Orson Xu (U. of Washington) - Explainable and Personalized Ubiquitous Computing

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With ubiquitous devices becoming more and more intelligent, they can robustly capture low-level behaviors such as daily activities and physical health metrics. However, current devices are still far from understanding and modeling users’ high-level behaviors such as mental wellbeing. A large body of research has emerged with the recent advances in mobile sensing and machine learning techniques. However, explainability and personalization are the two critical challenges before achieving the deployability of behavior models. This talk presents our new explainable and personalized behavior modeling techniques for depression detection. As applications of behavior models, this talk will also cover our recent progress of a novel intervention technique for smartphone overuse that is closely related to mental wellbeing, as well as a new interaction customization technique that fulfills individual needs.