Dr. Kowal develops statistical methodology and algorithms for massive data sets with complex dependence structures, such as functional, time series, and spatial data. His recent work focuses on Bayesian models for prediction and inference, decision theory, discrete data analysis, and scalable approximations to complex models.
In Spring 2020, Dr. Kowal was named Dobelman Chair Assistant Professor. In addition, Dr. Kowal was selected for an ARO Young Investigator Award for his work on Optimal Bayesian Approximations for Targeted Prediction.
Environmental health and policy, wearable devices, economics and finance, biomedical engineering, and astronomy
Ph.D. Statistics, Cornell University
M.S. Statistics, Cornell University
B.A. Mathematics, Washington University at St. Louis University
Probability and Statistics
Recent Patent Applications
"PCT/US2019/044051, (Rice) Tech ID: 2020-013; with Dr. Marina Vannucci and Shell. Title: Process for real time geological localization with Kalman filtering"
Societies & Organizations
American Statistical Association (ASA)
Institute of Mathematical Statistics (IMS)
International Society for Bayesian Analysis (ISBA)
The Ken Kennedy Institute (Rice)
Honors & Awards
2020 nominee, ARO Young Investigator Award
2020 Dobelman Chair Assistant Professor
2018 Arnold Zellner Thesis Award in Econometrics and Statistics: Honorable Mention
2018 Business and Economic Statistics Section Student Paper Award: Dynamic Shrinkage Processes
2017 Nonparametric Statistics Section Student Paper Award: Functional autoregression for sparsely sampled data
2016 ASA Section on Bayesian Statistical Science Student Paper Award: “A Bayesian multivariate functional dynamic linear model”
2016 Cellular and Molecular Bioengineering Editors' Choice Award: “Probing the biophysical properties of primary breast tumor-derived fibroblasts”
2014 Benjamin Miller Research Fellowship, Industrial and Labor Relations School, Cornell University