Selected Publications 

Please see a complete list of publications from my google scholar

(*): current or former students/postdocs supervised by Sang, H.

• Luo, Z.T. (*), Sang, H. and Mallick, B.K. (2023), A Nonstationary Soft Partitioned Gaussian Process Model via Random Spanning Trees, Journal of the American Statistical Association, [link]

• Zhong, Y. (*), Sang, H., Cook, S. and Kellstedt, P. (2023), Sparse Spatially Clustered Coefficient Model via Adaptive Regularization, Computational Statistics and Data Analysis, 177 [link] 

• Zhang, B.(*), Sang, H., Luo, Z.T.  and Huang, H. (2023), Bayesian Clustering of Spatial Functional Data with Application to a Human Mobility Study During COVID-19, Annals of Applied Statistics, 17, 583-605. [link]

• Hu, G. , Geng, J.X., Xue, Y., Sang,H. (2023), Bayesian Spatial Homogeneity Pursuit for Functional Income Data, Bayesian Analysis, 18, 579-605. [link]

• Chang, H., Lee, C. J.(*), Luo, Z. T.(*), Sang, H., & Zhou, Q. (2022). Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems. NeurIPS (oral, acceptance rate < 3%) 35. [link]

• Luo, Z.T. (*), Sang, H. and Mallick, B.K. (2022), BAMDT: Bayesian Additive Semi-multivariate Decision Trees for Nonparametric Regression, International Conference on Machine Learning (ICML, Long oral, 2% acceptance rate), 14509-14256. [link] [code]

• Lee. C.J. (*) and Sang, H. (2022), Why the Rich Get Richer? On the Balancedness of Random Partition Models, International Conference on Machine Learning (ICML), 12521-12541. [link][code]

• Lee, C.J. (*), Luo, Z.T. (*) and Sang, H. (2021), T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs, NeurIPS, 34.[link][code]

• Yin, L. (*), Xu, G., Sang, H. and Guan, Y. (2021), Row-clustering of a Point Process-valued Matrix, NeurIPS, 34. [link][code]  

• Luo, Z.T. (*), Sang, H. and Mallick, B.K. (2021), BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain, NeurIPS, 34, [link][code][]

• Yin, L. (*), Sang, H. (2021), Fused Spatial Point Process Intensity Estimation on Complex Domains, Spatial Statistics, 46, 100547. [link]

• Luo, Z.T. (*), Sang, H. and Mallick, B.K. (2021), A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables, Journal of Machine Learning Research, 22, 1-52. [link]

• Shin, Y. (*), Liu, D., Sang,H. and Song P.X.K. (2021), A Binary Hidden Markov Model on Spatial Network for ALS Disease Spreading Pattern Analysis, Statistics in Medicine,  40, 3035-3052. [link]

• Yin, L. (*), Sang, H., Schnoebelen, D.J., Wels, B., Simmons, D., Mattson, A., Schueller, M. and Dai, S.Y. (2021), Risk Based Arsenic Rational Sampling Design for Public and Environmental Health Management, Chemometrics and Intelligent Laboratory Systems, 211. [link] 

• Li, F.(*) and Sang, H. (2019), Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets, Journal of the American Statistical Association, 114(527), 1050-1062. [link]

• Zhang, B. (*), Sang, H. and Huang, J. Z. (2019). Smoothed full-scale approximation of Gaussian process models for computation of large spatial data sets. Statistica Sinica, 29(4), 1711-1737.[link]

• Shin, Y. (*), Sang,H., Liu, D. and Song P.X.K. (2019), Autologistic Network Model on Binary Data for Disease Progression Study, Biometrics, 75, 1310-1320. [link]

• Li, F.(*), Sang, H. and Jing, Z. (2017), Quantify the Continuous Dependence of SST‐turbulent Heat Flux Relationship on Spatial Scales, Geophysical Research Letters, 44, 6326-6333.

• Zhang, B.(*), Konomi, B.(*), Sang, H, Karagiannis, G. and Lin, G.(2015), Full-scale Multi-output Gaussian Process Emulator with Nonseparable Auto-covariance Functions, Journal of Computational Physics, 300, 623-642.

• Genton, M., Padoan, S. and Sang, H. (2015), Multivariate Max-stable Spatial Processes, Biometrika, 102, 215-230.

• Sang, H. (2015), Composite Likelihood for Extreme Values, Book chapter in Extreme Value Modeling and Risk Analysis: Methods and Applications, Chapman Hall/CRC.

• Zhang, B.(*), Sang, H. and Huang, J.Z. (2015), Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets, Statistica Sinica, 25, 99-114.

• Konomi, B.*, Sang, H. and Mallick, B. (2014), Adaptive Bayesian nonstationary modeling for large spatial datasets using covariance approximations, Journal of Computational and Graphical Statistics, 23, 802-829. 

• Sang, H. and Huang, J.Z. (2012), A full-scale approximation of covariance functions for large spatial data sets, Journal of the Royal Statistical Society, Series B, 74, 111-132.

