The power and prevalence of modern smartphones has quickly made the relationship between people and their computers closer and stronger than ever. With an ever expanding array of onboard sensors, smartphones are not only connected to the user, but also to the user's environment. My research seeks to understand how user interactions and smartphone sensing can allow these systems to be more adaptable and context aware.
As an alumni of the Data Science for Social Good fellowship and a PhD student in the Technology & Social Behavior program at Northwestern University, I have learned to think critically of how we, as researchers, data scientists, developers, and even end-users gain insight from intelligent algorithms, the models they create, the systems they are embedded in, and the interactions we have with these systems. Recently, my research has focused on designing and evaluating user experiences for systems using semi-supervised learning.
mHealth and self-tracking applications are a fast growing area of research and development and show great potential to provide health services to many of those who would otherwise have difficulty getting access. Currently, I am collaborating with the Center for Behavioral Intervention Technology to understand how context-aware technologies might help users to track and manage their mental health.