selected publications academic article Ren, T., Yang, P., Wei, J., Huang, X., & Sang, H. (2022). Performance of Cloud 3D Solvers in Ice Cloud Shortwave Radiation Closure Over the Equatorial Western Pacific Ocean. Journal of Advances in Modeling Earth Systems. 14(2), Luo, Z. T., Sang, H., & Mallick, B. (2021). A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables. JOURNAL OF MACHINE LEARNING RESEARCH. 22(37), 1-52. Zhao, H., Merchant, N. N., McNulty, A., Radcliff, T. A., Cote, M. J., Fischer, R., Sang, H., & Ory, M. G. (2021). COVID-19: Short term prediction model using daily incidence data. PLoS ONE. 16(4), e0250110-e0250110. Shin, Y. E., Sang, H., Liu, D., Ferguson, T. A., & Song, P. (2019). Autologistic network model on binary data for disease progression study. 75(4), 1310-1320. Li, F., & Sang, H. (2019). Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. 114(527), 1050-1062. Li, D., Chiang, Y., Sang, H., & Sullivan, W. C. (2019). Beyond the school grounds: Links between density of tree cover in school surroundings and high school academic performance. Urban Forestry and Urban Greening. 38, 42-53. Li, F., & Sang, H. (2018). On approximating optimal weighted composite likelihood method for spatial models. 7(1), Hong, Y. A., Forjuoh, S. N., Ory, M. G., Reis, M. D., & Sang, H. (2017). A Multi-Level, Mobile-Enabled Intervention to Promote Physical Activity in Older Adults in the Primary Care Setting (iCanFit 2.0): Protocol for a Cluster Randomized Controlled Trial. JMIR Research Protocols. 6(9), e183-e183. Li, F., Sang, H., & Jing, Z. (2017). Quantify the continuous dependence of SSTturbulent heat flux relationship on spatial scales. 44(12), 6326-6333. Dai, S. Y., Sang, H., Lee, K., & Herrman, T. J. (2016). Cost of speed: A practical approach to evaluate a screening method from a Bayesian perspective. Chemometrics and Intelligent Laboratory Systems. 156, 273-279. Tapia, G., Elwany, A. H., & Sang, H. (2016). Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. ADDITIVE MANUFACTURING. 12, 282-290. Chakraborty, A., De, S., Bowman, K. P., Sang, H., Genton, M. G., & Mallick, B. K. (2015). An adaptive spatial model for precipitation data from multiple satellites over large regions. Statistics and Computing. 25(2), 389-405. Genton, M. G., Padoan, S. A., & Sang, H. (2015). Multivariate max-stable spatial processes. Biometrika. 102(1), 215-230. Zhang, B., Sang, H., & Huang, J. Z. (2015). FULL-SCALE APPROXIMATIONS OF SPATIO-TEMPORAL COVARIANCE MODELS FOR LARGE DATASETS. STATISTICA SINICA. 25(1), 99-114. Zhang, B., Konomi, B. A., Sang, H., Karagiannis, G., & Lin, G. (2015). Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. Journal of Computational Physics. 300, 623-642. Konomi, B. A., Sang, H., & Mallick, B. K. (2014). Adaptive Bayesian Nonstationary Modeling for Large Spatial Datasets Using Covariance Approximations. Journal of Computational And Graphical Statistics (JCGS). 23(3), 802-829. Sang, H., & Genton, M. G. (2014). Tapered composite likelihood for spatial max-stable models. Spatial Statistics. 8(C), 86-103. Sang, H., & Huang, J. Z. (2012). A full scale approximation of covariance functions for large spatial data sets. Journal of the Royal Statistical Society Series B: Statistical Methodology. 74(1), 111-132. Sang, H., Jun, M., & Huang, J. Z. (2011). COVARIANCE APPROXIMATION FOR LARGE MULTIVARIATE SPATIAL DATA SETS WITH AN APPLICATION TO MULTIPLE CLIMATE MODEL ERRORS. Annals of Applied Statistics. 5(4), 2519-2548. Genton, M. G., Ma, Y., & Sang, H. (2011). On the likelihood function of Gaussian max-stable processes. 98(2), 481-488. Sang, H., & Gelfand, A. E. (2010). Continuous Spatial Process Models for Spatial Extreme Values. 15(1), 49-65. Sang, H., & Gelfand, A. E. (2009). Hierarchical modeling for extreme values observed over space and time. Environmental and Ecological Statistics. 