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Webinar

IPVS ECR Workshop
Mentorship for Early Career Researchers:
What It Really Is -- and How to Thrive in it
 
LINK to SLIDES
International Papillomavirus Society (IPVS) 
February 4, 2026
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Ryan Suk Headshot.jpg

DR. RYAN SUK (석리언)
She/They

"My research mission is OPTIMIZE"

O – Optimize resource allocation
P – Prevent HPV-associated cancers
T – (understand) Trade-offs in health decisions
I – Improve supportive oncology care
M – Model health outcomes using decision analytics and economic evaluation
I – Inform individuals through navigation/information tools
Z – Zero in on barriers to care
E – Evaluate equity and decision-making in healthcare access

I am an Assistant Professor in Health Economics who focuses on data-driven, community-engaged, and process-embedded research to advance health equity and improve healthcare efficiency. My research integrates health economics and decision science to understand and improve how individuals and health systems navigate trade-offs, with the goal of optimizing resource allocation, reducing opportunity costs, and improving decisions under constraints. I focus on improving prevention and care in HPV-associated cancers and supportive oncology, where individuals face informational and access barriers and health systems must allocate limited resources efficiently. For example, I develop microsimulation models for cost-effective palliative oncology, conduct qualitative interviews on healthcare navigation barriers, and design navigation tools that help low-income families access cancer prevention care.

  • Conceptual frame: Health Economics + Decision Science

  • Clinical topic: HPV-associated cancer prevention and control; patient-centered cancer survivorship care

  • Core problem:  trade-offs, opportunity costs, constrained decisions, inequity

  • Method expertise: Economic evaluation & decision modeling; quantitative data analysis; mixed-methods

Suk's Decision Science Lab at Emory University

Data-driven, community-engaged, and process-embedded research for optimizing resources and allocations to improve cancer prevention and reduce cancer health inequity.

- Decision modeling - Economic evaluation - Machine learning - Policy learning

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