I study personal informatics (PI) technologies with specific attention towards how these emerging technologies impact self-perception and emotional well-being. In addition to this research, I’ve had the fortunate opportunity to study knowledge worker productivity for two summers as a UX Researcher intern at Google. A few research highlights:

graph-2-icon Impact & Usage of Analytics for Emotional Well-being: Designed, prototyped, and deployed a novel web application for emotion analytics in a one-month, nation-wide field experiment. Conducted a separate mixed-methods interview study to contrast app analytics against user beliefs. Thematically coded interviews to develop a framework of judgement strategies for how users determine the accuracy of personal analytics. Presented findings at HCIC.
stress-2-icon Influence of Machine Feedback on Emotion Perception: Led a team of computer science students to build a system to detect and provide feedback of physiological arousal. Designed and conducted an in-lab experiment to study how alternative frames of this same system impact stress perception. Triangulated results across qualitative and quantitative measures to demonstrate the negative effects of one particular system design. Further analyzed qualitative responses to report the high trust that users have for inferential self-tracking systems. Presented findings in multiple UCSC courses and published results in the IMWUT journal.
stress-2-icon Implicit Beliefs in Self-Tracking Technologies: Designed and conducted a 5-part study to explore the effects of implicit beliefs in self-tracking systems. This projected involved a competitive analysis of commercial applications, creating app UI mocks, and multiple online studies to validate our app designs. Conducted subsequent in-lab and field experiments with alternative systems for mood- or stress-tracking, followed by user interviews.
target-icon The Impact of Emotional Reflection on Behavior Change: Designed and conducted a one-month field study comparing the behavior change success rates of two technology-based interventions (traditional and emotion-focused prompts). Results were triangulated from three quantitative measures for behavior change success and from user interviews. Presented findings at the CHI conference and at IBM, highlighting design implications for fitness trackers.

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