16(3), 407-426. Finley, A. O., Sang, H., Banerjee, S., & Gelfand, A. E. (2009). Improving the performance of predictive process modeling for large datasets. Computational Statistics and Data Analysis. 53(8), 2873-2884. Sang, H., Gelfand, A. E., Lennard, C., Hegerl, G., & Hewitson, B. (2008). INTERPRETING SELF-ORGANIZING MAPS THROUGH SPACE-TIME DATA MODELS. Annals of Applied Statistics. 2(4), 1194-1216. Banerjee, S., Gelfand, A. E., Finley, A. O., & Sang, H. (2008). Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society Series B: Statistical Methodology. 70(4), 825-848. Latimer, A. M., Banerjee, S., Sang, H., Mosher, E. S., & Silander, J. A Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States. Ecology Letters. 12(2), 144-154. chapter Sang, H. (2016). Spatial Extremes and Max-Stable Processes Mathieu Ribatet, Clement Dombry, and Marco Oesting. Extreme Value Modeling and Risk Analysis. (pp. 199-214). Taylor & Francis. conference paper Tian, Y., Ayers (ret.), W. B., Sang, H., McCain, W. D., & Ehlig-Economides, C. (2018). Quantitative Evaluation of Key Geological Controls on Regional Eagle Ford Shale Production Using Spatial Statistics. SPE Reservoir Evaluation and Engineering. 238-256. Zhou, P., Pan, Y., Sang, H., & Lee, W. J. (2018). Criteria for Proper Production Decline Models and Algorithm for Decline Curve Parameter Inference Tian, Y., Ayers, W. B., Sang, H., McCain, W. D., & Ehlig-Economides, C. (2017). Quantitative Evaluation of Key Geological Controls on Regional Eagle Ford Shale Production Using Spatial Statistics. 113-134. Zhou, P., Sang, H., Jin, L., & Lee, W. J. (2017). Application of Statistical Methods to Predict Production From Liquid-Rich Shale Reservoirs Han, W., Sang, H., Sheng, M., Li, J., & Cui, S. (2015). Efficient learning of statistical primary patterns via Bayesian network. 2013 National Conference on Communications, NCC 2013. 4871-4876.
principal investigator on Bayesian and Regularization Methods for Spatial Homogeneity Pursuit with Large Datasets awarded by National Science Foundation - (Arlington, Virginia, United States) 2019 - 2022 ATD: A Statistical Geo-Enabled Dynamic Human Network Analysis awarded by National Science Foundation - (Arlington, Virginia, United States) 2017 - 2020 Statistical Modeling and Computation of Extreme Values in Large Datasets awarded by National Science Foundation - (Arlington, Virginia, United States) 2016 - 2019 Collaborative Research: EARS: Large-Scale Statistical Learning Based Spectrum Sensing and Cognitive Networking awarded by National Science Foundation - (Arlington, Virginia, United States) 2014 - 2017
investigator on A longitudinal investigation of the cerebellum in adulthood: anatomical and network changes, motor function, and cognition awarded by National Institutes of Health - (Bethesda, Maryland, United States) 2019 - 2024
teaching activities ARSC292 Co-op Ed In Arts & Sciences Instructor ARSC492 Co-op Ed In Arts & Sciences Instructor STAT211 Prin Of Statistics I Instructor STAT404 Statistical Computing Instructor STAT415 Math Statistics Ii Instructor STAT482 Statistics Capstone Instructor STAT484 Internship Instructor STAT491 Research Instructor STAT610 Distribution Theory Instructor STAT647 Spatial Statistics Instructor STAT685 Directed Studies Instructor STAT691 Research Instructor
chaired theses and dissertations Konomi, Bledar (2012-02). Bayesian Spatial Modeling of Complex and High Dimensional Data. Zhu, Xinxin (2013-07). Wind Speed Forecasting for Power System Operation.
education and training Ph.D. in Statistics, Duke University - (Durham, North Carolina, United States) 2008 B.S. in Mathematics and Applied Mathematics, Peking University - (Beijing, Beijing, China) 2004
awards and honors ASA Fellows, conferred by American Statistical Association - (Alexandria, Virginia, United States), 2024
mailing address Texas A&M University Department Of Statistics 3143 TAMU College Station, TX 77843-3143 USA