• Sang, H., Jun, M and Huang, J.Z. (2011), Covariance approximation for large multivariate spatial datasets with an application to multiple climate model errors, Annals of Applied Statistics, 5, 2519-2548.

• Genton, M., Ma, Y. and Sang, H. (2011), On the likelihood function of Gaussian max-stable processes, Biometrika, 98, 481-488.

• Sang, H. and Gelfand, A.E. (2010), Continuous Spatial Process Models for Extreme Values, Journal of Agricultural, Biological and Environmental Statistics, 15 ,49-65.

• Finley, A.O., Sang, H., Banerjee, S. and Gelfand A.E. (2009), Improving the Performance of Predictive Process Modeling for Large Datasets, Computational Statistics and Data Analysis, 53,2873-2884.

• Banerjee, S., Gelfand A.E., Finley, A.O. and Sang, H. (2008), Gaussian Predictive Process Models for Large Spatial Data Sets, Journal of the Royal Statistical Society, Series B, 70, 825-848. 

• Sang, H., Gelfand, A.E., Lennard, C., Hegerl, G. and Hewitson, B. (2008), Interpreting Self Organizing Maps Through Space Time Data Models, Annals of Applied Statistics, 2, 1194-1216

Selected Interdisciplinary Publications 

• Zhu, X., Lee, H., Sang, H., Muller, J., Yang, H., Lee, C., & Ory, M. (2022). Nursing home design and covid-19: implications for guidelines and regulation. Journal of the American Medical Directors Association, 23(2), 272-279.

• Zhou, P. (*), Sang,H., and Lu, L. (2021), Application of Machine Learning Methods to Well Completion Optimization: Problems with Groups of Interactive Inputs, SPE ATCE.

• Li, D., Chiang, Y.C., Sang, H. and Sullivan, W.C. (2019), Beyond the School Grounds: Links between Density of Tree Cover in School Surroundings and High School Academic Performance, Urban Forestry & Urban Greening, 38, 42-53

• Tian, Y. (*), Ayers, W. B., Sang, H., McCain, W.D. and Ehlig-Economides, C. (2018), Quantitative Evaluation of Key Geological Controls on Regional Eagle Ford Shale Production Using Spatial Statistics, SPE Reservoir Evaluation & Engineering, 21, 238-256

• Han, W. (*), Sang, H., Mao, X., Li, J., Zhang,Y. and Cui, S. (2017), Sensing Statistical Primary Network Patterns via Bayesian Network Structure Learning, IEEE Transactions on Vehicular Technology, 66, 3143-3157.

• Tapia, G. (*), Elwany, A. H and Sang, H. (2016), Prediction of Porosity in Metal-based Additive Manufacturing using Spatial Gaussian Process Models, Additive Manufacturing, 12, 282-290

• Apostolopoulos, Y., Lemke, M.K., Hege, A., Wideman, L., Sang, H., Sonmez, S. and Oberlin, D.J. (2016), Work and Chronic Disease: Comparison of Cardiometabolic Risk Markers Between Truck Drivers and the General U.S. Population, Journal of Occupational and Environmental Medicine, 58, 1098-1105

• Latimer, A.M., Banerjee, S., Sang, H., Mosher, E. and Silander, J.A. (2009), Hierarchical Models Facilitate Spatial Analysis of Large Data Sets: A Case Study on Invasive Plant Species in the Northeastern United States. Ecological Letters, 12, 144-154.

Current grants

• National Science Foundation-DMS (Role, PI), 07/2023-06/2026.

• National Science Foundation-DMS (Role, PI), 09/2022-08/2025.

• Shell Inc. AI Innovation Award (Role, PI), 01/2021-12/2023

• National Institute of Health R01 (Role, Co-PI/MPI), 08/2023-07/2028. (PI, Dongying Li)

• National Institute of Health R01 (Role, Co-Investigator), 09/2019-05/2024. (PI, Jessica Bernard)

Completed grants

• National Science Foundation-DMS (Role, PI), 08/2019-07/2023.

• National Science Foundation-DMS (Role, Investigator), 10/2019-09/2022
  “HDR Tripods: Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS)".

• National Science Foundation-DMS (Role, PI), 09/2017-08/2020. 

• National Institute of Health R01 (Role, Co-Investigator), 09/2015-08/2022. (PI: Chanam Lee, Xuemei Zhu, Marcia Ory). 

• National Science Foundation-DMS (Role, PI), 09/2016-08/2019.

• Texas A&M University-T3 Triad Fund(Role, Co-PI), 01/2019-12/2019.

• Biogen MA Inc. (Role, PI), 03/2017-12/2017. 

• National Science Foundation-CISE/CNS (Role, PI), 10/2013-12/2017.

• National Science Foundation-DMS (Role, PI), 07/2010-07/2014.

• TAMU-Institute for Applied Mathematics and Computational Science (IAMCS) (Role, PI or Co-PI), 06/2013-05/2014, 06/2010-05/2011,10/2008-05/2009.

• KAUST GRP award (Role, Investigator), 06/2008-05/2013.