**Peer-Reviewed Manuscripts**

Sakitis, C., **Brown, A.**, and Rowe, D., “Increased accuracy in statistical analysis of task activation with a formal Bayesian approach to SENSE image reconstruction,” submitted.

Niu, J., Hur, B., Absher, J., and **Brown, D. A., **“Bayesian regularization for functional graphical models,” under revision. arXiv preprint

Nicholson, J., Kiessler, P., and **Brown, D. A.**, “A kernel-based approach for Gaussian process modeling with functional information,” under revision. arXiv preprint

**Brown, D. A.**, McMahan, C. S., Shinohara, R. T., and Linn, K. L. (2022), “Bayesian spatial binary regression for label fusion in structural neuroimaging,” *Journal of the American Statistical Association, *117, 547-560.

Mokalled, S., McMahan, C., Tebbs, J., **Brown, D. A.**, and Bilder, C. (2021), “Incorporating the dilution effect into group testing regression,” *Statistics in Medicine*, 40, 2540-2555.

Ehrett, C., **Brown, D. A.**, Kitchens, C., Xu, X., Platz, R., and Atamturktur, S. (2021), “Simultaneous Bayesian calibration and engineering design with application to a vibration isolation system,” *ASME Journal of Verification, Validation, and Uncertainty Quantification, *6:011007.

**Brown, D. A., **McMahan, C. S., and Self, S. W. (2021), “Sampling strategies for fast updating of Gaussian Markov random fields,” *The American Statistician*, 75, 52-65. (R Code (.zip))

Ehrett, C., **Brown, D. A.**, Chodora, E., Kitchens, C., and Atamturktur, S. (2021), “Multi-objective engineering design via computer model calibration,” *ASME **Journal of Mechanical Design*, 143:051702.

Gettings, J. R., Self, S. W., McMahan, C. S., **Brown, D. A.**, Nordone, S. K., and Yabsley, M. J. (2020), “Regional and local temporal trends of *Borrelia burgdorferi* and *Anaplasma *spp. seroprevalence in domestic dogs: contiguous United States 2013-2019,” *Frontiers in Veterinary Science, *7:561592.

Gettings, J., Self, S. C. W., McMahan, C. S., **Brown, D. A.**, Nordone, S. K., and Yabsley, M. J. (2020), “Local and regional temporal trends (2013-2019) of canine *Ehrlichia *spp. seroprevalence in the United States,” *Parasites and Vectors*, 13:153.

Prabhu, S., Ehrett, C., Javanbarg, M., **Brown, D. A.**, Lehmann, M., and Atamturktur, S. (2020), “Uncertainty quantification in fault tree analysis: Estimating business interruption due to seismic hazard,” *Natural Hazards Review*, 21:04020015.

Flynn, G. S., Chodora, E., Atamturktur, 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*, 4:021005.

Saibaba, A. K., 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.

Self, S. W., Pulaski, C. N., McMahan, C. S., **Brown, D. A.**, Yabsley, M. J., and Gettings, J. (2019), “Regional and local temporal trends in the prevalence of canine heartworm infection in the contiguous United States: 2012-2018,” *Parasites and Vectors*, 12:380.

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.

**Brown, D. A.**, Saibaba, A., and Vallélian, S. (2018), “Low rank independence samplers in hierarchical Bayesian inverse problems,” *SIAM/ASA Journal on Uncertainty Quantification*, 6, 1076-1100. (PDF / Supplement)

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

**Brown, D. A.** and Atamturktur, S. (2018), “Nonparametric functional calibration of computer models,” *Statistica Sinica*, 28, 721-742 (Matlab Code (.zip))

**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. (R Code (.zip))

**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

**Peer-Reviewed Proceedings**

Gallagher, E., Vagnozzi, A. M., Lanning, R., **Brown, D.,** Brown, C., Frady, K., Brisbane, J., Matthews, M., Murphy, J., Patel, K., Pfirman, A., Rabb, R., Roberts, R., Welch, R., and Gramopadhye, A. (2020), “Poverty and guidance: Challenges and opportunities in mathematics preparation for engineering,” *Proceedings of the 2020 American Society of Engineering Education Annual Conference and Exhibition, *June 21 – 24, Montreal, Canada.

Marcanikova, M., Gallagher, E., Brown, C., Brisbane, J., **Brown, A.**, Dunwoody, L. A., Frady, K., Hines, A., Murphy, J., Patel, K., Pfirman, A., Roberson, S., and Gramopadhye, A. (2019), “High school technology as a NON-predictor of first college math course,” *Proceedings of the 2019 American Society of Engineering Education Southeast Section Conference,* March 10-12, Raleigh, NC.

Gallagher, E., **Brown, D. A., **Brown, C. J., , Frady, K., Bass, P., Matthews, M., Peters, T., Rabb, R., Solan, I., Welch, R., and Gramopadhye, A. (2018), “Identifying mathematical pathways to engineering in South Carolina,” *Proceedings of the 2018 American Society of Engineering Education Annual Conference and Exhibition,* June 24-27, Salt Lake City, UT.* *

Gallagher, E., Brown, C. J., **Brown, D. A.**, Frady, K., Marcanikova, M., Atamturktur, S., Ihekweazu, S., Matthews, M., Rabb, R., Roberts, R., Solan, I., Welch, R., and Gramopadhye, A. K. (2018), “Statewide coalition: Supporting underrepresented populations in precalculus through organizational redesign toward engineering diversity (SC:SUPPORTED) year 1,” *Proceedings of the 2018 American Society of Engineering Education Annual Conference and Exhibition, *June 24-27, Salt Lake City, UT.

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

**Letters and Discussions**

**Brown, D. A.** (2022), Discussion of “Deep Gaussian processes for calibration of computer models,” by S. Marmin and M. Filippone, *Bayesian Analysis*, 17, 1342-1343.

**Brown, D. A.** and Lazar, N. A. (2018), Discussion of “Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging,” by M. Bezener, J. Hughes, and G. Jones, *Bayesian Analysis*, 13, 1307-1308.

**Book Chapters**

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

**Book Reviews**

**Brown, D. A.** (2017), Review of *Analysis of Neural Data*, by R. E. Kass, U. T. Eden, and E. N. Brown, *Biometrics, *73, 710-713.

**Unreviewed Proceedings**

Sakitis, C. J., Rowe, D. B., and **Brown, D. A. **(2021), “A formal Bayesian approach to SENSE image reconstruction,” in *Proceedings of the 2021 Joint Statistical Meetings*, Section on Statistics in Imaging.

Stevens, G. N., Atamturktur, S., and **Brown, D. A.** (2017), “Empirical training of constituent models: Defining meso-scale behavior in a multi-scale plasticity model,” *IMAC XXXV, *Society for Experimental Mechanics, Jan. 30 – Feb. 2, Garden Grove, CA.

**Brown, D. A.**, Lazar, N. A., and Datta, G. S. (2011), “Bayesian multiple testing under dependence with application to functional magnetic resonance imaging,” in *Proceedings of the 2011 Joint Statistical Meetings*, Bayesian Statistical Science Section, Alexandria:American Statistical Association, pp. 4708 – 4722.

Jaeger, A., **Brown, D. A.**, Seymour, L., and Beuckert, R. (2010), “Response of Canadian crop yields to climate change,” in *Proceedings of the 2010 Joint Statistical Meet**ings*, Statistics and the Environment Section, Alexandria: American Statistical Association, pp. 4395 – 4405.