Title: Precision Medicine in HIV

Abstract: The use of antiretroviral therapy (ART) has significantly reduced HIV-related mortality and morbidity, transforming HIV infection to a chronic disease with the care now focusing on treatment adherence, comorbidities including mental health, and other long-term outcomes. Since combination ART with three or more drugs of different mechanisms or against different targets is recommended for all people living with HIV (PWH) and they must continue on it indefinitely once started, understanding the long-term ART effects on health outcomes and personalizing ART treatment based on individuals’ characteristics is crucial for optimizing PWH’s health outcomes and facilitating precision medicine in HIV. In this talk, I will present reinforcement learning (RL) methods designed to learn and understand the impact of ART on the health outcomes of PWH, and explore the future of HIV care through incorporating large language models (LLM) with RL.

Bio: Dr. Yanxun Xu is an Associate Professor and Joseph & Suzanne Jenniches Faculty Scholar in the Department of Applied Mathematics and Statistics, Division of Biostatistics in the School of Medicine at Johns Hopkins University. Her research interests lie in developing theory and methods for a broad range of problems, such as reinforcement learning, high-dimensional data analysis, Bayesian nonparametric statistics, and uncertainty quantification. She also develops new statistical and machine learning methods for various applications, including electronic health records, dynamic treatment regimens, cancer genomics, early detection of Alzheimer’s disease, mental health in people with HIV, and early-phase clinical trial designs. Her research has been continuously funded by NSF, NIH, and industries.