Papers
2026
K. Goode, J. D. Tucker, D. Ries, and H. Hofmann, “Explainable Machine Learning for Functional Data,” Journal of Data Science, vol. 24, no. 1, pp. 125-145, 2026, doi: 10.6339/25-JDS1212. PDF · CRAN · GitHub
J. D. Tucker, G. Collins, G. Huerta, and J. L. Brown, “Elastic Multi-Fidelity Bayesian Model Calibration,” SIAM/ASA Journal on Uncertainty Quantification, submitted, 2026. arXiv
G. Collins, G. Huerta, J. D. Tucker, and K. Rumsey, “A Review of Bayesian Surrogate Modeling for High-Dimensional Computer Simulations,” WIREs Computational Statistics, submitted, 2026. arXiv
N. Winovich, L. Moynihan, O. Abdelrahman, R. West, S. Dauphin, J. D. Tucker, G. Huerta, K. Potter, R. Forrest, C. Phillips, and M. Ebeida, “VoroClust: Scalable Clustering for Remote Sensing,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 19, pp. 15071-15089, 2026, doi: 10.1109/JSTARS.2026.3686609. PDF
K. Rumsey, D. Francom, G. Gibson, J. D. Tucker, and G. Huerta, “Bayesian adaptive polynomial chaos expansions,” Stat, vol. 15, no. 1, e70151, 2026, doi: 10.1002/sta4.70151. PDF · R
2025
J. Adams, B. Berman, J. Michalenko, and J. D. Tucker, “Conformal Anomaly Detection for Functional Data with Elastic Distance Metrics,” Proc. Symposium on Conformal and Probabilistic Prediction with Applications (COPA), Proceedings of Machine Learning Research, vol. 266, 2025. arXiv
J. E. Borgert, J. Hannig, J. D. Tucker, L. Arbeeva, A. N. Buck, Y. M. Golightly, S. P. Messier, A. E. Nelson, and J. S. Marron, “Elastic Shape Analysis of Movement Data,” JASA Case Studies and Applications, 2025, doi: 10.1080/01621459.2025.2572778. PDF
A. Heiskala, J. D. Tucker, P. Choudhary, R. Nedelec, J. Ronkainen, O. Sarala, M. Järvelin, M. J. Sillanpää, and S. Sebert, “Timing based clustering of childhood BMI trajectories reveals differential maturational patterns; Study in the Northern Finland Birth Cohorts 1966 and 1986,” International Journal of Obesity, vol. 49, no. 5, pp. 872-880, 2025, doi: 10.1038/s41366-025-01714-8. PDF
D. Francom, J. D. Tucker, J. G. Huerta, K. Shuler, and D. Ries, “Elastic Bayesian Model Calibration,” SIAM/ASA Journal on Uncertainty Quantification, vol. 13, no. 1, pp. 195-227, 2025, doi: 10.1137/24M1644092. PDF · Code
2024
J. D. Tucker and D. Yarger, “Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources,” Environmetrics, vol. 35, no. 1, e2826, 2024, doi: 10.1002/env.2826. PDF 2024 Wiley-TIES Best Environmetrics Paper Award
A. McCombs, M. A. Ausdemore, K. Goode, J. G. Huerta, K. Shuler, J. D. Tucker, A. Zhang, and D. Ries, “Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data,” Frontiers in Nuclear Engineering, vol. 3, 2024, doi: 10.3389/fnuen.2024.1331349. PDF
2023
D. Yarger and J. D. Tucker, “Detecting changepoints in globally-indexed functional time series,” arXiv preprint arXiv:2308.05915 [stat.AP], 2023. arXiv
R. Darling, G. S. Clark, and J. D. Tucker, “Hidden Ancestor Graphs: Models for Detagging Property Graphs,” arXiv preprint arXiv:2102.09581 [math.PR], 2023. arXiv
D. Ries, A. Zhang, J. D. Tucker, K. Shuler, and M. Ausdemore, “A framework for inverse prediction using functional response data,” Journal of Computing and Information Science in Engineering, vol. 23, no. 1, 011002, 2023, doi: 10.1115/1.4053752. PDF
