ab7_photo
Office: Martin O-309
Phone: (864) 656-1716
Email: ab7@clemson.edu
Fax: (864) 656-5230

Andrew Brown

Position:
Associate Professor
Students:
Education:
PhD in Statistics from University of Georgia, 2013
MS in Statistics from University of Georgia, 2010
BS in Applied Mathematics from Georgia Institute of Technology, 2006
Professional Research Interests
Bayesian statistics, uncertainty quantification, computer experiments, neuroimaging data analysis, Bayesian computation, (Gaussian) Markov random fields
Professional Memberships
International Society for Bayesian Analysis
Eastern North American Region of the International Biometric Society
American Statistical Association
Other Professional Activities
Treasurer, Industrial Statistics Section of the International Society for Bayesian Analysis (2018-19)
Treasurer, South Carolina Chapter of the American Statistical Association  (2017 - present)
Reader for the AP Statistics Exam
Professional Honors and Awards
Visiting Research Fellow, Statistical and Applied Mathematical Sciences Institute, Spring 2016
IMS New Researchers Travel Award, 2016
Selected Publications
Ehrett, C.*, Brown, D. A., Chodora, E.*, Kitchens, C., and Atamturktur, S. (2019+), "Coupling material and mechanical design processes via computer model calibration," under revision. [arXiv]

Brown, D. A., McMahan, C. S., Shinohara, R. T., and Linn, K. A. (2019+), "Bayesian spatial binary regression for label fusion in structural neuroimaging," under review. [arXiv]

Prabhu, S., Ehrett, C.*, Javanbarg, M., Brown, D. A., Lehmann, M., and Atamturktur, S. (2019), "Uncertainty quantification in fault tree analysis: Estimating business interruption due to seismic hazard," Natural Hazards Review, accepted.

Flynn, G. S., Chodora, E.*, Atamtuktur, S., and Brown, D. A. (2019), "A Bayesian inference-based approach to empirical training of strongly-coupled constituent models," ASME Journal of Verification, Validation, and Uncertainty Quantification, accepted. [DOI]

Saibaba, A., Bardsley, J., Brown, D. A., and Alexanderian, A. (2019), "Efficient marginalization-based MCMC methods for hierarchical Bayesian inverse problems," SIAM/ASA Journal on Uncertainty Quantification, 7, 1105-1131. [DOI] [arXiv]

Brown, D. A., McMahan, C. S., and Self, S. W.* (2019), "Sampling strategies for fast updating of Gaussian Markov random fields," The American Statistician, to appear. [DOI] [arXiv]

Self, S. W.*, McMahan, C., Brown, D. A., Lund, R., Gettings, J., and Yabsley, M. (2018), "A large scale spatio-temporal binomial regression model for estimating seroprevalence trends," Environmetrics, 29:e2538. [arXiv]

Brown, D. A., Saibaba, A., and Vallelian, S. (2018), "Low rank independence samplers in hierarchical Bayesian inverse problems," SIAM/ASA Journal on Uncertainty Quantification, 6, 1076-1100. [arXiv]

Stevens, G. N.*, Atamturktur, S., Brown, D. A., Williams, B., and Unal, C. (2018), "Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling," Engineering Computations, 35, 672-691.

Atamturktur, S., Stevens, G. N., and Brown, D. A. (2017), "Empirically improving model adequacy in scientific computing," in Model Validation and Uncertainty Quantification 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017, eds. Barthorpe, R., Platz, R., Lopez, I., Moaveni, B., Papadimitriou, C., P. 363 - 370.

Brown, D. A. and Atamturktur, S. (2018), "Nonparametric functional calibration of computer models," Statistica Sinica, 28, 721-742. [arXiv]

Brown, D. A., Datta, G. S., and Lazar, N. A. (2017), "A Bayesian generalized CAR model for correlated signal detection," Statistica Sinica, 27, 1125-1153. [arXiv]

Atamturktur, S. and Brown, D. A. (2015), "State-aware calibration for inferring systematic bias in computer models of complex systems," NAFEMS World Congress Proceedings, June 21-24, San Diego, CA. ISBN 978-1-910643-24-2.

Brown, D. A., Lazar, N. A., Datta, G. S., Jang, W., and McDowell, J. E. (2014), "Incorporating spatial dependence into Bayesian multiple testing of statistical parametric maps in functional neuroimaging," NeuroImage, 84, 97-112.

* indicates student author


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Last Updated: 9/16/19