Extended registration deadline: April 15, 2010.

Submission deadline: May 30, 2010.

Notification of acceptance: June 15, 2010.

Higher-order tensors are becoming increasingly important in signal processing, data mining, scientific computing and many other fields. Tensors and symmetric tensors were actually already studied in the nineteenth century (symmetric tensors were studied as homogeneous polynomials). In the '70s tensors appeared in Psychometrics and Linguistics, where multi-way arrays had to be analyzed. Later on they were also used in Chemometrics, where sets of excitation-emission matrices were stacked in a third-order tensor. In the '90s non-Gaussian signal processing and Higher-Order Statistics became popular, where the basic quantities are higher-order tensors. Around 2000 it was understood that the concept of "diversity" in telecommunication corresponds to the order of a tensor. Exponential signals, which can be considered as the atoms of signal processing, can be represented by rank-1 tensors. Tensors have led to new efficient and accurate computation techniques. Semantic graphs, multilayer networks and hyperlink documents are represented by higher-order tensors. Wherever one starts from a data matrix, one can wonder whether it would not be worthwhile to measure several matrices (under different conditions, at different time instances, etc.) so that one could make use of the more powerful structure of tensor algebra. This shift of paradigm concerns the most diverse aspects of mathematical engineering and goes together with the explosion of available information and the increase in computing power. The development of tensor methods is relevant to countless applications.

This workshop will bring together researchers investigating tensor decompositions and their applications. It will feature a series of invited talks by leading experts and contributed presentations on specific problems. Invited talks are supposed to be broadly accessible, as a help for researchers who are new to the field.

Topics of interest include:

- Mathematical properties of tensor decompositions
- Numerical algorithms for the computation of tensor decompositions
- Tensor-based optimization
- Tensor-based scientific computing
- Computational complexity
- Algebraic geometry
- Representation theory
- Tensor-based signal processing
- Tensor-based machine learning
- Tensor-based data mining
- Independent component analysis, blind source separation and factor analysis
- Applications in chemometrics, psychometrics, econometrics
- Applications in telecommunication
- Applications in sensor array processing
- Applications in bioinformatics and biomedical engineering
- Applications in quantum information theory and quantum computing
- Diffusion tensor imaging
- Multi-matrix methods