J. L. Brown, J. P. Davis, J. D. Tucker, J. G. Huerta, and K. Shuler, “Quantifying uncertainty in analysis of shockless dynamic compression experiments on platinum. II. Bayesian model calibration,” Journal of Applied Physics, vol. 134, no. 23, 235902, 2023. PDF · Code
C. A. Pattyn, A. W. Edstrom, A. L. Sanchez, K. Westlake, J. D. van der Laan, J. D. Tucker, J. Jones, K. Hagopian, J. C. Shank, L. K. Casias, and J. B. Wright, “Event-based sensing for the detection of modulated signals in degraded visual environments,” Proc. SPIE, vol. 12514, Image Sensing Technologies: Materials, Devices, Systems, and Applications X, paper 1251409, 2023. PDF
K. Rumsey, J. G. Huerta, and J. D. Tucker, “A Localized Ensemble of Approximate Gaussian Processes for Fast Sequential Emulation,” Stat, vol. 12, no. 1, e576, 2023, doi: 10.1002/sta4.576. PDF
J. D. Tucker, M. T. Martinez, and J. M. Laborde, “Dimensionality Reduction using Elastic Measures,” Stat, vol. 12, no. 1, e551, 2023, doi: 10.1002/sta4.551. PDF
J. Upston, D. Sulsky, J. D. Tucker, and Y. Guan, “CIEL*Ch color map for visualization and analysis of sea ice motion,” Journal of Computational and Applied Mathematics, vol. 429, 115126, 2023, doi: 10.1016/j.cam.2023.115126. PDF
2022
T. Harris, B. Li, and J. D. Tucker, “Scalable Multiple Changepoint Detection for Functional Data Sequences,” Environmetrics, vol. 33, no. 2, e2710, 2022, doi: 10.1002/env.2710. PDF
M. A. Ausdemore, A. McCombs, D. Ries, A. Zhang, K. Shuler, K. Goode, J. D. Tucker, and J. G. Huerta, “A Probabilistic Inverse Prediction Method for Predicting Plutonium Processing Conditions,” Frontiers in Nuclear Engineering, vol. 1, 2022, doi: 10.3389/fnuen.2022.1083164. PDF
2021
L. Patel, L. Shand, J. D. Tucker, and G. Huerta, “Spatio-temporal extreme event modeling of terror insurgencies,” arXiv preprint arXiv:2110.08363 [stat.AP], 2021. arXiv
C. Ting, N. Johnson, U. Onunkwo, and J. D. Tucker, “Faster classification using compression analytics,” Proc. IEEE ICDM Workshop on Data Mining and Machine Learning for Cybersecurity, October 2021. PDF
J. D. Tucker, L. Shand, and K. Chowdhary, “Multimodal Bayesian Registration of Noisy Functions using Hamiltonian Monte Carlo,” Computational Statistics and Data Analysis, vol. 163, 107298, 2021, doi: 10.1016/j.csda.2021.107298. PDF
C. King, N. Martin, and J. D. Tucker, “Bounding Uncertainty in Functional Data: A Case Study,” Quality Engineering, vol. 33, no. 1, pp. 178-188, 2021. PDF
T. Harris, B. Li, N. J. Steiger, J. E. Smerdon, N. Narisetty, and J. D. Tucker, “Evaluating proxy influence in assimilated paleoclimate reconstructions—Testing the exchangeability of two ensembles of spatial processes,” Journal of the American Statistical Association, vol. 116, no. 535, pp. 1100-1113, 2021, doi: 10.1080/01621459.2020.1799810. PDF · R
T. Harris, J. D. Tucker, B. Li, and L. Shand, “Elastic depths for detecting shape anomalies in functional data,” Technometrics, vol. 63, no. 4, pp. 466-476, 2021, doi: 10.1080/00401706.2020.1811156. PDF · R
2020
J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, “A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data,” Journal of Applied Statistics, vol. 47, no. 3, pp. 481-505, 2020. PDF
X. Zhang, S. Kurtek, O. Chkrebtii, and J. D. Tucker, “Elastic k-means clustering of functional data for posterior exploration, with an application to inference on acute respiratory infection dynamics,” arXiv preprint arXiv:2011.12397 [stat.ME], 2020. arXiv
