Xiao Wu is a Data Science Postdoctoral Fellow at Stanford Data Science, where he works with Dr. Trevor Hastie in the Department of Statistics. His research interests lie in developing statistical and causal inference methods to address methodological needs in climate and health research. The key goal of his research is to provide scientific evidence on the health impacts of environmental factors in an age of rapidly changing climate.
He completed his Ph.D. in Biostatistics at Harvard University, where he was advised by Dr. Francesca Dominici and Dr. Danielle Braun. His dissertation focuses on developing causal inference methods to handle error-prone, continuous, and time-series exposures. He is also working on collaborative projects to design Bayesian clinical trials, meta-analyses, and real-world evidence studies.
He has been named to Forbes 30 Under 30 list. His research has been published in prestigious scientific venues such as Science Advances, the Lancet Planetary Health, and the Annals of Applied Statistics, and it has attracted the attention of international journalism, including at the New York Times, the Guardian, National Geographic, USA Today, and Scientific American.
Ph.D. in Biostatistics, 2021
M.S. in Biostatistics, 2017
Harvard T.H. Chan School of Public Health
LL.B. in Laws, B.S. in Mathematics, 2015