More Publications

. Introducing diffusion tensor to high order variational model for image reconstruction. Digital Signal Processing, 2017.


. Novel Methods for microglia segmentation, feature extraction and classification. IEEE/ACM Trans. Comput. Biol. Bioinform, 2016.


. Tracking tracer motion in a 4-D electrical resistivity tomography experiment. WRR, 2016.


. Spherical harmonics for surface parametrisation and remeshing. Mathematical Problems in Engineering, 2015.


. Surface reconstruction from point clouds using a novel variational model. SGAI 2015: Research and Development in Intelligent Systems XXXII, 2015.


. Retinal vasculature classification using novel multifractal features. Physics in Medicine and Biology, 2015.


. Spatial monitoring of groundwater drawdown and rebound associated with quarry dewatering using automated time-lapse electrical resistivity tomography and distribution guided clustering. Engineering Geology, 2015.


. Three-dimensional vessel segmentation using a novel combinatory filter framework. Phys. Med. Biol., 2014.


. Derivation of lowland riparian wetland deposit architecture using geophysical image analysis and interface detection. WRR, 2014.


Recent Posts

Fuzzy Rules for Recursive Bayesian Filtering in Multi-Process State Models ABSTRACT Systems under the influence of uncertain dynamic processes can pose a distinct challenge for predictive estimators, especially in the case where there are multiple non-linear processes influencing the system state to varying degree. In a wide range of application domain problems, including sensing data and target tracking, there are complex system processes that occur simultaneously or consecutively at unknown intervals.



Deep Probabilistic Machine Learning

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.

Development and application of machine learning techniques for characterisation and quantification of change in time-lapse resistivity monitoring

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.


Lead Demonstrator

  • Computer Graphics, Spring Semester 2015-2017
  • Professional Ethics in Computing, Autumn Semester 2016
  • Graphical User Interfaces, Spring Semester 2015
  • Software Quality Metrics, Autumn Semester 2016

Teaching Assistant

  • Professional Ethics in Computing, Autumn Semester 2014-2015

Tutorial Author

  • Computer Graphics, Spring Semester 2016-2017
  • Graphical User Interfaces, Spring Semester 2015

Guest Lecturer

  • Computer Graphics, June 2017