Toward The Next-Generation Health and Well-being: The Synergy of Behavior Modeling and Intervention

Where: (in-person) Bob and Betty Beyster Building (BBB on North Campus) 3725

 (remote access) Zoom: https://umich.zoom.us/j/95319665875 (passcode: misc2324)

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, 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.

Xuhai “Orson” Xu is a postdoc at MIT EECS. He received his PhD at the University of Washington. His research straddles multiple disciplines, including human-computer interaction, ubiquitous computing, machine learning, and health. By leveraging everyday data from various sources, Xu develops deployable machine learning algorithms to model long-term human behavior related to well-being. Based on these behavior models, he designs new human-AI interaction and intervention techniques that benefit domain experts and support end-users to promote well-being. Xu has earned several awards, including 8 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 recognized as the Gaetano Borriello Outstanding Student Award Winner at UbiComp 2022. https://orsonxu.com/

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