Karl Pazdernik

Dixon, S., Keshavamurthy, R., Farber, D. H., Stevens, A., Pazdernik, K. T., & Charles, L. E. (2022). A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time. PATHOGENS, 11(2). https://doi.org/10.3390/pathogens11020185

Bakerman, J., Pazdernik, K., Korkmaz, G., & Wilson, A. G. (2022). Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest. INTERNATIONAL JOURNAL OF FORECASTING, 38(2), 648–661. https://doi.org/10.1016/j.ijforecast.2021.07.003

Keshavamurthy, R., Dixon, S., Pazdernik, K. T., & Charles, L. E. (2022). Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches. ONE HEALTH, 15. https://doi.org/10.1016/j.onehlt.2022.100439

Pazdernik, K., & Maitra, R. (2021, July 31). Estimating basis functions in massive fields under the spatial mixed effects model. STATISTICAL ANALYSIS AND DATA MINING. https://doi.org/10.1002/sam.11537

Pazdernik, K., LaHaye, N. L., Artman, C. M., & Zhu, Y. (2020). Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods. COMPUTATIONAL MATERIALS SCIENCE, 181. https://doi.org/10.1016/j.commatsci.2020.109728

Bakerman, J., Pazdernik, K., Wilson, A., Fairchild, G., & Bahran, R. (2018). Twitter geolocation: A hybrid approach. ACM Transactions on Knowledge Discovery from Data, 12(3).