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Dan Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University. His research primarily focuses on Bayesian statistical methods, particularly in the context of large dependent data, which includes multivariate, time series, functional, and spatial data. Kowal emphasizes the development of reliable and scalable algorithms for Bayesian inference with complicated dependencies, addressing issues related to nonstandard support for data types. His work also explores predictive inference and uncertainty quantification, striving for clear communication of statistical measures to both scientific and public audiences. This interdisciplinary research is motivated by pressing questions in public health, epidemiology, and environmental justice, as well as in economics and finance. Kowal has received notable accolades, including the Young Investigator Award from the Army Research Office and the inaugural Blackwell-Rosenbluth award from the International Society for Bayesian Analysis for junior researchers in Bayesian statistics. He is dedicated to innovating Bayesian methodologies and has published extensively on various aspects of Bayesian analysis and its applications.
Cornell University • Ithaca, NY
Teaching and conducting research in Bayesian statistics and its applications.
Department of Architecture