaly detection in networks with application to financial transaction networks
This paper is motivated by the task of detecting anomalies in networks of financial...
A. Elliott, M. Cucuringu, M. M. Luaces, P. Reidy, and G. Reinert, Anomaly detection in networks with application to financial transaction networks, (arXiv 1901.00402) (2019)
Semi-Unsupervised Learning of Human Activity using Deep Generative Models
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning...
Willetts, M., Doherty, A., Roberts, S., Holmes, C. (2018) Semi-Unsupervised Learning of Human Activity using Deep Generative Models. NeurIPS ML4Health Workshop 2018
Semi-Unsupervised Learning using Deep Generative Models
Willetts, M., Doherty, A., Roberts, S., Holmes, C., (2018) Semi-unsupervised Learning using Deep Generative Models. NeurIPS 2018 Workshop on Bayesian Deep Learning
Events
The Turing Lectures: Do you see what I see?
Tuesday 04 May 2021
Time: 16:00 - 17:30
Machine learning interpretability, explainability and trustability
Wednesday 11 Mar 2020
Time: 18:00 - 20:30
Data science for physics and astronomy scoping workshop
Monday 02 Dec 2019 - Tuesday 03 Dec 2019
Time: 10:30 - 16:30
Towards Autonomous Robotic Systems Conference (TAROS)
Wednesday 03 Jul 2019 - Friday 05 Jul 2019
Time: 09:00 - 18:00
Machine learning meets statistics
Monday 25 Mar 2019 - Tuesday 26 Mar 2019
Time: 09:30 - 16:15
Interest groups
Meta-learning for multimodal data
How can we transfer knowledge across tasks and domains to improve machine learning on multimodal data?
Precision dementia medicine
How can we harness the power of data science and artificial intelligence to combat dementia and promote brain health?
Bridging machine learning and behaviour models
How can we bridge data-driven and theory-driven behavioural models?
Omics data generation and analysis group
Developing rigorous and robust data science methods for curation, collection and computation of omic data sets
Machine learning and dynamical systems
How do we analyse dynamical systems on the basis of observed data, rather than attempt to study them analytically?
Developing best practice for collecting cybersecurity data
How do we get the best quality relevant and appropriate data for researching cybersecurity?
People
Dr Katie Awty-Carroll
Senior Data Scientist
Yuhan Wang
Enrichment Student
Shubhr Singh
Enrichment Student
Ruby Wood
Enrichment Student
Penny Clarke
Enrichment Student
Nikolai Juraschko
Enrichment Student
Matt De Vries
Enrichment Student