New advances in data collection and analysis has led to increased availability of big data, large datasets with interconnected variables and unknown relationships. Parsing this information in interpretable ways for use in decision making and in clinical settings is an intriguing challenge.
To this end, 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.
Yang, F., Barros-Lane, L., Rountree, J., Shoemaker, K., Sirrianni, L., Miller, D., (2024). What Motivates Social Work Students to Vote? A Mixed-Method Study. Submitted, Under Review.
Hassan, J., Berdego, J., Kurati, S., Dinh, A., Garcia, A., Shoemaker, K., Shastri, D., (2024). Bioindicators of Attention Detection in Online Learning Environments. HCI International 2024 Posters. HCII 2024. Communications in Computer and Information Science, vol 2117. Springer, Cham.
Shoemaker, K., Ger, R., Court, L. E., Aerts, H., Vannucci, M., Peterson, C. B., (2023). Bayesian feature selection for radiomics using reliability metrics. Frontiers of Genetics, 14:1112914.
Parker, M. J, Feng, W., Jegdic, K., Jiang, M., Qavi, H., Shoemaker, K., Xu, L., (2020).Summer Bridge/Undergraduate Research Program – Going Remote with COVID-19, International Journal of Applied Science and Technology. Vol. 10, No. 4.
Argiento, R., Cremaschi, A., Shoemaker, K., Peterson, C., and Vannucci, M. (2019). Hierarchical normalized completely random measures for robust graphical modeling. Bayesian Analysis. 14(4), 1271-1301.
Shoemaker, K., Hobbs, B. P., Bharath, K., Ng, C. S., & Baladandayuthapani, V. (2018). Tree-based Methods for Characterizing Tumor Density Heterogeneity. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 23, 216-227.
Harnessing the Power of Gradescope for Dynamic Rubrics, ePoster Presentation, UHD Teaching and Learning Symposium, Houston, TX.
Interpretation of Machine Learning Research. Invited talk, Session "Machine Learning Challenges from Benchtop to Clinical Implementation", American Association of Physicists in Medicine 65th Annual Meeting, Houston, TX.
Data Science in Action: Real-World Final Projects in an Introductory Course. Poster presentation, US Conference On Teaching Statistics, Penn State University, State College, PA