Scientific Machine Learning at University of Leeds
Hi!
Welcome to the website for people interested in scientific machine learning (SciML) at Leeds (SciML is the discipline of combining machine learning with scientific computing see e.g. sciml.ai for an overview). This website serves to organise our work and allow others to join us.
If you’re based at Leeds and would like to join:
- you can simply make a pull-request to add yourself to this website: people.md
- or instead of a pull request, use the automated form here
We meet the first Friday of every month at 11am UK/local time in our home in LIDA (Leeds Institute for Data Analytics), Worsley Building, 11th Floor, room 11.87. (.ics-file download to add the event to your calendar). We also organise seminars and workshops, as well as send out a monthly newsletter.
View the SciML newsletter archives here.
People
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Leif Denby
Self-supervised/unsupervised learning, convolutional networks
meso-scale cloud organisation, denoising of LIDAR water vapour observations
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Chetan Deva
Random Forests, XGBoost, Long Short-Term Memory
Modelling Plant Behaviour across G*E, Time Series Forecasting of Vegetation Dynamics
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Samuel Bancroft
Semi-supervised/self-supervised learning, convolutional networks
crop type classification, satellite time series classification, crop yield estimation
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Jonathan Coney
Supervised learning, convolutional networks
segmentation of atmospheric gravity waves, learning physical characteristics from model output
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Trystan Surawy-Stepney
Weakly-supervised learning, Convolutional networks, Numerical modelling
Glacier damage identification, SAR data, Glacial process modelling
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Ben Wallis
Supervised learning, Convolutional networks, Bayesian methods
Tidewater glacier dynamics, Ice ocean interaction
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Joey Smith
Random Forests, GANs
Storm tracking and initiation prediction, Precipitation nowcasting
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Maria Luisa Taccari
Operator learning, PINNs, Supervised learning, Convolutional networks
Groundwater prediction, Surrogate modelling
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Calum S. Skene
Neural networks, Neural operators, Reservoir computing
Geophysical and astrophysical turbulence
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Dody Dharma
Supervised learning, Continuous Convolution, Graph Neural Networks, Multilayer Perceptron, LSTM
Deformbale Solid and Fluid Simulation, Vorticity Conservation
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Naomi Shakespeare-Rees
PINN's, Convolutional Networks
Geomagnetic fields, Core Flows
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Shenghao Qiu
Machine learning Compiler, Graph Neural Network, Self-supervised learning, Convolutional Networks
Parallel and Distributed Deep Learning, Models acceleration, Tensors and models backend graph optimization
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Shu Zhang
Random Forests, XGBoost, LSTM
Tropospheric ozone forcast, Weather classification, Time series decomposition of air pollutants
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Phil Livermore
Physics informed neural networks
Reconstruction of earth/planetary magnetic fields from sparse data, deep earth geophysics, inverse theory
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Jakub Lewandowski
Supervised learning, physics-informed neural networks
Weather nowcasting, parametrization of atmospheric processes
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Joe Gallear
Supervised learning, neural networks, random forests
Crop yield estimation, impacts of climate variability, effects of extreme events
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Sam Llanwarne
Neural Networks, Segmentation, auto-encoding, classification, Want to use transformers and graphs more
very large histology tissue image, preprocessing and classification
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Andy Nowacki
Neural networks, classification
Detection and location of earthquakes and icequakes, subsurface structure and characterisation
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John Chong Zhang
Neural networks, inversions, filters
mineral system exploration, subsurface structure, geophysical potential fields
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Miranda Horne
Operator Learning, Physics Constrained Neural Networks
Computational Fluid Dynamics, Inverse Modelling, Interior Air Control
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Longwei Chen
Physics-informed neural networks, deeponet, Bayesian PINN
Planetary magnetic field modelling, asteroid gravity modelling, geophysical inverse problem
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Leyuan Wu
Physics informed neural networks
Earth and planets' gravity field modelling
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Jack Smith
Cnns, synthetic data, gans
Semantic segmentation of masonry lined tunnel joints, structural condition assessment
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Fergus Shone
Informed machine learning, Physics-informed neural networks
Haemodynamic flow super-resolution, 4D-flow MRI, Left ventricular remodelling
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Alhanof Alolyan
Deep reinforcement learning, deep learning
Computer-aided design, crowd simulation
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Claire Bartholomew
Supervised learning, CNNs
Precipitation nowcasting, Convection / thunderstorm prediction
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Jacob Connolly
Supervised learning, Random Forests
InSAR Time series, Noise reduction in SAR data
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Andy Hooper
Ica, deep learning,
Radar interferometry (insar), volcano deformation, tectonic deformation
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Mark Richardson
Fortran, mpi, openmp
Met office um, dynamo, cesm
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Eric Atwell
Text analytics, sketchengine, chatbots
University teaching and assessment, understanding quran and hadith, detecting bias and fake news
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Simon Peatman
Neural networks
Classification of convective weather systems
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Andy Turner
Multi-layered Perceptron, Fuzzy Inference
Predicting spatial and temporal change, Forecasting climate change impacts, River level forecating