Uploaded May 2026 | Updated September 2026, 1 week ago
Yanxun Xu, Jenniches Faculty Scholar, is a professor of applied mathematics and statistics and an adjunct professor in the Division of Quantitative Sciences at the Sidney Kimmel Comprehensive Cancer Center at the School of Medicine and a member of the Data Science and AI Institute.
Xu specializes in the intersection of Bayesian statistics and artificial intelligence, with contributions in reinforcement learning, high-dimensional data analysis, nonparametric statistics, and uncertainty quantification. Her methods have been successfully applied in intelligent health care, including clinical trial designs, cancer genomics, early disease diagnosis—such as predictive models for Alzheimer’s disease—and electronic health records analysis.
She actively serves the scientific community on the Executive Committee of the International Society for Bayesian Analysis and as an editor of Bayesian Analysis, alongside roles as an associate editor for several other leading statistical journals. Her research is continually funded by the National Science Foundation, the National Institutes of Health, and industry partners. This support sustains a complete innovation pipeline, from theory to clinical translation, and advances the scientific application of artificial intelligence in health care.
Description of "From Data to Optimal Decisions: Bayesian Learning and AI for Personalized Medicine":
Modern health care and clinical research generate vast amounts of complex, fragmented data, but translating this raw information into reliable clinical insights remains challenging. This talk explores the synergy between statistics, artificial intelligence, and biomedicine to bridge the gap between messy real-world health data and the precise, individualized decisions that optimal patient care demands. Xu will discuss how Bayesian statistics and reinforcement learning can be leveraged to optimize complex treatment regimes, such as identifying the optimal drug combinations for improving the quality of life for people living with HIV. She will also introduce AI-powered tools that automate the synthesis of high-fidelity clinical data to support advanced survival modeling and subgroup inference. Together, these advances illustrate how mathematical innovation, paired with deep clinical insight, is reshaping our understanding of disease and our capacity to personalize treatment for every patient.
Yanxun Xu, Jenniches Faculty Scholar, is a professor of applied mathematics and statistics and an adjunct professor in the Division of Quantitative Sciences at the Sidney Kimmel Comprehensive Cancer Center at the School of Medicine and a member of the Data Science and AI Institute.
Xu specializes in the intersection of Bayesian statistics and artificial intelligence, with contributions in reinforcement learning, high-dimensional data analysis, nonparametric statistics, and uncertainty quantification. Her methods have been successfully applied in intelligent health care, including clinical trial designs, cancer genomics, early disease diagnosis—such as predictive models for Alzheimer’s disease—and electronic health records analysis.
She actively serves the scientific community on the Executive Committee of the International Society for Bayesian Analysis and as an editor of Bayesian Analysis, alongside roles as an associate editor for several other leading statistical journals. Her research is continually funded by the National Science Foundation, the National Institutes of Health, and industry partners. This support sustains a complete innovation pipeline, from theory to clinical translation, and advances the scientific application of artificial intelligence in health care.
Description of "From Data to Optimal Decisions: Bayesian Learning and AI for Personalized Medicine":
Modern health care and clinical research generate vast amounts of complex, fragmented data, but translating this raw information into reliable clinical insights remains challenging. This talk explores the synergy between statistics, artificial intelligence, and biomedicine to bridge the gap between messy real-world health data and the precise, individualized decisions that optimal patient care demands. Xu will discuss how Bayesian statistics and reinforcement learning can be leveraged to optimize complex treatment regimes, such as identifying the optimal drug combinations for improving the quality of life for people living with HIV. She will also introduce AI-powered tools that automate the synthesis of high-fidelity clinical data to support advanced survival modeling and subgroup inference. Together, these advances illustrate how mathematical innovation, paired with deep clinical insight, is reshaping our understanding of disease and our capacity to personalize treatment for every patient.
