Prof. Jared Tanner
Prof. Tanner’s research concerns extracting models of high dimensional date which reveal of the essential information in the data. Specific contributions include the derivation of sampling theorems in compressed sensing using techniques from stochastic geometry and the design and analysis of efficient algorithms for matrix completion which minimise over higher dimensional subspaces as the reliability of the data warrants. These techniques allow more efficient information acquisition as well as the ability to cope with missing data.
Recent interests include new models for low dimensional structure in heterogeneous data and topological data analysis. Prof. Tanner is the Oxford University Liaison Director to the Alan Turing Institute.
Selected Publications
Blanchard, J.D., Tanner, J. and Wei, K. (2015) ‘CGIHT: Conjugate gradient iterative hard thresholding for compressed sensing and matrix completion’, Information and Inference, 4(4), pp. 289–327. doi: 10.1093/imaiai/iav011.
Blanchard, J.D., Tanner, J. and Wei, K. (2015) ‘Conjugate gradient Iterative hard Thresholding: Observed noise stability for compressed sensing’, IEEE Transactions on Signal Processing, 63(2), pp. 528–537. doi: 10.1109/tsp.2014.2379665.
Blanchard, J.D. and Tanner, J. (2014) ‘Performance comparisons of greedy algorithms in compressed sensing’, Numerical Linear Algebra with Applications, 22(2), pp. 254–282. doi: 10.1002/nla.1948.
Bah, B. and Tanner, J. (2014) ‘Bounds of restricted isometry constants in extreme asymptotics: Formulae for Gaussian matrices’, Linear Algebra and its Applications, 441(1), pp. 88–109. doi: 10.1016/j.laa.2012.11.024.
