Please join us for a UW Data Science Seminar event on Thursday, January 19th from 4:30 to 5:20 p.m. PST. The seminar will feature Xuhai “Orson” Xu, a PhD candidate at the University of Washington’s Information School.
“Toward The Next-Generation Health and Well-being: Bridging Behavior Modeling & Intervention”
Abstract: As everyday devices such as smartphones and wearables become more intelligent, they can robustly capture low-level health behaviors such as step count and heart rate. However, they are still far from monitoring or influencing our high-level health behavior, such as mental well-being. Existing AI techniques for longitudinal behavior are still far from being deployable. With depression prediction as the application, this talk presents our recent longitudinal behavior modeling techniques to address the deployability challenge. Based on these models, this talk will also cover our recent work on a novel intervention technique for smartphone overuse, as it is closely related to mental well-being.
Biography: Xuhai “Orson” Xu is a 5th-year PhD candidate at Information School from the University of Washington, advised by Prof. Anind K. Dey and Prof. Jennifer Mankoff. His research interests span human-computer interaction, ubiquitous computing, applied machine learning, and health. His research leverages sensing data from everyday devices and develops deployable machine learning techniques to model daily behavior related to human well-being. Based on behavior models, he also designs new behavior change intervention methods and novel interaction techniques for well-being promotion. His research has won 5 Best Paper, Best Paper Honorable Mentioned, and Best Artifact awards, and has been covered by various media outlets such as ACM News, Hackster.IO, and UW News. He is the Gaetano Borriello Outstanding Student Award Winner of UbiComp 2022.
The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.
The 2022-2023 seminars will be virtual, and are free and open to the public.