Biography

Xiao Wu is an Assistant Professor of Biostatistics at Columbia University, and a member of Columbia Data Science Institute. His research focuses on developing statistical and causal inference methods to address methodological needs in health research. The key milestone of his research is to provide scientific evidence and policy solutions to mitigate the adverse impacts of environmental factors under rapidly evolving natural and societal conditions. Contact here if you want to harness the power of data science to build a healthier, more sustainable world!

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 was a Data Science Postdoctoral Fellow at Stanford University, where he worked with Dr. Trevor Hastie in Statistics during 2021-2022. 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, New England Journal of Medicine, the Lancet Planetary Health, and the Journal of the American Statistical Association, and it has attracted the attention of international journalism, including at the New York Times, the Guardian, National Geographic, USA Today, and Scientific American.