Developing pioneering research on fundamental data science and machine learning for real world applications
We use data science and machine learning to model real-world observations and increase the reliability of predictions made by forecasting models. Areas of research include:
- urban air pollution;
- medical image segmentation;
- fluid dynamics; and
- wildfire prediction.
If you are interested in one of the projects listed below, we encourage you to contact the primary project supervisor or the alternative contact person for further information.
Current projects
Dynamic Isotopic and Probabilistic Modelling of Global Metal Flows: Integrating Isotope Geochemistry, Bayesian Inference, and System Dynamics to Advance Industrial Ecology [Info Sheet - Plancherel DIPMod]
Supervisors: Dr Yves Plancherel, Rupert Myers, Pablo Brito-Parada, Kolyan Ray
Integrating Multimodal Very-High-Resolution Data for Ecosystem Intelligence and Natural Capital Monitoring [Info Sheet - Plancherel AI4UAV]
Supervisors: Dr Yves Plancherel, Matt Piggott
Data-Driven Detection and Attribution of Illegal Resource Extraction [Info Sheet - Plancherel IllegalResources]
Supervisors: Dr Yves Plancherel
Automated Crater Detection and Classification with Machine Learning [Info Sheet - Collins ACDC]
Supervisors: Prof Gareth Collins, Dr Ben Moseley, Dr Joel Davis
New generation data assimilation and rapid response models for urban flooding [Info Sheet - Fang Flooding]
Supervisors: Dr Fangxin Fang, Professor Christopher Pain
Journey to the Core-Mantle Boundary Region: Uncovering Earth’s Hidden Structures through Seismology [Info Sheet - Kim Deep Earth]
Supervisors: Dr Doyeon Kim
Shaking Worlds: Exploring Planetary Quakes and Deep Interiors [Info Sheet - Kim Planetary]
Supervisors: Dr Doyeon Kim
Brain ultrasound imaging with diffusion-guided full-waveform inversion. Info Sheet - Moseley Brain ultrasound imaging ]
Supervisors: Dr Ben Moseley
Using machine learning to magnetically fingerprint particulate matter [Info Sheet - Muxworthy fingerprint]
Supervisors: Prof. Adrian Muxworthy, David Green (Public Health) &Wyn Williams (University of Edinburgh)
Developing machine learning models for using crystal textures to investigate magmatic evolution leading to explosive volcanic eruptions [Info Sheet - Nathwani volcanology]
Supervisors: Dr Chetan Nathwani, Dr. Chiara Petrone (NHM), Dr. Martin Mangler (Southampton), Dr. Rossella Arcucci
Reading the textures of zircon crystals from porphyry copper deposits using machine learning [Info Sheet - Nathwani zircon textures]
Supervisors: Dr Chetan Nathwani, Dr. Rossella Arcucci and Dr. Ethan Tonks (Natural History Museum)
Mapping thermal and compositional structure of cratons [Info Sheet - Goes Cratons]
Supervisors: Professor Saskia Goes, Dr Ian Bastow
Structure and Evolution of the African Plate from Geophysical Observations [Info Sheet - Goes Africa Joint Tomography]
Supervisors: Professor Saskia Goes, Dr Gareth Roberts
Earthquake Forecasting Using Machine Learning [Info sheet - Earthquake Forecasting]
Supervisors: Professor Saskia Goes, Dr. Alexandra Renouard, Prof. Peter Stafford (Civil), Dr. Alex Whittaker
Global CO2 storage capacity: Modeling limitations of geography and injectivity [Info Sheet - Krevor CO2 Storage Capacity]
Supervisor: Dr Sam Krevor and others TBA
Reservoir characterisation and modelling of CO2 storage underground [Info Sheet - Krevor CO2 Storage Modelling]
Supervisor: Dr Sam Krevor and others TBA
Reduced-Order and Sparse Reconstruction Methods for Cardiac Ultrasound FWI [Info Sheet - Nelson MI]
Supervisors: Dr Rhodri Nelson, Prof Gerard Gorman
Mantle Dynamic Impacts on Cenozoic Sea-Level Evolution [Info Sheet - Richards mantle]
Supervisors: Dr Fred Richards
Source-to-Sink Analysis of Critical Metal Mineralisation [Info Sheet - Richards source to sink]
Supervisors: Dr Fred Richards, Tom Lamont (UNLV) and Alex Lipp (UCL)
Artificial Intelligence for Life Detection at Europa [Info sheet - AI Techniques for Europa]
Supervisors: Professor Mark A. Sephton, Dr Jonathan Watson, Prof Jonathan Carter, Prof Hunter Waite (Alabama)
Machine learning for subsurface multiphase flow in the energy transition [Info sheet - Machine Learning for Multiphase Flow]
Supervisors: Dr Gege Wen