Muxuan Liang
Assistant Professor
The University of Texas MD Anderson Cancer Center at Houston
Department of Biostatistics
Research Description
My research program focuses on advancing statistical and machine learning (ML) methods to enable personalized decision-making in healthcare. I develop innovative approaches in three interconnected areas: individualized treatment strategies, responsible and explainable AI, and transfer learning. A central theme of my work is utilizing data from electronic health records, clinical trials, and patient-reported outcomes to develop models that support precision medicine and enhance clinical decision-making.
Students in my group may work on projects such as developing interpretable individualized treatment rules that account for biases in observational data, creating robust prediction models under outcome misclassification, or designing algorithms that leverage large external datasets to improve predictions in smaller patient populations. Through these projects, students will gain experience in modern statistical theory, causal inference, optimization, and practical applications of ML/AI to biomedical problems. They will also learn how to translate methodological innovations into tools that clinicians and researchers can use in practice.
Overall, my lab provides a collaborative environment where students can build strong quantitative skills while contributing to impactful interdisciplinary research at the interface of biostatistics, AI, and biomedical science. This training prepares students to become independent researchers equipped to tackle the challenges of data-driven healthcare innovation.
Education & Training
PhD, University of Wisconsin, 2018