Xiao Wu, PhD

  • Assistant Professor of Biostatistics
Profile Headshot


Xiao Wu is an Assistant Professor of Biostatistics at Columbia University. 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. Contact me if you are interested in using data science to build a healthier, environmentally 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 the Department of 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.

Academic Appointments

  • Assistant Professor of Biostatistics

Credentials & Experience

Education & Training

  • BS, 2015 Mathematics, Peking University
  • LLB, 2015 Laws, Peking University
  • MS, 2017 Biostatistics, Harvard T. H. Chan School of Public Health
  • PhD, 2021 Biostatistics, Harvard University

Honors & Awards

2022 Forbes Magazine 30 Under 30 - Healthcare


Research Interests

  • Biostatistical Methods
  • Environmental Health

Selected Publications

1. Wu, X., Weinberger, K.R., Wellenius, G.A., Dominici, F. and Braun, D., 2023. Assessing the causal effects of a stochastic intervention in time series data: Are heat alerts effective in preventing deaths and hospitalizations? Biostatistics, pp.1-23.

2. Josey, K.P., Delaney, S.W., Wu, X., Nethery, R.C., DeSouza, P., Braun, D. and Dominici, F., 2023. Air Pollution and Mortality at the Intersection of Race and Social Class. New England Journal of Medicine.

3. Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.

4. Wu, X., Braun, D., Schwartz, J., Kioumourtzoglou, M.A. and Dominici, F., 2020. Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly. Science Advances, 6(29), p.eaba5692.

5. Wu, X., Nethery, R.C., Sabath, M.B., Braun, D. and Dominici, F., 2020. Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Science Advances, 6(45), p.eabd4049.

6. Shi, L., Wu, X., Yazdi, M.D., Braun, D., Awad, Y.A., Wei, Y., Liu, P., Di, Q., Wang, Y., Schwartz, J. and Dominici, F., 2020. Long-term effects of PM2ยท 5 on neurological disorders in the American Medicare population: a longitudinal cohort study. The Lancet Planetary Health, 4(12), pp.e557-e565.

7. Wu, X., Braun, D., Kioumourtzoglou, M.A., Choirat, C., Di, Q. and Dominici, F., 2019. Causal inference in the context of an error prone exposure: air pollution and mortality. The Annals of Applied Statistics, 13(1), p.520.