I'm a researcher at IBM T. J. Watson Research Center. My current research focuses around two themes: 1) Automating visual data exploration for scalable, guided data analysis and 2) developing interactive data science tools for iterative visual data and model experimentation. The goal underlying both themes is to help people rapidly explore, understand and reason about their data as well as models. To solve data visualization and visual analysis problems, I build on insights and techniques from perceptual psychology, statistical machine learning, geometry, and topology. This approach is also exemplified in my postdoctoral research at Stanford, which focused on foundational constructs for data visualization to operationalize automated visualization design, as well as my PhD research at Brown, which contributed models, techniques, and interactive tools for exploring and understanding structural brain connectivity. I have extensive experience in building visualization systems and applying machine learning and crowdsourcing to solve data visualization and visual analysis problems in domains.
To learn more, see http://hci.stanford.edu/~cagatay/.