My research interests lie in the intersection of hearing science, audiology, and signal processing, with a particular focus on:
Bio-inspired Auditory Modeling: Developing computational models of the auditory system to better understand how humans process sound and how hearing impairments affect this process.Speech Intelligibility in Noise: Investigating how noise affects speech understanding in both normal-hearing and hearing-impaired individuals, and developing algorithms to improve speech intelligibility in noisy environments.Cochlear Implants and Hearing Aids: Developing and evaluating signal processing strategies for cochlear implants and hearing aids to improve speech perception and sound quality for hearing-impaired individuals.Auditory Evoked Potentials: Investigating the use of auditory evoked potentials as an objective measure of auditory function and as a tool for evaluating the effectiveness of auditory rehabilitation.Psychoacoustics: Studying the relationship between the physical properties of sound and the perceptual experience of hearing, with a focus on how hearing impairments affect this relationship.
My research combines experimental, computational, and clinical approaches to address these key areas, with the ultimate goal of improving the quality of life for people with hearing impairments.
I develop and study advanced algorithms that can solve challenging inverse problems by efficiently exploiting complex prior information. Using techniques from mathematics, statistics and machine learning, my work concentrates primarily on problems in x-ray tomographic image reconstruction and modelling.
I work closely with state-of-the-art imaging facilities (µ-VIS, the National Research Facility in Lab-based XCT, the UK’s synchrotron facility at the Diamond Light Source, and ISIS neutron imaging beamline) to find practical solutions to a range of important scientific problems from plant science to manufacturing.
My research interests cover areas such as: Theoretical and computational methods for Signal and Image Processing (Machine Learning, Compressed Sensing, Statistical Signal and Image Processing, Quantum Computing, Inverse Problems, Optimisation, X-ray Tomographic Imaging); Advanced tomographic imaging strategies: (limited angle tomography and laminography, Spectral X-ray imaging, Stereo and extreme limited view tomography); Efficient computational methods for tomographic reconstruction, including GPU acceleration, distributed computation and advanced optimisation strategies, Constrained optimisation for ill-conditioned and underdetermined tomographic inverse problems, Applications of X-ray tomography to the inspection of manufactured components, Multimodal tomographic imaging
