My research focus is on the design and evaluation of ‘intelligent’ quantified self systems for emotion tracking. In particular, ones that use algorithms to make recommendations or insights about one’s well-being. In addition to this line of research, I’ve conducted research on (and am passionate about) personal productivity, behavior change, and technologies for memory. My earliest research also involved designing and experimentally evaluating the success of different digital interventions for well-being.

graph-2-icon Impact & Usage of Algorithm-Based Insights for Emotional Well-being: One mobile system we developed applies analytics to quantified self data and presents users with visualizations about how factors impact their moods. A one-month field trial with this app showed improvements in daily mood reports for the experimental group. In progress is a qualitative study of how users choose to trust system-based inferences vs. their own beliefs about what factors impact their moods.
stress-2-icon Influence of Machine Feedback on Emotion Perception: Another section of my research looks at how feedback from algorithms which are ostensibly automatic ’emotion trackers’ can influence the emotional appraisal of events. Furthermore, how algorithms for ’emotion sensing’ systems impact memory for that event and, qualitatively, how users react to such feedback.
target-icon The Impact of Emotional Reflection on Behavior Change: One of my early projects showed that simply changing the prompt of a behavior change app to prioritize emotional reflection rather than rich context reporting led to significant improvements in behavior change success for the emotional reflection group.

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