The Ken Kennedy Institute's September Member of the Month, Dr. Daniel Kowal, is the Dobelman Family Assistant Professor in the Department of Statistics.
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.
What is your favorite book?
Catch-22. It’s that rare combination of hilarious and reflective moments so perfectly timed one after the other. For 2021, I was surprised how much I enjoyed Lonesome Dove--and reading it during the winter storm really brought the Texas adventure to life!
How do you explain your research in one sentence?
My goal is to model and understand the uncertainties that arise in data-driven decision making, especially when there is structure or dependence in the data.
What is your favorite aspect of your research?
I particularly enjoy the evolution and translation of research: one good idea can transform into a great idea for a different problem. I try to maintain that spirit of optimism and open-mindedness when a current project feels uninspiring.
What challenges do you see in your research that you didn't expect?
Disclaimer: I am very interested in collaborations! That said, I’ve been surprised how often potential collaborations fizzle out. There is a real skill in identifying and maintaining productive collaborations, so I’m working to improve those skills within the Rice community and beyond.
What is a favorite experience with the Ken Kennedy Institute?
The virtual networking event with Texas Medical Center researchers was an uplifting reminder of the exciting and impactful research that goes on right across the street.