New machine learning approaches and applications to omics
Wednesday 13 Dec 2023
Time: 10:00 - 16:00
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Applications (Machine learning)
The Turing Lectures: Engineering the future of medicine
Tuesday 08 Jun 2021
Time: 15:00 - 16:30
Turing Lecture: Building dynamic robots
Tuesday 22 Sep 2020
Time: 15:00 - 16:30
TMCF workshop series: Prediction algorithms with a causal interpretation (online event)
Wednesday 06 May 2020
Time: 11:00 - 16:30
Prediction algorithms with a causal interpretation
Monday 10 Feb 2020 - Wednesday 19 Feb 2020
Time: 09:00 - 17:00
UK multi-agent systems symposium
Monday 24 Feb 2020
Time: 10:00 - 17:00
Targeted learning
Wednesday 04 Mar 2020
Time: 18:00 - 20:30
Machine learning interpretability, explainability and trustability
Wednesday 11 Mar 2020
Time: 18:00 - 20:30
Citizen participation and machine learning for a better democracy
Friday 07 Feb 2020
Time: 13:00 - 18:00
Projects
London Air Quality Impact
Maximising the impact of the London Air Quality project by demonstrating live pollution forecasting
Environmental Data Science Book (EDS Book)
A computational notebook community for open environmental science
DeepSensor
DeepSensor is a Python package and open-source project for modelling environmental data with neural processes.
DEFIANT - Drivers and Effects of Fluctuations in sea Ice in the Antarctic
One of the most ambitious observational campaigns aimed at understanding Antarctic sea ice variability.
Demoland
Spatial modelling for (urban) land management predicting the impact of large-scale planning and land use changes.
Programmes
Publications and software
The AI Revolution: Opportunities and Challenges for the Finance Sector
This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to...
arXiv:2308.16538
Explaining the decisions of anomalous sound detectors
Deciding whether a sound is anomalous is accomplished by comparing it to a learnt...
K. T. Mai, T. Davies, L. D. Griffin, E. Benetos, "Explaining the decisions of anomalous sound detectors", in 7th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), Nov. 2022.
Measuring national mood with music
Using machine learning to construct a measure of national valence from audio data
E. Benetos, A. Ragano, D. Sgroi, and A. Tuckwell, “Measuring National Mood with Music: Using Machine Learning to Construct a Measure of National Valence from Audio Data”, Behavior Research Methods, Feb. 2022. https://doi.org/10.3758/s13428-021-01747-7
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
Hybrid Models for Learning to Branch
A recent Graph Neural Network (GNN) approach for learning to branch has been shown...
Gupta, Prateek, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, and Yoshua Bengio. "Hybrid models for learning to branch" NeurIPS 2020.
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.
Near Real-Time Social Distancing in London
During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance...
Near Real-Time Social Distancing in London, James Walsh, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas, Patrick O'Hara, Neil Dhir, Theodoros Damoulas, arXiv:2012.07751.
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.
Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency
The aim of this study was to estimate a 48 hour prediction of moderate...
Ferrari D, Milic J, Tonelli R, Ghinelli F, Meschiari M, Volpi S, Faltoni M, Franceschi G, Iadisernia V, Yaacoub D, Ciusa G, Bacca E, Rogati C, Tutone M, Burastero G, Raimondi A, Menozzi M, Franceschini E, Cuomo G, Corradi L, Orlando G, Santoro A, Digaetano M, Puzzolante C, Carli F, Borghi V, Bedini A, Fantini R, Tabbì L, Castaniere I, Busani S, Clini E, Girardis M, Sarti M, Cossarizza A, Mussini C, Mandreoli F, Missier P, Guaraldi G. Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency. PLoS One. 2020 Nov 12;15(11):e0239172. doi: 10.1371/journal.pone.0239172. PMID: 33180787; PMCID: PMC7660476.
Articles
Events
New machine learning approaches and applications to omics
Wednesday 13 Dec 2023
Time: 10:00 - 16:00
The Turing Lectures: Engineering the future of medicine
Tuesday 08 Jun 2021
Time: 15:00 - 16:30
Turing Lecture: Building dynamic robots
Tuesday 22 Sep 2020
Time: 15:00 - 16:30
TMCF workshop series: Prediction algorithms with a causal interpretation (online event)
Wednesday 06 May 2020
Time: 11:00 - 16:30
Machine learning interpretability, explainability and trustability
Wednesday 11 Mar 2020
Time: 18:00 - 20:30
Targeted learning
Wednesday 04 Mar 2020
Time: 18:00 - 20:30
UK multi-agent systems symposium
Monday 24 Feb 2020
Time: 10:00 - 17:00
Citizen participation and machine learning for a better democracy
Friday 07 Feb 2020
Time: 13:00 - 18:00
Computational archival science (CAS) symposium
Monday 20 Jan 2020
Time: 09:30 - 17:00
Impact stories
Helping London to navigate lockdown safely
Project Odysseus monitors activity on the streets of London, allowing authorities to make interventions to keep people socially distanced
Supporting innovation in the fintech sector
The Turing evaluated the synthetic data used in the Financial Conduct Authority’s Digital Sandbox Pilot
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
Dynamic forecasting with British Airways
Predicting demand for flights is central to airline business strategy, and enormously complex. In collaboration with British Airways, the Turing has developed novel machine learning techniques to tackle the challenge
Transforming medicine through AI-enabled healthcare
World-leading expertise in healthcare-focused machine learning combined with the world’s largest, high-quality cancer data collection service could lead to a quantum leap in personalised medicine
A machine learning revolution in disaster response
Turing researchers have combined crowd-sourcing, machine learning and neural networks to rapidly reveal the many dimensions of natural disasters, deploying the technology in the aftermath of Hurricane Dorian
Towards a greener grid
Turing researchers and doctoral students contributed to a new solar forecasting system for National Grid, which is 33% more accurate at day-ahead forecasts, aiding in more efficient balancing of supply and demand and lowering consumer costs
Interest groups
Novel data linkages for health and wellbeing
How can we use novel digital footprint data for research to benefit health and wellbeing?
Biodiversity monitoring and forecasting
How can we best catalyse and champion the development of new AI and data-driven methods for monitoring and forecasting biodiversity change?
Data-centric biological design and engineering
How can AI support the practical engineering of biology to tackle global challenges?
Health equity
How can we ensure that developments and applications of data science and AI improve everyone’s health?
Meta-learning for multimodal data
How can we transfer knowledge across tasks and domains to improve machine learning on multimodal data?
People
Dr James Wilsenach
Turing Postdoctoral Research Associate
Dr Alex Hickey
Research Engineering
Dr Noushin Eftekhari
Research Associate
Dr Thijs van der Plas
Postdoctoral Research Associate
Dr Edmund Dable-Heath
Data Scientist
Yuhan Wang
Enrichment Student
Ye Wei
Enrichment Student