Jared Tanner is Professor of the Mathematics of Information at the University of Oxford and the Turing University Lead for Oxford. He obtained his PhD (2002) in applied mathematics at the University of California at Los Angeles, and was a postdoctoral fellow at the University of California at Davis (Maths) and Stanford University (Stats.) where he worked with David L. Donoho. Prior to joining the University of Oxford in 2012 he was Professor of the Mathematics of Information at the University of Edinburgh (2007-2012).
He is founding editor-in-chief of Information and Inference: A Journal of the IMA, whose mission is to publish high quality mathematically oriented articles furthering the understanding of the theory, methods of analysis, and algorithms for information and data. He is also on the editorial board for Applied and Computational Harmonic Analysis, Multiscale modelling and simulation A SIAM Interdisciplinary Journal, and was an associate editor for the Princeton Companion to Applied Mathematics. His research has appeared in the Proc Natl Acad Sci USA, Phil Trans Royal Soc A, and other leading journals.
Jared 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.