We are pleased to invite you to the IEEE SPS Summer School on Signal Processing and Machine Learning for Big Data, to be held in Pittsburgh, PA, USA, May 17 - 19, 2016.
Motivation and description Humans, machines and sensors collectively generate an enormous amount of data on a daily basis. The fact that much of this data is now accessible provides an opportunity to explore, analyze and extract previously unavailable and potentially highly useful information. In many cases, the volume and speed of data generation makes traditional centralized data analysis infeasible. The lack of structure, and the amount of noise and outliers emphasize the need for robust processing across heterogeneous data domains. High dimensionality makes it challenging to visualize and interpret the data. Overall, Big Data analysis presents many challenges and opportunities for current and future signal processing professionals. This Summer School is intended to provide an introduction to the current efforts to explore Big Data from a signal processing perspective. Topics will range from foundations for Big Data analysis and processing (robust statistical methods, sparse representations, numerical linear algebra, machine learning, convergence and complexity analysis) to Big Data applications (social networks, behavior and language analysis, bioinformatics, smart grid, environmental monitoring, and others).
Questions should be directed to Ervin Sejdic (esejdic at pitt.edu) or Murat Akcakaya (akcakaya at pitt.edu)