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J. Derek Tucker

Profile picture for J. Derek Tucker

Contact Information

101 Illini Hall
M/C 374
Champaign, IL 61820
Adjunct Clinical Associate Professor


I obtained my Ph.D. in Statistics from Florida State University. I received the B.S. and M.S. degrees from Colorado State University, Fort Collins, in 2007 and 2009, respectively, both in electrical engineering, with an emphasis on statistical signal processing. My dissertation was advised by Anuj Srivastava and Wei Wu and focused modeling of unaligned functional data. Currently, I am a Principal Member of the Technical Staff at Sandia National Laboratories in Albuquerque, NM, and an adjunct faculty member of the Department of Statistics at the University of Illinois. At Illinois, I teach a topics course on functional data analysis. Recently, I received the Director of National Intelligence Team Award for contributions to the Signal Location in Complex Environments (SLiCE) team.

My main interest lies in the area of functional data analysis with a focus on geometric methods and unaligned data. I recently have emphasized on statistical analysis of data of this type. My other research interests include statistical image understanding, statistical learning methods, and point processes.


Doctor of Philosophy in Statistics (2014)
Florida State University, Tallahassee, FL
Thesis title: Functional Component Analysis and Regression Using Elastic Methods PDF
Thesis advisor: Prof. Anuj Srivastava
Thesis co-advisor: Prof. Wei Wu

Master of Science in Electrical Engineering (2009)
Colorado State University, Fort Collins, CO
Thesis title: Coherence-based Underwater Target Detection for Side-Scan Sonar Imagery PDF
Thesis advisor: Prof. Mahmood Azimi

Bachelor of Science in Electrical Engineering (2007)
Colorado State University, Fort Collins, CO
Senior project: Magnetic Spin Wave Detection and Localization

Highlighted Publications

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 29, no. 2. pp 318-330, Apr 2014. [PDF | Code]

J. D. Tucker, W. Wu, and A. Srivastava, “Generative Models for Function Data using Phase and Amplitude Separation,” Computational Statistics and Data Analysis, vol. 61, pp. 50-66, May 2013. [PDF | R | MATLAB | Python | Julia]


Recent Publications

T. Harris, B. Li, and J. D. Tucker. “Scalable Multiple Changepoint Detection for Functional Data Sequences”, submitted, 2020. arxiv preprint

J. D. Tucker, L. Shand, and K. Chowdhary. “A Robust Bayesian Approach to Function Registration in R1R1” Computational Statistics and Data Analysis, submitted, 2020. arxiv preprint

T. Harris, B. Li, N. J. Steiger, J. E. Smerdon, N. Narisetty, and J. D. Tucker. “Testing the exchangeability of two ensembles of spatial processes - Evaluating proxy influence in assimilated paleoclimate reconstructions” Journal of the American Statistical Association, 10.1080/01621459.2020.1799810, 2020. arxiv preprint

C. King, N. Martin, and J. D. Tucker. “Bounding Uncertainty in Functional Data: A Case Study in Reliability” Quality Engineering, accepted, 2020. PDF

T. Harris, J. D. Tucker, B. Li, and L. Shand, “Elastic depths for detecting shape anomalies in functional data,” Technometrics, 10.1080/00401706.2020.1811156, 2020. arxiv preprint

M. K. Ahn, J. D. Tucker, W. Wu, and A. Srivastava. “Regression Models Using Shapes of Functions as Predictors” Computational Statistics and Data Analysis, 10.1016/j.csda.2020.107017, 2020. PDF

S. Reza, N. Martin, T. Buchheit, and J. D. Tucker, “Tolerance Bound Calculation for Compact Model Calibration Using Functional Data Analysis,” Proc ETDM20, 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, 2019. PDF

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 41, pp 444–463, 2019 PDF