Alan E. Gelfand

Alan E. Gelfand

James B. Duke Emeritus Professor of Statistical Science

External address: 
223A Old Chem Bldg, Durham, NC 27708
Internal office address: 
Box 90251, Durham, NC 27708-0251
(919) 668-5229

Selected Grants

Double MOCHA: Phase II Multi-study Ocean acoustic Human effects Analysis awarded by University of St. Andrews (Co-Principal Investigator). 2018 to 2021

EMSW21-RTG: Geometric, Topological awarded by National Science Foundation (Key Faculty). 2011 to 2018

Collaborative Research: Climate Change Impacts on Forest Biodiversity: Individual Risk to Subcontinental Impacts awarded by National Science Foundation (Co-Principal Investigator). 2012 to 2017

CDI-Type II: Integrating Algorithmic and Stochastic Modeling Techniques for Environmental Prediction awarded by National Science Foundation (Co-Principal Investigator). 2009 to 2014

Space-time Modeling for Linking Climate Change, Pollutant Exposure, Built Environments, and Health Outcomes awarded by North Carolina State University (Principal Investigator). 2011 to 2014

Dynamic Sensor Networks-Enabling the Measurement, Modeling, and Prediction of Biophysical Change in a Landscape awarded by National Science Foundation (Co-Principal Investigator). 2006 to 2012

Sharing Confidential Datasets With Geographic Identifiers Via Multiple Imputation awarded by National Institutes of Health (Co Investigator). 2009 to 2012

Public Health Research Grant awarded by Centers for Disease Control and Prevention (Principal Investigator). 2008 to 2009

Bayesian Nonparametric Methods for Spatial and Spatiotemporal Data awarded by National Science Foundation (Principal Investigator). 2005 to 2009


Trevisani, M., and A. Gelfand. “Spatial misalignment models for small area estimation: A simulation study.” Studies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies, 2013, pp. 269–79. Scopus, doi:10.1007/978-3-642-35588-2_25. Full Text

Gelfand, A. E., et al. “Spatial Design for Knot Selection in Knot-Based Dimension Reduction Models.” Spatio-Temporal Design: Advances in Efficient Data Acquisition, 2012, pp. 142–69. Scopus, doi:10.1002/9781118441862.ch7. Full Text

Gelfand, A. E. “Gibbs sampling.” Statistics in the 21st Century, 2001, pp. 341–49.

Schliep, E. M., and A. E. Gelfand. “Velocities for spatio-temporal point patterns.” Spatial Statistics, vol. 29, Mar. 2019, pp. 204–25. Scopus, doi:10.1016/j.spasta.2018.12.007. Full Text

Wang, F., et al. “Rejoinder on: Process modeling for slope and aspect with application to elevation data maps.” Test, vol. 27, no. 4, Dec. 2018, pp. 783–86. Scopus, doi:10.1007/s11749-018-0623-1. Full Text

Lu, X., et al. “Local real-time forecasting of ozone exposure using temperature data.” Environmetrics, vol. 29, no. 7, Nov. 2018. Scopus, doi:10.1002/env.2509. Full Text

Schliep, E. M., et al. “Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data.” Journal of Agricultural, Biological, and Environmental Statistics, vol. 23, no. 3, Sept. 2018, pp. 334–57. Scopus, doi:10.1007/s13253-018-0327-8. Full Text

Wang, F., et al. “Disease Mapping With Generative Models.” American Statistician, July 2018, pp. 1–11. Scopus, doi:10.1080/00031305.2017.1392358. Full Text

Schliep, E. M., et al. “Assessing the joint behaviour of species traits as filtered by environment.” Methods in Ecology and Evolution, vol. 9, no. 3, Mar. 2018, pp. 716–27. Scopus, doi:10.1111/2041-210X.12901. Full Text

Schliep, Erin M., et al. “Alternating Gaussian Process Modulated Renewal Processes for Modeling Threshold Exceedances and Durations..” Stochastic Environmental Research and Risk Assessment : Research Journal, vol. 32, no. 2, Feb. 2018, pp. 401–17. Epmc, doi:10.1007/s00477-017-1417-9. Full Text

Wang, Feifei, et al. “Accommodating the ecological fallacy in disease mapping in the absence of individual exposures..” Statistics in Medicine, vol. 36, no. 30, Dec. 2017, pp. 4930–42. Epmc, doi:10.1002/sim.7494. Full Text Open Access Copy

White, P., et al. “Prediction and model comparison for areal unit data.” Spatial Statistics, vol. 22, Nov. 2017, pp. 89–106. Scopus, doi:10.1016/j.spasta.2017.09.002. Full Text

Paci, L., et al. “Analysis of residential property sales using space–time point patterns.” Spatial Statistics, vol. 21, Aug. 2017, pp. 149–65. Scopus, doi:10.1016/j.spasta.2017.06.007. Full Text


Ahn, S., et al. “Maximum weight matching using odd-sized cycles: Max-product belief propagation and half-integrality.” Ieee Transactions on Information Theory, vol. 64, no. 3, 2018, pp. 1471–80. Scopus, doi:10.1109/TIT.2017.2788038. Full Text

Caponera, A., et al. “Hierarchical spatio-temporal modeling of resting state fMRI data.” Springer Proceedings in Mathematics and Statistics, vol. 257, 2018, pp. 111–30. Scopus, doi:10.1007/978-3-030-00039-4_7. Full Text

Yu, R., et al. “Geographic segmentation via latent poisson factor model.” Wsdm 2016  Proceedings of the 9th Acm International Conference on Web Search and Data Mining, 2016, pp. 357–66. Scopus, doi:10.1145/2835776.2835806. Full Text

Pan, J., et al. “Markov-modulated marked poisson processes for check-in data.” 33rd International Conference on Machine Learning, Icml 2016, vol. 5, 2016, pp. 3311–20.

Gelfand, A. E., et al. “Belief propagation for linear programming.” Ieee International Symposium on Information Theory  Proceedings, 2013, pp. 2249–53. Scopus, doi:10.1109/ISIT.2013.6620626. Full Text

Chertkov, M., et al. “Loop calculus and bootstrap-belief propagation for perfect matchings on arbitrary graphs.” Journal of Physics: Conference Series, vol. 473, no. 1, 2013. Scopus, doi:10.1088/1742-6596/473/1/012007. Full Text

Shin, J., et al. “A graphical transformation for belief propagation: Maximum Weight Matchings and odd-sized cycles.” Advances in Neural Information Processing Systems, 2013.

Gelfand, A. E., and M. Welling. “Generalized belief propagation on tree robust structured region graphs.” Uncertainty in Artificial Intelligence  Proceedings of the 28th Conference, Uai 2012, 2012, pp. 296–305.

Welling, M., et al. “A cluster-cumulant expansion at the fixed points of belief propagation.” Uncertainty in Artificial Intelligence  Proceedings of the 28th Conference, Uai 2012, 2012, pp. 883–92.

Chen, Y., et al. “Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation.” Proceedings of the Ieee International Conference on Computer Vision, 2011, pp. 2635–42. Scopus, doi:10.1109/ICCV.2011.6126553. Full Text