计算机学院系列讲座菁英论坛第32期——Human-AI Ecosystem for Health and Wellbeing
报告题目(Title):Human-AI Ecosystem for Health and Wellbeing
时间(Date & Time):2024/07/10 10:30-11:30
腾讯会议:558-175-483
主讲人(Speaker):Xuhai "Orson" Xu
邀请人(Host):Daqing Zhang
报告摘要(Abstract):
As the intelligence of everyday smart devices continues to evolve, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent health monitoring and intervention pipeline seems to be within reach. How do we get there?
In this talk, I will introduce a comprehensive pipeline that connects AI, end-users, and health experts. For end-users, I will introduce our work that bridges behavior science theory-driven intervention designs and generalizable behavior models. I will also introduce my efforts on passive sensing datasets, human-centered algorithms & large language models (LLMs), as well as a benchmark platform that drives the community toward more robust and deployable health systems for both end-users and experts.
主讲人简介(Bio):
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Xuhai "Orson" Xu is an incoming tenure-track assistant professor at Columbia University. He is currently a postdoc at MIT and received his PhD at the University of Washington. Specializing in human-computer interaction, applied machine learning, and health, Xu develops deployable behavior modeling algorithms to monitor various health and well-being conditions using everyday sensor data and health records. He further designs and deploys intelligent intervention & interaction techniques that help users achieve personal health and well-being goals and support health experts in making decisions. Xu has earned several awards, including 10 Best Paper, Best Paper Honorable Mention, and Best Artifact awards. His research has been covered by media outlets such as the Washington Post and ACM News. He was recognized as the Outstanding Student Award Winner at UbiComp 2022, the 2023 UW Distinguished Dissertation Award, and the 2024 Innovation and Technology Award at the Western Association of Graduate Schools.
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