George Papadogeorgou

George Papadogeorgou is an Assistant Professor in the Department of Statistics at the University of Florida, where he joined in August 2020. His academic journey began with a B.Sc. in Mathematics from the National and Kapodistrian University of Athens in 2013. He then pursued advanced studies at Harvard University, earning an M.A. in Biostatistics in 2015 and a Ph.D. in Biostatistics in 2018. His dissertation, titled "Causal Inference Methods in Air Pollution Research," was supervised by Francesca Dominici and Corwin M. Zigler. Before his current role, Papadogeorgou held a Postdoctoral Associate position in the Department of Statistical Science at Duke University from July 2018 to August 2020, mentored by David Dunson and Fan Li. Earlier, he interned as a Decision Support Intern on Google's Geo Data Analytics Team during the summer of 2015.

Papadogeorgou's research centers on causal inference in complex settings, including interference, unmeasured spatial confounding, bipartite and network structures, and spatio-temporal data. He develops semiparametric and Bayesian methodologies, often incorporating machine learning for efficiency, with applications to environmental health, political science, ecology, and policy evaluation. His work has led to influential publications such as "Low levels of air pollution and health: effect estimates, methodological challenges, and future directions" (Current Environmental Health Reports, 2019, 130 citations), "Causal inference with interfering units for cluster and population level treatment allocation programs" (Biometrics, 2019, 103 citations), "Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching" (Biostatistics, 2019, 99 citations), "Bipartite causal inference with interference" (Statistical Science, 2021, 94 citations), "Soft Tensor Regression" (Journal of Machine Learning Research, 2021), "Causal inference with spatio-temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq" (Journal of the Royal Statistical Society Series B, 2022), and "Covariate-informed latent interaction models: Addressing geographic & taxonomic bias in predicting bird-plant interactions" (Journal of the American Statistical Association, 2023). He has authored R packages including geocausal for spatio-temporal causal inference, DAPSm for spatial propensity score matching, and others to support his methodologies. With nearly 1,000 citations on Google Scholar, his contributions have advanced design-based causal inference under interference and spatial dependencies. Papadogeorgou has received prestigious awards, including the 2023 Blackwell-Rosenbluth Award from j-ISBA, the 2016 Student Paper Award from the Joint Statistical Meetings Health Policy Statistics Section, the 2016 Rose Traveling Fellowship, multiple Certificates of Distinction in Teaching from Harvard, and honorary titles for top GPA rankings during his undergraduate studies. He frequently presents at major conferences such as ENAR, JSM, ISBA World Meetings, and European Causal Inference Meeting.

Professional Email: gpapadogeorgou@ufl.edu