Data sketching for large-scale tomography

The Computational Imaging and Data Analytics Group of the School of Engineering at the University of Edinburgh is looking for motivated and talented students who are interested in pursuing full-time research towards a PhD in data analytics for large-scale tomography. This research is motivated by applications of tomographic imaging in biomedicine and non-destructive testing where speed of data acquisition and image reconstruction is critical.   

Data sketching algorithms are becoming increasingly popular in analysing massive data sets, such as those arising in network analytics (internet) and bioinformatics (genomics). In general, these statistical algorithms have the capacity to sample the most informative subsets of data and their respective models, to yield problems of significantly smaller dimensions that can be processed faster with minimal distortion. This project’s ultimate objective is to transfer this computationally efficient framework to the context of tomographic modelling and imaging. In particular, focus will be on sketching of two large-scale (high-dimensional) problems: a sparse linear system usually arising in modelling the tomographic measurement, and a regression problem to yield the reconstructed image given a set of data. The project requires algorithmic design, coding (in Matlab or Python), mathematical analysis of the sketching-induced errors, as well as dissemination of the methods in conference and journal publications. 

The successful candidate will join a vibrant community of researchers working in signal and image processing and will benefit from the interaction with our academic and industrial collaborators. There will also be opportunities to travel to conferences and attend workshops relevant to imaging sciences, inverse problems, data science, and biomedical imaging.

Further Information: 

Closing Date: 

Thursday, May 10, 2018
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Engineering School Logo

Principal Supervisor: 

Eligibility: 

Minimum entry qualification - preferably a first class degree (or International equivalent) in engineering, computer science or mathematics and/or a Masters degree in a computational discipline. Further information on English language requirements for EU/Overseas applicants.

Funding: 

Tuition fees and stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate).

Further information and other funding options.

Informal Enquiries: