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Dan Kowal is an Associate Professor in the Department of Statistics & Data Science at Cornell Bowers University. His research primarily revolves around important themes such as Bayesian models and algorithms for large dependent data, which encompasses time series, spatial, and functional data analysis. He focuses on modeling, generation, and imputation of mixed data streams, alongside predictive inference and actionable uncertainty quantification. Dan directs research on pressing open questions in public health, epidemiology, physical activity data, economics, and finance, particularly addressing urgent issues related to racial inequities and biases in statistical modeling. His work has been widely published in esteemed journals including the Proceedings of the National Academy of Sciences, the Journal of the American Statistical Association, Bayesian Analysis, and the Annals of Applied Statistics. His notable awards include the inaugural Blackwell-Rosenbluth Award received in 2021, and the Young Investigator Award from the Army Research Office in 2020, along with honorable mentions for the Lindley Prize in 2024 and the Arnold Zellner Thesis Award in 2018.
Department of Computer Science - PhD program focus on research leadership.