My work focusses on the fields of sparse representations and compressed sensing, and their application to various signal processing, imaging and machine learning problems. During the past decade I have made significant contributions to the key areas of fundamental CS theory, new signal models, including dictionary learning techniques, the development and analysis of new reconstruction algorithms, This work includes: the proposal and analysis of the highly popular Iterative Hard Thresholding family of algorithms for sparse reconstruction; the development of new reconstruction theory for structured sparse signal models; the introduction and analysis for a new model (co-sparsity) for redundant analysis representations; and the characterization of types of statistical distribution that admit accurate low dimensional approximations.
I have applied these ideas to a number of applications including: chemical identification in Raman spectroscopy, dynamic MRI and 3D brain imaging, a new compressed sensing framework for quantitative MRI, electronic surveillance and Synthetic Aperture Radar.
ERC Advanced Grant: C-SENSE, "Exploiting Low Dimensional Models in Sensing, Computation and Processing"
The aim of this project is to develop the next generation of compressive and computational sensing and processing techniques.
UDRC: University Defence Research Collaboration in Signal Processing
An academia led partnership between the defence industry, academia and the government sector. The UDRC develops research in signal processing with application to the defence industry.
CQ-MRI: EPSRC funded award in Compressed Quantitative MRI
The proposed research will provide the first proof-of-principle for a new family of Compressed Quantitative Magnetic Resonance Imaging (CQ-MRI), able to rapidly acquire a multitude of physical parameter maps for the imaged tissue from a single scan.
CIRI: EPSRC funded award in Compressed Imaging in Radio Interferometry
The CIRI project aims to bring new advances for interferometric imaging for next-generation radio telescopes, together with theoretical and algorithmic evolutions in generic compressive imaging.
EU Innovative Training Networks:
SpaRTaN: Sparse Representations and Compressed Sensing Training Network
MacSeNet: Machine Sensing Training Network
I currently have the following opennings:
- 3 year PhD postion in Computational Imaging
Please contact me (firstname.lastname@example.org) for informal enquiries. For full details and how to apply visit our vacancies pages