Data+Experience - Tools & Strategies for Social Data Analysis
Friday, March 21, 2014 - 8:45am - 9:45am KEC 1005 Wesley Willett Post-Doctoral Researcher AVIZ group INRIA-Saclay Abstract: There is an overwhelming amount of data all around us. However, individuals often don't have the time, knowledge, technical expertise, or diversity of perspectives to tackle large data analysis tasks on their own. As a result, data analysis tools must increasingly support collaboration and enable social interactions around data. Multiple analysts working on a dataset may need to share questions or views of the data with one another or gather their findings for presentation. Alternately, communities with local knowledge or a vested stake in the data may wish to engage in the analysis process. Analysts may also wish to enlist pools of crowd workers to perform specific analysis tasks at scale. Wes's ongoing research explores the process of social data analysis and provides tools and strategies that integrate collaborative activities into a variety of different analysis settings. This talk will explore (1) social visualization tools that allow analysts to organize findings and f! acilitate deeper analytic reasoning, (2) approaches for scaffolding analysis in novice communities to enable participation and cultivate local knowledge, (3) tools and strategies for using paid crowds to generate explanations and insights, and (4) design explorations that examine the relationship between digital note-taking, analysis, and collaboration.Biography: Wesley Willett is a post-doctoral researcher in the AVIZ group at INRIA-Saclay in Paris, France. Wes's interests span information visualization, social computing, new media, and human computer interaction, and his research focuses on pairing data and interactivity to support collaboration, learning, and discovery. In particular, his work explores the intersection of visualization and computer-supported collaborative work and shows how communities, crowds, and novice users can work together build knowledge from data. This work has earned a number of awards, including the Best Paper award at Pervasive 2010 and a Best Paper Honorable Mention at CHI 2011. Wes received his BS in Computer Science from CU Boulder in 2006 and his Ph.D. in Computer Science from UC Berkeley in 2012, where he was advised by Dr. Maneesh Agrawala. He has also engaged in visualization and interaction research at Adobe, Intel Labs, and Google Research.
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