Alan E. Gelfand
James B. Duke Emeritus Professor of Statistical Science
Double MOCHA: Phase II Multi-study Ocean acoustic Human effects Analysis awarded by University of St. Andrews (Co-Principal Investigator). 2018 to 2021
EAGER-NEON: Probabilistic forecasting of biodiversity response to intensifying drought: A proposal to combine NEON with national climate, species, and trait data bases awarded by National Science Foundation (Investigator). 2015 to 2018
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.
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
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.
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