معرفی
Xiao Wu is an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University. He is also a verified member of the Data Science Institute (DSI) and affiliated with the Computational Social Science Committee, Foundations of Data Science, and Health Analytics initiatives. His work bridges statistical methodology and public health applications, particularly in climate and environmental health.
Education:
- Ph.D. in Biostatistics, Harvard University (advised by Dr. Francesca Dominici and Dr. Danielle Braun)
- Postdoctoral Fellow in Data Science, Stanford University (Department of Statistics, with Dr. Trevor Hastie, 2021–2022)
Xiao Wu's research focuses on developing advanced statistical and causal inference methods to assess the health impacts of environmental factors amid climate change. His work addresses methodological challenges in handling error-prone and time-series exposures, with applications in air pollution, extreme weather, and global health. He integrates data science techniques with public health questions, emphasizing real-world evidence, Bayesian modeling, and meta-analysis. His methodological innovations support rigorous evaluation of environmental policies and health interventions.
His recent publications span high-impact journals such as Science Advances, The Lancet Planetary Health, and Annals of Applied Statistics. Collectively, his articles emphasize causal frameworks, scalable algorithms, and integrative modeling for environmental health. Key trends include the use of satellite data, hierarchical Bayesian models, and real-world data to estimate health risks under climate variability. Topics frequently involve exposure-response relationships, measurement error correction, and health disparity assessment.
Scientific Awards:
- Forbes 30 Under 30
Xiao Wu has advised doctoral students and postdoctoral researchers in biostatistics and data science. His collaborative projects include designing Bayesian clinical trials and conducting meta-analyses using real-world evidence. While specific grants are not listed, his affiliations and research scope suggest active involvement in federally and foundation-funded studies at the intersection of climate, health, and data science.
He is actively involved in interdisciplinary research teams, including the Data Science Institute and Health Analytics Center at Columbia. His work leverages large-scale datasets and computational methods to address pressing public health challenges related to climate change. Through these collaborations, he contributes to building robust statistical frameworks for policy-relevant environmental health research.