A. H. Foss, R. B. Lehoucq, W. Z. Stuart, J. D. Tucker, and J. W. Berry, “A Deterministic Hitting-Time Moment Approach to Seed-set Expansion over a Graph,” arXiv preprint arXiv:2011.09544 [cs.SI], 2020. arXiv
L. Patel, L. Shand, J. D. Tucker, and G. Huerta, “Assessing Extreme Value Analysis to predict rare events from the Global Terrorism Database,” Proc. JSM, August 2020. PDF
M. Ahn, J. D. Tucker, W. Wu, and A. Srivastava, “Regression Models Using Shapes of Functions as Predictors,” Computational Statistics and Data Analysis, vol. 151, 107017, 2020, doi: 10.1016/j.csda.2020.107017. PDF
N. Martin and J. D. Tucker, “Estimation of physical tolerance bounds for functional data,” Proc. SIAM Conference on Uncertainty Quantification, 2020.
S. Reza, N. Martin, T. Buchheit, and J. D. Tucker, “Tolerance Bound Calculation for Compact Model Calibration Using Functional Data Analysis,” Proc. 4th IEEE Electron Devices Technology and Manufacturing (EDTM) Conference, 2020.
2019
Y. Guan, C. Sampson, J. D. Tucker, W. Chang, A. Mondal, M. Haran, and D. Sulsky, “Computer model calibration based on image warping metrics: an application for sea ice deformation,” Journal of Agricultural, Biological and Environmental Statistics, vol. 24, no. 3, pp. 444-463, September 2019. PDF
J. D. Tucker, L. Shand, and J. R. Lewis, “Handling Missing Data in Self-Exciting Point Process Models,” Spatial Statistics, vol. 29, pp. 160-176, 2019. PDF
J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic Functional Principal Component Regression,” Statistical Analysis and Data Mining, vol. 12, no. 2, pp. 101-115, 2019. PDF
2018
T. Harris, B. Li, N. Steiger, J. Smerdon, and J. D. Tucker, “Evaluating Proxy Influence in Data Assimilation,” Proc. Climate Informatics, September 2018.
T. Harris, B. Li, N. Steiger, J. Smerdon, and J. D. Tucker, “Evaluating proxy influence in data assimilation based climate field reconstruction,” Proc. JSM, August 2018.
M. K. Ahn, J. D. Tucker, W. Wu, and A. Srivastava, “Elastic handling of predictor phase in functional regression models,” Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, International Workshop on DIFF-CVML: Differential Geometry in Computer Vision and Machine Learning, June 2018. PDF
2017
J. Ramirez, M. Orini, J. D. Tucker, P. Laguna, and E. Pueyo, “Variability of Ventricular Repolarization Dispersion Quantified by Time-Warping the Morphology of the T-waves,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1619-1630, 2017. PDF
2016
D. Bryner, F. Huffer, M. M. Rosenthal, J. D. Tucker, and A. Srivastava, “Estimation of Linear Target-Layer Trajectories Using Cluttered Point Cloud Data,” Computational Statistics and Data Analysis, vol. 102, pp. 1-22, October 2016. PDF
J. Ramirez, M. Orini, J. D. Tucker, E. Pueyo, and P. Laguna, “An Index for T‐wave Pointwise Amplitude Variability Quantification,” Proc. Computing in Cardiology (CinC), September 2016. PDF Mortara Winner
J. D. Tucker, “Functional Statistical Process Control using Elastic Methods,” Proc. JSM, August 2016. PDF
D. Bryner, F. Huffer, A. Srivastava, and J. D. Tucker, “Underwater Minefield Detection in Clutter Data Using Spatial Point-Process Models,” IEEE Journal of Oceanic Engineering, vol. 41, no. 3, pp. 670-681, 2016. PDF
