Please join us for the Information Science colloquium with guest, Jeff Rzeszotarski (rez-oh-tar’-ski). Rzeszotarski is a Ph.D. candidate in the Carnegie Mellon University Human-Computer Interaction Institute. He holds a BA in computer science from Carleton College and a MS in human-computer interaction from Carnegie Mellon University. Focusing on the intersection of visualization, crowdsourcing, and social computing, his work has been featured publicly in venues such as TechCrunch and GigaOM, and he has received several Best Paper awards. Jeff is a Siebel Scholar, former Carnegie Mellon Innovation Scholar, former Microsoft Research Graduate Fellow, and cofounder of a data visualization startup, DataSquid.

Title: Revealing Nuances in Data at Scale

Abstract: People are asked to explore and make sense of enormous amounts of information on a daily basis. Faced with the limits of human perception and cognition, many turn to machine learning and data visualization technologies to augment their natural sensemaking abilities. For directed tasks where auser has a precise question or a target to investigate, these technologies work very well. However, for many users who do not yet have clearly defined questions or decision points, the challenge becomes much greater: a system must also help users first develop an understanding of both the data and their own needs.

I will present my work on developing and evaluating systems that allow people to use their own intuition to make sense of complex data. My core approach is to identify and surface meaningful new data features that lead to deeper insights. I will first explore two projects where I curate historical and behavioral data in crowdsourcing platforms to help users understand the processes a crowd uses to generate content. I will then expand on this basic approach, describing ongoing work on visualization systems that use physics metaphors to help users themselves identify useful or important features as they explore. As a part of this discussion, I will consider the role that sensemaking and cognitive psychology play in constructing useful exploratory visualizations. I will conclude the talk by setting a vision for data exploration systems that incorporate feedback, taking action, and sharing discoveries as crucial steps in the exploration process.