|Wednesday, December 7|
|10:00 - 11:00|
|Keynote: SPBD-K1: Jelena Kovačević - Sampling on Graphs|
|11:00 - 12:20|
|SPBD-1: Signal Processing of Big Data I|
|14:00 - 15:40|
|SPBD-2: Signal Processing of Big Data II|
|16:10 - 17:30|
|SPBD-3: Signal Processing of Big Data III|
|Thursday, December 8|
|10:00 - 11:00|
|Keynote: SPBD-K2: Aleksandra Mojsilovic - Data 4 Good|
With the explosive growth of information and communication, signals are generated at an unprecedented rate from various sources, including social, citation, biological, and physical infrastructure, among others.
Unlike time-series signals or images, these signals possess complex, irregular structure, which requires novel processing techniques leading to the emerging field of signal processing on graphs.
Signal processing on graphs extends classical discrete signal processing to signals with an underlying complex, irregular structure. The framework models that underlying structure by a graph and signals by graph signals, generalizing concepts and tools from classical discrete signal processing to graph signal processing. I will talk about graph signal processing, and, in particular, the classical signal processing task of sampling and interpolation within the framework of signal processing on graphs. As the bridge connecting sequences and functions, classical sampling theory shows that a bandlimited function can be perfectly recovered from its sampled sequence if the sampling rate is high enough. I will follow up with a number of applications where sampling on graphs is of interest.
The social good movement has taken root with many a corporation, entrepreneur and big thinker, with the simple aim of using technology to help create a better world. Data analytics, signal processing and related disciplines present one increasingly important way in which social good can be made possible and new communities are growing around it, fueled in large part by the fact that we are no longer constrained by data. Everything from Internet activity, satellite imagery, social media, health records, news, scientific publications, economic data, weather data, and government records is at our fingertips, giving us an unprecedented opportunity to change the world for the better using data sciences. From reducing or eliminating inequalities, to improving access to health care and education, to reducing pollution and our carbon footprint, the opportunities are endless. In this talk, Saška will give an overview of the emerging area of data science for social good. She will illustrate how the state of the art signal processing toolkit (e.g. prediction, classification, optimization, visualization, NLP) is driving new social good applications, and will present a broad range of innovative examples of doing good with data. She will explore the interdisciplinary nature of social good projects, and highlight data and algorithmic challenges that might call for new research directions.
Signal processing is becoming a central discipline in the study and analysis of big data, with the emergence of datasets having volume, variety, velocity and veracity in countless applications and industries. This symposium aims to bring together researchers and experts in the field of signal and information processing for improving future big data systems.
Submissions are welcome on topics including:
Prospective authors are invited to submit full-length papers, with up to four pages for technical content including figures and possible references, and with one additional optional 5th page containing only references. Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. Submission is through the GlobalSIP website at http://2016.ieeeglobalsip.org/Papers.asp.
|Paper Submission Deadline|
|Review Results Announced|
|Camera-Ready Papers Due||September 30, 2016|