Liangyuan Hu

Dr. Liangyuan Hu, Ph.D., serves as an associate professor in the Department of Biostatistics and Epidemiology at the Rutgers School of Public Health. With a keen focus on statistical methods for causal inference and missing data, her work extends to Bayesian inference, particularly in the context of significant health challenges such as cancer, HIV/AIDS, and cardiovascular diseases. Her innovative approach to these complex issues has garnered attention and respect within the academic community. Dr. Hu's research has been particularly influential in understanding the causal effects of treatment initiation timing on mortality rates among patients with HIV and tuberculosis. Her work in this area has not only advanced theoretical knowledge but also has practical implications for patient care and treatment strategies. Her contributions have been recognized through various accolades, underscoring the impact of her research on public health outcomes. Leading a team funded by the Patient-Centered Outcomes Research Institute (PCORI) and the National Institutes of Health (NIH), Dr. Hu is at the forefront of developing statistical methods for comparative effectiveness research. Her team employs Bayesian machine learning techniques to derive precise causal inferences from intricate health datasets, pushing the boundaries of what is possible in health data analysis. In addition to her research, Dr. Hu is committed to the dissemination of knowledge and tools that can aid fellow researchers. She has developed several open-source software packages that facilitate the implementation of her statistical methods, making advanced analytical techniques more accessible to the broader research community. Her dedication to open science and collaboration is evident in her efforts to empower other researchers with the tools necessary for cutting-edge analysis. Dr. Hu's work is characterized by a blend of theoretical rigor and practical application, reflecting her commitment to improving health outcomes through advanced statistical methodologies. Her contributions continue to shape the field of biostatistics, offering new insights and tools for tackling some of the most pressing health issues of our time.