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 Scaling up Personalized Health Support in Everyday Life Speaker (s):
Yuhan Luo
Assistant Professor,
City University of Hong Kong
| Date: Time: Venue: | | 8 July 2024, Monday 1:30pm – 2:30pm School of Computing & Information Systems 2 (SCIS 2)
Level 3, Seminar Room 3-2
Singapore Management University
90 Stamford Road
Singapore 178903 Please register by 7 July 2024. We look forward to seeing you at this research seminar. 
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About the Talk Designing effective health support tools, such as food recommendation systems, fitness planners, and mental health companions, warrants a holistic understanding of individuals’ situations, encompassing their daily activities, health conditions, lifestyles, and social environments. However, achieving such a level of personalization is challenging. Partially, collecting and analyzing various personal health data is resource-consuming. Moreover, individuals without domain expertise often face challenges in interpreting these data and understanding the implications, not to mention making informed decisions to improve their health.
The recent surge of Large Language Models (LLMs), with their remarkable text and image generation ability, holds promise to overcome these barriers. By gathering various health-related data from users, LLMs can generate relevant health summaries and recommendations without excessive data training. Despite the promise, it is unclear how researchers and developers can make the best use of LLMs in personalized health support, given the emerging concerns about privacy, transparency, and hallucination.
In this project, we bring together experts in computer science, human-computer interaction, and health professionals to scale up personalized health support leveraging LLMs in several health contexts: diet, exercise, and mental health support. Specifically, we aim to (1) give users the agency to customize their ideal health companion, (2) evaluate the effectiveness of LLM-powered health support systems through field studies, and (3) address emerging issues with LLMs’ generated content regarding hallucinations and transparency.
About the Speaker Yuhan's research seeks to enhance individuals' everyday health and well-being by unlocking the potential of ubiquitous computing technologies. She builds multimodal systems (e.g., speech interfaces, chatbots) to support self-tracking, designs interventions to encourage health behaviors, and explores opportunities for utilizing personal data in healthcare contexts. Yuhan received her Ph.D. in Information Studies from University of Maryland in 2022, MS in Information Science and Technology from Penn State in 2017, and BEng in Computer Science from Southeast University in 2015, respectively. She was a UX researcher intern at Meta in 2020 and Google in 2019. |
