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MS Faculty - Katherine Shoemaker

Katherine Shoemaker

Assistant Professor

Office: S731

​​​After receiving my undergraduate degree in Applied Mathematics from Texas A&M, I worked as an analyst for a short period of time. This sparked my interest in Data Science and Statistics, and I received my doctorate in Statistics from Rice University, teaching classes while there and researching with my advisor at MD Anderson Cancer. I enjoyed teaching courses during graduate school, so I made the decision to shift my focus to education. Thus, after completing my degree, I began teaching at UHD. My primary goals as an educator are to use my courses to bring immediacy and intuition to the study of statistics and data science, and to demonstrate to students the impressive analytics they're capable of doing.

Texas A&M University, B.S., Applied Mathematics, 2012

Rice University, M.A., Statistics, 2018

Rice University, Ph.D., Statistics,  2019

​DATA 2401 – Introduction to Data Science
STAT 5301 / MATH 5310 – Statistical Foundations for Data Analytics
STAT 5310 – Applied Regression Analysis


My research explores the interpretable application of statistics in the field of radiomics, a rapidly growing area of medical research, specifically in that of cancer. Radiomics is the process of mining large amounts of data from images and using the collected information to help make clinical decisions or to classify lesions, such as discriminating between malignant tumors and benign lesions.  There are many different parts to this field, ranging from collecting the information to using it in statistical models. The feature sets collected are typically massive in size and scope, and my research both addresses the creation of new features to add to the radiomic feature space and building interpretable models for classification that take the reliability and robustness of the features in to account.


-  Cremaschi, A., Argiento, R., Shoemaker, K., Peterson, C.B. and Vannucci M. (2019). Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling. Bayesian Analysis, 14(4), 1271-1301.
- Shoemaker, K., Hobbs, B., Bharath, K., Ng, C., Baladandayuthapani, V. (2018). Tree-based Methods for Characterizing Tumor Density Heterogeneity.  Pacific Symposium on Biocomputing. 23:216-227​


2018 – “Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling," Honorable Mention, ISBA Lindley Prize


Last updated 5/11/2021 6:40 AM