Here you can find a list of all projects which the Research Engineering Group are involved in.
Search
REG Projects
Adaptive multilevel MCMC sampling
Developing an MCMC algorithm for efficient Bayesian inference in multilevel models
Analysing humanities books and newspapers data
Text analysis using Cray's supercomputing analytics platform Urika-GX
Analysing social and geographic datasets
Mapping the available tools and techniques for qualitatively understanding human behaviour, in the context of social dynamics
App-based information governance for trustworthy research environments
Delivering an open source 'information governance system in a box' to support data protection in trustworthy research environments
Artificial intelligence for data analytics (AIDA)
Drawing on new advances in AI and machine learning to address data wrangling issues, and help to automate the data analytics process
Automating data visualisation
Researching algorithms for visualising data in digital twins, including automating the layout of large scale data and visualising the uncertainty of data values
Automating translation by determining text difficulty
Building a model to determine the features of text which make it more difficult for machine translation
Chronotopic cartographies for literature
Mapping fictional worlds from literary texts to advance understanding and interpretation of literature in entirely new ways
Data safe havens in the cloud
Developing a policy and process framework for secure environments for productive data science research projects at scale
Data science toolkit for explorable data visualisations
Helping scientists understand data analyses by connecting parts of visualisations to the data they depend on
Data science tools for high-performance computing
Enabling the provision of popular data science packages within a managed, multi-user, academic high performance computing (HPC) environment
Decision-making under uncertainty in air traffic control
Investigating machine learning methods to support air traffic controllers
Detecting and understanding harmful content online
Developing benchmarks and datasets for online harms researchers, and guidance for practitioners using tools to detect online harms
Enabling meta-learning in Shogun.ML
Allowing data scientists to exchange and analyse their workflows more easily
Environmental monitoring: blending satellite and surface data
Intelligent fusion of data from satellite and in-situ surface sensors to help understand our changing planet
Evaluating homomorphic encryption
Exploring different ways of encrypting sensitive data that can allow for secure, outsourced computation in the cloud
Global urban analytics for resilient defence
Understanding the mechanics that cause conflict and identifying multi-scale population areas that are at risk of conflict
Improving Android game recommendations
Creating a deep learning based tool to improve game recommendations on Samsung Galaxy devices
Integrating information visualisation with machine learning
Integrating interactive data and information visualisation with machine learning technologies for learning for large datasets
Investigating industrial CT scanner damage
Gathering community-sourced data on damage to x‑ray detectors in industrial CT scanners, to inform cost-effective maintenance
Language models for quantitative science studies
Developing models to represent the use of language in research and improve performance of specific tasks, including the retrieval and summarisation of literature
Living with Machines
A five-year research project that will take a fresh look at the well-known history of the Industrial Revolution using data-driven approaches
London air quality
Developing machine learning algorithms and data science platforms to understand and improve air quality over London
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.
Managing uncertainty in government modelling
Developing practical methods and tools for managing uncertainty in government modelling
Network modelling of the UK's urban skill base
Modelling networks of locally embedded knowledge and skills to investigate the future diversification potential of individual UK cities
Nocell: Probabilistic programming for spreadsheet experts
Developing a programming language for probabilistic model-building, for organisations that produce spreadsheets as output
Optimising analysis of network graphs
Investigating how to optimise software that allows for the analysis of complex data networks on state-of-the-art processors
Optimising flow within mobility systems with AI
Using interactive data visualisation, mathematical and computer modelling, and machine learning to transform the way cities are planned and urban traffic is managed
Project Bluebird: An AI system for air traffic control
Advancing probabilistic machine learning to deliver safer, more efficient, and predictable air traffic control
QUIPP – Quantifying utility and preserving privacy in synthetic data sets
Understanding the balance between utility, privacy and the uncertainty associated with synthetic data sets
Raphtory: A practical system for the analysis of dynamic graphs
Developing an open source system for dynamic graph analysis of datasets
Safety of offshore floating facilities
Predicting the hazardous conditions faced by offshore oil and gas facilities, to inform and improve operational decision-making
Scalable regression: Tools and techniques
Developing techniques for regression models to scale at large data volumes, incorporating regression model power into big data analytics engines
Scalable topological data analysis
Developing software that enables meaningful conclusions to be drawn from the shape of massive, noisy, and potentially incomplete datasets
Security in the cloud
Investigating and prototyping the secure software components needed to enable data sharing without compromising data privacy.
Simplifying the setup of simulations
Developing an intuitive framework to allow researchers to setup, configure and run simulation jobs, using different simulation packages and computing resources
sktime: A toolbox for data science with time series
A unified toolbox for time series in the Python programming language
The (mis)informed citizen
Using computational approaches to evaluate the quality of online news and studying its impacts
The Gamma: democratising data science
Democratising data science by building tools that encourage everyone to understand data better
The Turing Way
An open source and community-led handbook for reproducible, ethical and collaborative data science
Turing benchmarking framework
Developing a cross-architecture benchmarking framework for data science algorithms
UK Biobank activity recognition
Using deep learning to find new connections between patterns of physical activity and health outcomes
Uncertainty quantification of multi-scale and multi-physics computer models
Developing new tools to investigate and quantify uncertainties in computer models, with applications to climate, earthquake and tsunami models
Visual diagnostics for Markov Chain Monte Carlo (MCMC)
Designing visual diagnostics that articulate the concepts underlying robust MCMC computational algorithms
