Jay Xu

Jay Xu is a Data Sciences Institute Postdoctoral Fellow at the Institute of Health Policy, Management and Evaluation at the Dalla Lana School of Public Health at the University of Toronto under the supervision of Dr. Kuan Liu. He received his PhD and MS in Biostatistics from the University of California, Los Angeles (USA), where his advisor was Dr. Thomas Belin, and he received an AB-ScB in Statistics, Applied Mathematics, and Mathematics-Economics from Brown University (USA), where his advisor was Dr. Roee Gutman. During his PhD studies, he was awarded a National Defense Science and Engineering Graduate Fellowship from the U.S. Department of Defense. 

Dr. Xu's primary research interests center around the methodological development of causal inference methods to inform both clinical care and public health policy. His specific methodological areas of expertise include generalizability and transportability methods, causal inference methods for survival and recurrent event outcomes, joint modeling of longitudinal and time-to-event data, principal stratification, and methods for handling missing and incomplete data. His applied research interests span several critical areas of public health, including cognitive and mental health, COVID-19, and drug overdose epidemiology. He is also a leading expert on the analysis of U.S. National Vital Statistics System mortality data and U.S. Census Bureau-produced population estimates for population-level mortality disparities research. Dr. Xu's research has been featured in prominent media outlets such as the Los Angeles Times and the Washington Post. 

You can follow Dr. Xu on X (formerly known as Twitter) at @Dr_JayXu and on Instagram at @drjayxu.