The goal of the workshop is to bring together researchers and practitioners interested in sharing their experiences in practical machine learning or workflow systems they have developed for machine learning applications for tasks as diverse as fusing multimodal information to analyzing web content to performing genomics analyses. Our hope is to have participants develop a better sense of the variety and scope of different workflows available, as well as an appreciation for some of the workflow tools and approaches other scientists have found useful. Our ultimate objective would be to provide a forum for cross-fertilization of ideas by surveying how a wide variety of machine learning practitioners have started to harness the power of workflows in their own domains. In particular, the workshop will aim to explore new and existing workflows, and approaches for using workflows, to conduct various machine learning tasks in a myriad of different scientific fields. We will thus solicit papers to share the challenges, experiences, and solutions developed in applying workflows to accomplish machine learning tasks.

In particular, we're looking for papers on using scientific workflows for machine learning applications including:

• Cutting edge research
• Works in progress
• Practical applications in industry or academia