Daniel Kowal is an assistant professor in the Department of Statistics at Rice University.

Daniel Kowal is an assistant professor in the Department of Statistics at Rice University.

I am interested in developing innovative statistical methodology for massive data sets with complex dependence structures, including functional, time series, and spatial data. For many applications, these dependence structures appear concurrently.

I prefer hierarchically Bayesian models, which provide both sufficient model flexibility to tackle complex problems as well as mechanisms for regularization to prevent overfitting. 

With my research, I seek to directly and meaningfully address open questions in important fields such as economics, neuroscience, biomedical engineering, finance, and astronomy.  

 


Recent News:

Install and load using the following code:

library(devtools)

devtools::install_github("drkowal/dsp")

library(dsp)

Uses include:

  1. Curve-fitting of irregular data via Bayesian trend filtering: btf(...) 
  2. An adaptive time-varying parameter regression model: btf_reg(...)
  3. Curve-fitting of irregular data with unequally-spaced observations: btf_bpsline(...)