Papers
2
Total Citations
17
H-Index
2
About
You Wu’s research sits at the intersection of computer vision, human-computer interaction, and healthcare AI, with a particular focus on egocentric perception and digital health innovation. Their most notable contribution is the development of **AssistQ**, an affordance-centric, question-driven framework for task completion in egocentric assistant systems. This work, published in 2022 and garnering 15 citations, addresses the critical challenge of enabling AI to understand human intent and physical affordances from a first-person perspective, paving the way for more intuitive wearable assistants. Beyond core vision research, Wu has been instrumental in advancing global digital health, co-organizing the PRC-HI 2024 conference and contributing to its proceedings, which explore transformative healthcare technologies. This dual expertise—bridging foundational AI for egocentric interaction with applied digital health solutions—positions Wu as a researcher who not only pushes technical boundaries but also translates them into real-world impact. Their work is particularly relevant for students and researchers interested in embodied AI, assistive technologies, and the future of intelligent healthcare systems.
Research Focus
Key Achievements
Top Papers
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