2017: Ph.D. in Statistics, Cornell University
2015: M.S. in Statistics, Cornell University
2012: B.A. in Mathematics, summa cum laude, Washington University in St. Louis
Economics, finance, neuroscience, biomedical engineering, and astronomy.
Dr. Kowal's research focuses on statistical methods for massive data sets with complex dependence structures, including functional, time series, and spatial data. For many applications, these dependence structures appear concurrently. He develops and studies hierarchical Bayesian models, which provide both sufficient model flexibility to tackle complex problems as well as mechanisms for regularization to prevent overfitting.
Recently, Dr. Kowal has been selected for an ARO Young Investigator Award for his work on Optimal Bayesian Approximations for Targeted Prediction.