These are some of my previous research projects from academia.
At Microsoft Research, I investigated a physiological sensor data approach to providing just-in-time support for emotional eating. This work includes investigating emotional eating behaviors, exploring intervention techniques, and training machine learning classifiers to detect emotional responses. Physiological sensors were inserted into women's bras.
ITMC is a computational approach for studying the temporal, creative work process. Specifically, I use electroencephalography (EEG) and machine learning to detect moments of high creative experience. The motivation for this computational approach is for the evaluation of digital creativity support tools.
The Creativity Support Index is a psychometric tool designed for the purpose of evaluating creativity support tools. It was extensively developed and validated using a psychometric approach. More information about the CSI, including resources to download an electronic version, can be found here.
Dance.Draw was a project funded by the NSF CreativeIT program. The primary goals of this project involved developing technology for tracking the position and movements of dancers, to be used as input into interactive visualizations; developing a web-based, collaborative tool for annotating dance videos (i.e. The Choreographer's Notebook); and understanding audience engagement with physiological sensors. In this project, I was involved in the evaluation of the Dance.Draw project as a whole, as well as employing physiological measurements for investigating audience responses to performing arts. These research directions resulted in two ACM CHI papers. I was also involved in the evaluation of the Choreographer's Notebook, which was published to ACM Creativity & Cognition and ACM CSCW.