T. G-Michael, B. Marchand, J. D. Tucker, D. D. Sternlicht, T. M. Marston, and M. R. Azimi-Sadjadi, “Image-based Automated Change Detection for Synthetic Aperture Sonar,” IEEE Journal of Oceanic Engineering, vol. 41, no. 3, pp. 592-612, 2016. PDF
T. G-Michael, J. D. Tucker, and R. R. Roberts, “Statistically Normalized Coherent Change Detection for Synthetic Aperture Sonar Imagery,” Proc. SPIE, April 2016. PDF
2014
J. D. Tucker, W. Wu, and A. Srivastava, “Analysis of Proteomics Data: Phase Amplitude Separation Using an Extended Fisher-Rao Metric,” Electronic Journal of Statistics, vol. 8, no. 2, pp. 1724-1733, 2014. PDF · Code
T. G-Michael, B. Marchand, J. D. Tucker, D. D. Sternlicht, and T. M. Marston, “Automated Change Detection for Synthetic Aperture Sonar,” Proc. SPIE, vol. 9072, pp. O1-O10, May 2014. PDF
J. D. Tucker, W. Wu, and A. Srivastava, “Analysis of signals under compositional noise with applications to SONAR data,” IEEE Journal of Oceanic Engineering, vol. 39, no. 2, pp. 318-330, April 2014. PDF · Code
2013
J. D. Tucker, W. Wu, and A. Srivastava, “Generative Models for Functional Data using Phase and Amplitude Separation,” Computational Statistics and Data Analysis, vol. 61, pp. 50-66, May 2013. PDF · R · MATLAB · Python · Julia
2012
J. D. Tucker, W. Wu, and A. Srivastava, “Analysis of Signals Under Compositional Noise with Application to SONAR Data,” Proc. MTS/IEEE Oceans 2012, pp. 1-6, October 2012. PDF
2011
J. D. Tucker and N. Klausner, “Compressive Sensing for Gauss-Gauss Detection,” Proc. IEEE SMC 2011, pp. 3335-3340, October 2011. PDF
J. C. Isaacs and J. D. Tucker, “Diffusion features for target specific recognition with synthetic aperture sonar raw signals and acoustic color,” Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 27-32, June 2011. PDF
J. D. Tucker and A. Srivastava, “Statistical analysis and classification of acoustic color functions,” Proc. SPIE, vol. 8017, pp. O1-O10, April 2011. PDF
J. D. Tucker and M. R. Azimi-Sadjadi, “Coherence-based underwater target detection from multiple sonar platforms,” IEEE Journal of Oceanic Engineering, vol. 36, no. 1, pp. 37-51, January 2011. PDF
J. C. Isaacs and J. D. Tucker, “Signal diffusion features for automatic target recognition in synthetic aperture sonar,” Proc. IEEE DSP/SPE 2011, pp. 461-465, January 2011. PDF
J. D. Tucker, J. T. Cobb, and M. R. Azimi-Sadjadi, “Generalized likelihood ratio test for finite mixture model of K-distributed random variables,” Proc. IEEE DSP/SPE 2011, pp. 443-448, January 2011. PDF
2010
T. G-Michael and J. D. Tucker, “Canonical correlation analysis for coherent change detection in synthetic aperture sonar imagery,” Proc. IEEE SAR/SAS 2010, vol. 32, no. 4, pp. 117-122, September 2010. PDF
N. Klausner, M. R. Azimi-Sadjadi, and J. D. Tucker, “Multi-sonar target detection using multi-channel coherence analysis,” Proc. MTS/IEEE Oceans 2010, pp. 1-7, September 2010. PDF
J. D. Tucker and M. R. Azimi-Sadjadi, “Neyman-Pearson detection of K-distributed random variables,” Proc. SPIE, vol. 7664, pp. Q1-Q12, April 2010. PDF
2009
M. Kabatek, M. R. Azimi-Sadjadi, and J. D. Tucker, “An underwater target detection system for electro-optical imagery data,” Proc. MTS/IEEE Oceans 2009, pp. 1-8, October 2009. PDF
N. Klausner, J. D. Tucker, and M. R. Azimi-Sadjadi, “Multi-platform target detection using multi-channel coherence analysis and robustness to the effects of disparity,” Proc. MTS/IEEE Oceans 2009, pp. 1-7, October 2009. PDF
