Yiyang Liu,

I see myself as an epidemiologist and data scientist specializing in HIV and substance use research. My Ph.D. training is in psychiatric epidemiology, and most of my doctoral research involves identifying behavior risk factors of substance use and use disorder. My postdoctoral training focuses on leveraging data science and advanced modeling methodologies to study the intersection of HIV and substance use. Currently, I am a Research Assistant Professor at the University of Florida (UF), College of Public Health and Health Professionals, Department of Epidemiology.

I have rich experience working with OneFlorida+ (OneFL) electronic health records (EHR) and contributed to several HIV-related projects using OneFL EHRs. In one of my first-authored papers, I have developed and validated a rule-based HIV computable phenotype algorithm that leverages the multi-domain structured electronic health record (EHR) data and has identified over 70k patients with confirmed HIV diagnoses. Building on this virtual HIV cohort, I have been awarded an industry fund as a primary investigator (PI) to examine long-acting HIV antiretroviral therapy uptake among people with HIV. Additionally, I have participated as a co-investigator on a UF Clinical and Translational Science Institute (CTSI) pilot grant and led efforts in one of the aims to identify and characterize PrEP users using OneFL EHRs. I am currently preparing an R34 grant aiming to build an EHR-based HIV prediction model to better identify patients who might benefit from PrEP, especially among women.

0000-0002-5519-3853

• Big data analytics
• HIV
• HIV Prevention
• data science
• substance use