GlobalSIP 2013 Symposium on: Low-Dimensional Models and Optimization in Signal Processing

Marco Duarte sent me the following: 

Hi Igor - I wanted to bring to your attention (and to that of the readership of Nuit Blanche) a symposium that I'm co-organizing in topics that should be of particular interest to you.

The website and call for papers are available at http://www.ieeeglobalsip.org/sym/13/ldmosp

Regards,

Marco F. Duarte

Assistant Professor

Department of Electrical and Computer Engineering

University of Massachusetts Amherst

mduarte@ecs.umass.edu

http://www.ecs.umass.edu/~mduarte/

Thanks Marco, from the page:

GlobalSIP 2013 Symposium on:

Low-Dimensional Models and Optimization in Signal Processing

[Download the PDF Call for Papers]

Sparsity, low-rank, and other low-dimensional geometric models have long been studied and exploited in machine learning, signal processing and computer science. For instance, sparsity has made an impact in the problems of compression, linear regression, subset selection, graphical model learning, and compressive sensing. Similarly, low-rank models lie at the heart of a variety of dimensionality reduction and data interpolation techniques. Additionally, manifold models are able to succinctly characterize complex signal classes that exhibit few degrees of freedom.

The goal of this symposium is to showcase recent developments in the formulation of: (i) new low-dimensional models for high-dimensional data that are concise enough to be informative in signal recovery and information extraction, where examples include sparsity, low-rank matrices, and manifold models; (ii) new signal recovery and information extraction algorithms, in particular optimization-based approaches, that promote solutions well-matched to the aforementioned signal models, where examples include greedy and iterative algorithms, optimization solvers, and heuristic methods; and (iii) new applications where where low-dimensional signal models outperform existing signal processing, signal estimation, and data recovery approaches in the computational, fidelity, or measurement requirement realms.

Submissions of at most 1 page in two-column IEEE format are welcome on topics including:

• Dimensionality Reduction
• Algorithms for Signal Processing
• Signal Models
• Signal Processing
• Compressive Sensing

Paper Submission

Submit papers of at most 1 page in two-column IEEE format through the GlobalSIP website athttp://www.ieeeglobalsip.org/Papers.asp.

Important Dates

Paper Submission Deadline	June 1, 2013
Review Results Announce	July 30, 2013
Camera-Ready Papers Due	September 7, 2013

Organizing Committee

General Co-Chair

Volkan Cevher

Ecole Polytechnique Federale de Lausanne

General Co-Chair

Justin Romberg

Georgia Institute of Technology

Technical Co-Chair

Mark Davenport

Georgia Institute of Technology

Technical Co-Chair

Marco Duarte

University of Massachusetts Amherst

The Symposia will also include those other sessions:

• Advancing Neural Engineering Through Big Data
• Bioinformatics and Systems Biology
• Controlled Sensing For Inference: Applications, Theory and Algorithms
• Cyber-Security and Privacy
• Emerging Challenges in Network Sensing, Inference, and Communication
• Energy Harvesting and Green Wireless Communications
• Graph Signal Processing
• Information Processing in the Smart Grid
• Information Processing over Networks
• Low-Dimensional Models and Optimization in Signal Processing
• Low-Power Systems and Signal Processing
• Millimeter Wave Imaging and Sensing
• Mobile Imaging
• Network Theory
• New Sensing and Statistical Inference Methods
• Optimization in Machine Learning and Signal Processing
• Signal and Information Processing in Finance and Economics
• Software Defined and Cognitive Radios

Join the CompressiveSensing subreddit or the Google+ Community and post there !

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