I believe that many problems could be solved with increased statistical literacy, and focus on developing statistical communication skills with my students. My research focuses on spatial models for areal (regional) count data that prioritize valid interpretations and accesible implementations.

Geometrically Aware Weight Matrices

Spatial regression models often encode spatial dependence using a contiguity or nearest neighbor structure, which greatly reduce the spatial resolution of the covariance matrix. Click above to learn about my proposed improvement to these weighting schemes using the Hausdorff Distance.

GLMs for Spatially Aggregated Data

Much of the publicly available spatial data are aggregated over the county, city, state, etc, and are less rich compared to the point-referenced (e.g. with a latitude/longitude) datasets which often contain private information. Though recent (and excellent!) work has been done to bring aggregated data into the point-process modeling framework, I believe there is still a place for spatial GLMs in the spatial statistics literature. Click here to learn more about these models and how I’ve used them.


If a statistician publishes a methodology paper and no one implements it, does it make a difference? No. Click here to learn about the software companions to my methodological work.

Student Comments

“I liked how the instructor diversified the material, covering extraneous topics like Bertrand’s Paradox and Bayesian statistics. I appreciated how she chose to do an oral exam and an essay instead of a typical written exam. It allowed me to develop my ability to communicate about the material which is just as important as knowledge of the material.”

Stat 310: Probability and Statistics

taught @ Rice University, 2017

“Professor* Schedler was outstanding. She made exercises interactive and emphasized the importance of communicating our thoughts and ideas. She went through difficult topics with visuals and worked hard to make it easy to understand. She was also very generous with her time and was available for questions and homework help.”
*my title was instructor

Stat 310: Probability and Statistics

taught @ Rice University, 2017

“Ms. Schedler was a really great teacher. She fostered a classroom environment that made students comfortable with asking questions and provided the opportunity for students to contest ideas they didn’t agree with in class. She was very approachable outside of class and by email. She made us worry more about learning the concepts and less about our grades, which was a nice change from other classes I have taken.

Stat 310: Probability and Statistics

taught @ Rice University, 2017

Latest News

Here’s where you’ll find some posts on my musings about things in statistics, data science, and education.