In this talk, I will discuss some of the more general lessons we’ve learned about visualization and data science from domain collaborations, with a focus on work on literary scholarship. These projects highlight that Data Analysis is more than just running a machine learning algorithm to build an accurate model. Working with data is a process with many stages, involving many different kinds of stakeholders, who need to do many different tasks. I will present a framework for thinking about this range of concerns, and use it to consider a number of different approaches we’ve explored to helping people use data analysis. I will discuss a number of visualization tools we’ve built that address problems throughout the data analysis process. I’ll discuss tools for building models, exploring models, comparing models, and validating models.
Many of my examples will come from the Visualizing English Print project, an effort to build approaches that enable literary scholars to bring scalable, data-centric approaches to the study of English literature of the Early Modern period (roughly 1470-1700, including Shakespeare). However, this talk will seek to emphasize the general lessons, rather than the specifics of the domain.
Michael Gleicher is a Professor in the Department of Computer Sciences at the University of Wisconsin, Madison. Prof. Gleicher is founder of the Department’s Visual Computing Group. His research interests span the range of visual computing, including data visualization, robotics, image and video processing tools, virtual reality, and character animation. His current foci are human data interaction and human robot interaction. Prior to joining the university, Prof. Gleicher was a researcher at The Autodesk Vision Technology Center and in Apple Computer’s Advanced Technology Group. He earned his Ph. D. in Computer Science from Carnegie Mellon University, and holds a B.S.E. in Electrical Engineering from Duke University. In 2013-2014, he was a visiting researcher at INRIA Rhone-Alpes. Prof. Gleicher is an ACM Distinguished Scientist.