There is an increasing confluence of three major trends in information processing, an umbrella term that subsumes signal processing, machine learning, and statistics. The first one of these trends is "big data," which refers to our ability to continuously collect massive quantities of data across a wide range of modalities. The second trend is that of "distributed processing," which refers to the fact that one often needs to process big data in a distributed fashion due to either processing constraints or distributed nature of the collected data. The third trend is that of "optimization," which points to the fact that information processing problems increasingly rely on optimization theory and solvers for their solutions. While a number of approaches have been proposed in the literature in recent years that collectively account for these three trends, these research findings are spread across different research communities. The purpose of this workshop is to bring together researchers from these research communities (e.g., operations research, signal processing, machine learning, and high-performance computing) for cross fertilization of ideas related to the problem of distributed processing of, and statistical learning from, big data using optimization techniques. In addition to a number of invited research talks, the workshop will also feature a poster session where students and postdoctoral researchers will be able to share their latest research findings related to the theme of the workshop.
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