N. Klausner, M. R. Azimi-Sadjadi, and J. D. Tucker, “Underwater target detection from multi-platform sonar imagery using multi-channel coherence analysis,” Proc. IEEE SMC 2009, pp. 2728-2733, 2009. PDF
2008
J. D. Tucker and M. R. Azimi-Sadjadi, “Coherence-based underwater target detection and classification for side-scan sonar imagery,” Sea Technology Magazine, vol. 12, pp. 10-14, December 2008. PDF
J. D. Tucker, N. Klausner, and M. R. Azimi-Sadjadi, “Target detection in M-disparate sonar platforms using multichannel hypothesis testing,” Proc. MTS/IEEE Oceans 2008, pp. 1-7, September 2008. PDF
M. R. Azimi-Sadjadi and J. D. Tucker, “Target detection from dual disparate sonar platforms using canonical correlations,” Proc. SPIE, vol. 6953, paper 69530J, March 2008. PDF
2007
J. D. Tucker, M. R. Azimi-Sadjadi, and G. J. Dobeck, “Canonical coordinates for detection and classification of underwater objects from sonar imagery,” Proc. IEEE OCEANS 2007 Conference Europe, pp. 1-6, June 2007. PDF
J. D. Tucker, M. R. Azimi-Sadjadi, and G. J. Dobeck, “Coherent-based method for detection of underwater objects from sonar imagery,” Proc. SPIE, vol. 6553, paper 65530U, April 2007. PDF
Invited Talks
“Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources,” Environmetrics TIES Webinar, October 24, 2025. Slides · Video
“Bayesian Model Calibration: An Elastic Approach,” Colorado State University Statistics Seminar, April 28, 2025. Slides
“Elastic Functional Data Analysis: with Applications to Functional Changepoint,” Stony Brook SIAM Speaker, November 19, 2024. Slides
“Bayesian Model Calibration: An Elastic Approach,” Purdue Statistical Colloquium, February 23, 2024. Slides
“Elastic Bayesian Model Calibration,” ASA SDNS Webinar, December 04, 2023. Slides · Video
“Elastic Bayesian Model Calibration,” FSU Shape Group Statistical Meeting, January 24, 2023. Slides
“Elastic Functional Data Analysis,” SMU Statistical Colloquium, November 4, 2022. Slides
“Elastic Functional Tolerance Bounds,” LANL Statistical Colloquium, December 16, 2020.
“Elastic Functional Data Analysis,” Brigham Young University Statistics Colloquium, December 4, 2019. Slides
“Sandia Applications of Functional Data Analysis,” University of Illinois Statistics Colloquium, November 22, 2019.
“Elastic Functional Data Analysis,” Australian National University Statistics Colloquium, July 12, 2018.
“Elastic Functional Data Analysis,” University of New Mexico Statistics Colloquium, February 2, 2018.
“Doppler Multi-INT Detection and Tracking,” NSWC-PCD Invited Lecture, October 1, 2015.
“Alignment and Analysis of Proteomics Data using Square Root Slope Function Framework,” CTW: Statistics of Time Warpings and Phase Variations 2012, OSU MBI, Nov 7, 2012. Slides
“Statistical Analysis and Modeling of Elastic Functions,” Department of Mathematical and Statistical Sciences, University of Alberta, Aug 23, 2011 (Organizer: Yau Shu Wong). Slides
Patents
“Density-Based Clustering of Multidimensional Data,” US Patent Pending.
“Method and system for training an artificial neural network utilizing physics based knowledge,” US Patent No. US12229934. PDF
“Automated Change Detection Technique for Synthetic Aperture Sonar,” US Patent No. US10049295B2. PDF
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