Wil Ward is a Research Associate in Deep Probabilistic Machine Learning at the University of Sheffield. Previously, he studied his undergraduate degree to Masters level in Mathematics and Computer Science at the University of Nottingham. He went on to study a PhD in Computer Science in a collaborative project with the British Geological Survey, funded by the BGS-University Funding Initiative.
The research project dealt with developing and adapting Computer Vision and Machine Learning techniques for Electrical Resistivity Tomography images. He has developed solutions dealing with feature detection and time-lapse tracking using novel combinations of fuzzy and Bayesian inferences. Prior to this, he briefly worked as a research assistant looking at image analysis for medical data. His work has appeared across a range of application publications, including the proceedings of EMBC, and journals including Geophysical Journal International and Water Resources Research.
PhD Computer Science, 2014-ongoing
University of Nottingham
MSci Mathematics and Computer Science, 2013
University of Nottingham
This project will develop scalable approaches to deep non-parametric probabilistic models that use approximate inference techniques to learn the structure of the model. The project requires that the development of practical, interpretable models, with latent variables that can be used by clinicians and non-academics in a meaningful way. We also aim to build distributed user-centric data models, in which the learning occurs across distributed devices, through the paradigm of differential privacy. The successful candidate will be able to demonstrate knowledge of a wide range of machine learning techniques (in particular probabilistic modelling) and practical experience handling data which is noisy, sparse and/or of high dimensionality.
This project was part of a collaborative effort and joint supervision with British Geological Survey addressing the need for automatic feature detection and interpretation 3D time-lapse images of subsurface geology obtained through electrical resistivity tomography. The interdisciplinary work will combine new techniques in computer vision and change recognition in increased dimension to identify, predict and understand changes to and caused by complex hydro-geophysical processes. Results were be verified through a combination of synthetic, experimental and field models.