Dan Kowal

Dan Kowal is an associate professor of statistics and data science. His research primarily revolves around three themes: (1) Bayesian models and algorithms for large and dependent (e.g., time series, spatial, functional) data, (2) modeling, generation, and imputation of mixed data, and (3) predictive inference for actionable and interpretable uncertainty quantification. He directs his research toward open questions in public health, epidemiology, physical activity data, economics, and finance. Recently, he has worked on addressing urgent issues related to racial inequities and biases in statistical modeling.

Kowal’s research has been published widely in journals including Proceedings of the National Academy of Sciences, Journal of the American Statistical Association, Bayesian Analysis, Annals of Applied Statistics, and Journal of Machine Learning Research, among others.

His awards and honors include the inaugural Blackwell-Rosenbluth Award (2021), a Young Investigator Award from the Army Research Office (2020), and honorable mentions for the Lindley Prize (2024) and the Arnold Zellner Thesis Award (2018). He received his Ph.D. in 2017 from Cornell University.