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Application of compressed sensing to biomolecular NMR spectroscopy

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If you have a question about this talk, please contact Dr Vivien Gruar.

Reconstruction of incompletely sampled NMR data is an area of considerable interest within Biomolecular NMR spectroscopy enabling improvements in resolution with significant time savings, and is attracting considerable interest from mathematicians with the launch this year of an international competition for reconstruction of undersampled NMR data supported by Stanford Professor of Statistics, David Donoho. Compressed sensing (CS) reconstruction is a particularly popular method within NMR spectroscopy. We have a software package and accompanying GUI for CS reconstruction in use and are looking to extend this further. Principally we are interested in (i) new and improved algorithms for biomolecular NMR (ii) incorparation of prior information in CS reconstructions (iii) understanding the optimal sampling requirements for different experiments. The project could incorporate one or more of the above proposals. (iii) would require the further development of code to generate suitable sampling patterns for NMR data along with an accompanying GUI for ease of use by practising NMR spectroscopists.

This talk is part of the Cambridge Mathematics Placements Seminars series.

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