- Computational Social Science
- Critical Data Studies
- Data Science
- Economics and Information
- Education Technology
- Ethics, Law and Policy
- Human-Computer Interaction
- Human-Robot Interaction
- Incentives and Computation
- Infrastructure Studies
- Interface Design and Ubiquitous Computing
- Natural Language Processing
- Network Science
- Social Computing and Computer-supported Cooperative Work
- Technology and Equity
Please join us for Information Science guest, Çağatay Demiralp. Çağatay Demiralp is a researcher at IBM T. J. Watson Research Center. Çağatay’s research focuses on two themes: Building foundations for data visualization and developing new tools and techniques for interactive visual data analysis. Recently he was a postdoctoral scholar in Computer Science at Stanford University and a member of the Interactive Data Lab at the University of Washington. Çağatay obtained his PhD from Brown University.
Title: Visual Data Analysis: Perceptual Foundations and Biomedical Applications
Abstract: Visualizations facilitate data analysis by leveraging visual perception to support exploration and reasoning. My research seeks to extend the theoretical and perceptual foundations of data visualization and apply that understanding in the design of new visual analysis tools.
Driven by fast-developing, high-throughput data acquisition technologies, biomedicine is becoming increasingly data intensive. In the first part of my talk, I’ll present two interactive tools for visual analysis of complex biomedical data. I’ll demonstrate how both tools help domain experts explore patterned structures in data effectively by reducing the complexity with novel visual encoding and interaction techniques. In the second part of my talk, I will consider what—in measurable terms—constitutes a “good”
visualization. I’ll propose visual embedding as a new model of data visualization, claiming that good visualizations should perceptually preserve structures in data. I’ll then introduce perceptual kernels, distance matrices derived from aggregate perceptual judgments, to further operationalize the visual embedding model. I’ll discuss how to best elicit perceptual kernels by presenting results from a large-scale crowdsourcing study we conducted. I’ll finally demonstrate how perceptual kernels can be applied to improve visualization design through automatic palette optimization and by providing distances for visual embedding.