Abstract:
While the intelligence of everyday smart devices (e.g., smartphones and wearables) continues to evolve, they can already capture basic health behaviors such as physical activities and heart rates. However, we still face significant challenges in leveraging these devices for modeling and influencing long-term behavior, such as mental well-being. Existing AI techniques for longitudinal behavior are still far from being deployable.
In this talk, I will describe innovations in machine learning approaches for building longitudinal behavior models. These novel approaches will address key issues in AI deployability, such as cross-dataset generalizability and interpretability, in the context of mental health. Based on these models, I will further present novel intervention techniques that influence behavior and promote mental well-being.
 

Bio:
Orson Xuhai Xu is a 5th-year PhD candidate in the Information School at the University of Washington, advised by Anind K. Dey and Jennifer Mankoff. His research straddles multiple disciplines, including human-computer interaction, ubiquitous computing, machine learning, and health. By leveraging sensing data from everyday devices, Xu develops novel, deployable machine learning algorithms to model long-term human behavior related to well-being. Based on these behavior models, he designs new behavior change intervention methods and novel interaction techniques for promoting well-being. Xu has earned several awards, including 5 Best Paper, Best Paper Honorable Mention, and Best Artifact awards. His research has been covered by media outlets such as The Washington Post, ACM News, and UW News. He was recently recognized as the Gaetano Borriello Outstanding Student Award Winner at UbiComp 2022.