Keynote: Towards a Unifying Theory of Data, Task, and Visualization with a Grammar of Hypothesis
Abstract: In this talk, I present our recent work on developing a unifying theory that encompasses data, visualization, and analysis (tasks) based on a grammar of a (scientific) hypotheses. The grammar provides a mechanism to consider data, task, and visualization as “hypothesis spaces.” A “data hypothesis space” is a space of all the hypotheses that a dataset can be used to answer, a “visualization hypothesis space” is the space of hypotheses that a visualization can be used to validate, and an “analyst hypothesis space” is the space of the hypotheses that an analyst would like the answer to. With the hypothesis grammar, we can examine the relations between the three spaces and their practical implications.
In addition, with the formalization of a grammar, we can reconsider some classic research topics central to visualization research. For example, visualization recommendation can be thought of as finding a visualization that maximizes the intersection between the visualization hypothesis space and the others. Evaluating a visual analytics system can be thought of as evaluating the system’s capability to support a user in exploring a data hypothesis space. I will present the foundation of our grammar and introduce some promising new research directions that may become possible with our proposed formalization.
Bio: Remco Chang is an Associate Professor in the Computer Science Department at Tufts University. He received his BA from Johns Hopkins University in 1997 in Computer Science and Economics, MSc from Brown University in 2000, and PhD in Computer Science from UNC Charlotte in 2009. Prior to his PhD, he worked for Boeing developing real-time flight tracking and visualization software, followed by a position at UNC Charlotte as a research scientist. His current research interests include visual analytics, information visualization, HCI, and databases. His research has been funded by the NSF, DARPA, the Walmart Foundation, Army, Navy, DHS, MIT Lincoln Lab, and Draper. He has had best paper, best poster, and honorable mention awards at InfoVis, VAST, CHI, and VDA. He is currently an associate editor for the ACM TiiS, and he is the papers chair for the IEEE Visual Analytics conference (VAST) in 2018 and 2019. He received the NSF CAREER Award in 2015. He has supervised 3 PhD students, co-supervised 5 PhD students, and mentored 3 postdoctoral researchers, some of whom became professors in Computer Science at Smith College, DePaul University, Washington University in Saint Louis, University of Maryland, the University of San Francisco, Bucknell University, San Francisco State University, and the University of Utrecht (Netherlands).