September 18-20, 2017
University of Bremen
Determining the characteristics of data is a crucial part of all data-driven science. Detecting the boundaries of a region of interest in an image, ascertaining trends in time-series data, and uncovering latent variables and the non-linear but dependent relationships between them are just some examples of problems in signal processing and data analysis. Often low complexity modelling, like sparsity/cosparsity assumptions, (non-)linear dimensionality reduction, manifold learning, etc. underlies the analysis. Other approaches are powered by variational methods, neural networks, Bayesian analysis, computational topology, and more.
The goal of this workshop is to bring together researchers of different aspects of mathematical signal processing and data analysis. The workshop aims to address theoretical, applied, and numerical aspects of data analysis. To encourage younger researchers, there will be a poster session and, pending final funding, travel support.
The workshop is an official event of and supported by the DFG Research Training Group 2224 π3 Parameter Identification – Analysis, Algorithms, Applications and also serves as the annual meeting of the GAMM Activity Group Mathematical Signal and Image Processing
Ulrich Bauer, Geometry and Visualization, Technische Universität München, Germany
Monika Dörfler, Numerical Harmonic Analysis Group, Universität Wien, Austria
Barbara Hammer, Machine Learning Group, Universität Bielefeld, Germany
Valeriya Naumova, Section for Computing and Software, Simula, Norway
Steve Oudout, DataShape Team, Inria Saclay and École Polytechnique, Palaiseau, France
For more information visit http://www.math.uni-bremen.de/cda/GAMM-MSIP2017/