Twitter geolocation: A hybrid approach J Bakerman, K Pazdernik, A Wilson, G Fairchild, R Bahran ACM Transactions on Knowledge Discovery from Data (TKDD) 12 (3), 1-17, 2018 | 54 | 2018 |
Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches R Keshavamurthy, S Dixon, KT Pazdernik, LE Charles One Health 15, 100439, 2022 | 25 | 2022 |
Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods K Pazdernik, NL LaHaye, CM Artman, Y Zhu Computational Materials Science 181, 109728, 2020 | 17 | 2020 |
A comparison of infectious disease forecasting methods across locations, diseases, and time S Dixon, R Keshavamurthy, DH Farber, A Stevens, KT Pazdernik, ... Pathogens 11 (2), 185, 2022 | 7 | 2022 |
NukeLM: Pre-trained and fine-tuned language models for the nuclear and energy domains L Burke, K Pazdernik, D Fortin, B Wilson, R Goychayev, J Mattingly arXiv preprint arXiv:2105.12192, 2021 | 7 | 2021 |
Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest J Bakerman, K Pazdernik, G Korkmaz, AG Wilson International Journal of Forecasting 38 (2), 648-661, 2022 | 6 | 2022 |
Evaluation of Sampling and Testing Efficiencies of Plum pox virus Eradication Programs in Pennsylvania and Ontario AV Gougherty, KT Pazdernik, MS Kaiser, FW Nutter Jr Plant Disease 99 (9), 1247-1253, 2015 | 5 | 2015 |
Modeling the spread of plant disease using a sequence of binary random fields with absorbing states MS Kaiser, KT Pazdernik, AB Lock, FW Nutter Spatial Statistics 9, 38-50, 2014 | 5 | 2014 |
Murky waters: Farm pollution stalls cleanup of Iowa streams C Cox, A Hug Washington, DC, Environmental Working Group 52, 2012 | 5 | 2012 |
DBCal: Density based calibration of classifier predictions for uncertainty quantification A Hagen, K Pazdernik, N LaHaye, M Oostrom arXiv preprint arXiv:2204.00150, 2022 | 2 | 2022 |
Accelerated Computation of a High Dimensional Kolmogorov-Smirnov Distance A Hagen, S Jackson, J Kahn, J Strube, I Haide, K Pazdernik, C Hainje arXiv preprint arXiv:2106.13706, 2021 | 2 | 2021 |
Reduced basis kriging for big spatial fields K Pazdernik, R Maitra, D Nychka, S Sain Sankhya A 80, 280-300, 2018 | 2 | 2018 |
Estimating basis functions in massive fields under the spatial mixed effects model K Pazdernik, R Maitra Statistical Analysis and Data Mining: The ASA Data Science Journal 14 (5 …, 2021 | 1* | 2021 |
A partial-volume correction for quantitative spectral X-ray radiography WC Gillis, AJ Gilbert, K Pazdernik, A Erickson IEEE Transactions on Nuclear Science 67 (11), 2321-2328, 2020 | 1 | 2020 |
Determining Individual Origin Similarity (DInOS): Binary Classification of Authors Using Stylometric Features A Kingsland, D Fortin, E Cary, S Smith, K Pazdernik, R Perko arXiv preprint arXiv:1912.03750, 2019 | 1 | 2019 |
On the Behavior of Audio-Visual Fusion Architectures in Identity Verification Tasks D Claborne, E Slyman, K Pazdernik arXiv preprint arXiv:2311.05071, 2023 | | 2023 |
Testing and Evaluation of Data Analytic Approaches for Nonproliferation D Anderson, S Stewart, A Skurikhin, K Pazdernik, J Brogan, N Martindale Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2023 | | 2023 |
Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment S Chatterjee, D Thomas, D Fortin, K Pazdernik, B Wilson, L Newburn ESARDA Bulletin 65 (PNNL-SA-166748), 2023 | | 2023 |
Correlated Belief Matching for Uncertainty Quantification in Text Classification A Hollis, K Pazdernik, A Wilson, R Smith | | 2023 |
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy L Burke, K Pazdernik, D Fortin, B Wilson, R Goychayev, J Mattingly ESARDA Bulletin 63, 30-40, 2021 | | 2021 |