Hi, I'm Ray, a 3rd year PhD student at the University of Washington. I design, build, and evaluate human-centered, trustworthy AI systems that support decision-making in high-stakes environments. I'm also a dietitian with experience in mental health and health tech companies.
I am a PhD student studying human-centered AI at the University of Washington, advised by Ari Pollack, Wanda Pratt, and Xuhai “Orson” Xu. My research sits at the intersection of artificial intelligence (AI), human–computer interaction (HCI), and health informatics. Before my doctoral studies, I completed my master’s degree at the University of Michigan and then worked as a dietitian and research scientist across health organizations and tech startups such as Impossible Foods, Unita Health, and Dexcom.
My work combines human-centered design, mixed-methods user research, and AI/ML to study how intelligent systems can support human collaboration and decision-making in multi-stakeholder environments. I am especially motivated by work that measurably improves people’s lived experiences, or produces frameworks that help researchers and practitioners build more responsible, trustworthy AI. My research has appeared in high-impact venues such as ACM CHI conference, AMIA conference, and Nutrients journal.
I’m inspired by the late, great Kobe Bryant’s Mamba Mentality — his relentless commitment to inspiring those around him. If my work resonates with you, I’d love to connect and chat. Outside of research, I enjoy traveling and playing sports, mainly basketball and golf.
Go Blue!Go Dawgs!
Open to Opportunities
Currently seeking internship opportunities in the following areas for Summer/Fall 2026 and beyond!
Generative AIHuman-AI CollaborationResponsible AIAgentic SystemsHealth & Social Technologies
🔭 Current Work
User Trust towards AI Features
Human-AI CollaborationResponsible AI
I developed an interactive QA system using React that presents users with AI-generated responses augmented by trust-enhancing cues and measures whether those signals promote appropriate trust. Findings will inform design guidelines for trustworthy AI interactions that support appropriate rather than blind trust.
Building AI Digital Twins for Human-AI Collaboration
Human-AI CollaborationAgentic Systems
Health consumers increasingly use AI tools in isolation from healthcare providers, generating risks of hallucination, erosion of clinician authority, and fragmented decision-making. We are investigating what it takes to construct accurate digital proxies—AI agents that can represent individuals to support human-AI collaboration in this setting.
AI-Assisted Values Elicitation
Human-AI CollaborationResponsible AI
My team developed a React application that uses LLMs to elicit, extract, and visualize individual values from open-ended conversation, mapping them onto a shared representational space. This work explores the capacity of current LLMs to act as value mediators, bridging personal values and goals between humans to support collaborative decisions.
4. Understanding Perspectives of Patients, Caregivers and Clinicians towards Emerging Collaborative-decision Making Technologies
Responsible AIHealth InformaticsUser Experience Research
Ray-Yuan Chung, Athena Ortega, Zixuan Xu, Daeun Yoo, Jaime Snyder, Wanda Pratt, Aaron Wightman, Ryan Hutson, Cozumel Pruette, Ari Pollack. The Workshop on Interactive Systems in Healthcare (WISH), AMIA (2025).
Generative AIResponsible AIUser Experience Research
Luna Xingyu Li, Ray-Yuan Chung, Feng Chen, Wenyu Zeng, Yein Jeon, Oleg Zaslavsky. The Workshop on Cultural and Linguistic Diversity in AI Applications (CALD-AI), ASIS&T (2025).
