Jun Zhu

Position title: Professor, Statistics

Email: jzhu@stat.wisc.edu

Phone: 608-263-4499

6120 Medical Sciences Center
1300 University Avenue
Madison WI 53706

Jun Zhu


Department of Entomology, Department of Biostatistics and Medical Informatics, Center for Demography and Ecology

Honors and Awards 

Distinguished Achievement Medal, Section of Statistics and the Environment, American Statistical Association.
Elected Member, International Statistical Institute.
Fellow, American Statistical Association.
Presidential Award, American Society of Agronomy.


PhD  Iowa State University, 2000 (Statistics)
MSE  Johns Hopkins University,  1995 (Mathematical Sciences)
BA  Knox College, 1994 (Mathematics and Computer Science)

Research Interests

Spatial statistics, spatio-temporal statistics, Markov random fields, agricultural statistics, environmental statistics, statistical ecology, environmental/population health, disease mapping, medical imaging, spatial demography

The main components of my research activities are statistical methodological development and scientific collaborative research. My statistical methodological research concerns developing statistical methodology for analyzing spatially referenced data (spatial statistics) and spatial data repeatedly sampled over time (spatio-temporal statistics) that arise in the biological, physical, and social sciences. My collaborative research concerns applying modern statistical methods, especially spatial and spatio-temporal statistics, to studies of agricultural, biological, ecological, environmental, health, and social systems conducted by research scientists. To a large extent, my overall research program involves a close connection between the two types of research activities. Problems in my collaborative research that do not have adequate statistical tools motivate my statistical methodological research, whereas the new methods I develop in statistical methodological research are applied in my collaborative projects.

Selected Publications

Kamenetsky, M, Lee, J, Zhu, J, and Gangnon, R (2022). Regularized spatial and spatio-temporal cluster detection. Spatial and Spatio-Temporal Epidemiology, 41, 100462.

Liu, J, Chu, T, Zhu, J, and Wang, H (2021). Semiparametric method and theory for continuously indexed spatio-temporal processes. Journal of Multivariate Analysis, 183, 104735.

Lin, P-S and Zhu, J (2020). A heterogeneity measure for cluster identification with application to disease mapping. Biometrics, 76, 403–413.

Berg, S, Zhu, J, Clayton, MK, Shea, ME, and Mladenoff, DJ (2019) A latent discrete Markov field approach for identifying and classifying historical forest communities based on spatial multivariate tree species counts. Annals of Applied Statistics, 13, 2312-2340.

Al-Sulami, D, Jiang, Z, Lu, Z, and Zhu, J (2019) On a semiparametric data-driven nonlinear model with penalized spatio-temporal lag interactions. Journal of Time Series Analysis, 40, 327-342.

Feng, X, Zhu, J, Steen-Adams, MM, and Lin, P-S (2017). Composite likelihood approach to the regression analysis of spatial multivariate ordinal data and spatial compositional data with exact zero values. Environmental and Ecological Statistics, 24, 39-68.

Lee, J, Gangnon, R, and Zhu, J (2017) Cluster detection of spatial regression coefficients. Statistics in Medicine, 16, 1118-1133.

Corsi, SR, Borchardt, MA, Carvin, RB, Burch, TR, Spencer, SK, Lutz, MA, McDermott, CM, Busse, KM, Kleinheinz, GT, Feng, X, and Zhu, J (2016) Human and bovine viruses and bacteria at three Great Lakes beaches: Environmental variable associations and health risk. Environmental Science and Technology, 50, 987-995.

Thurman, AL, Fu, R, Guan, Y, and Zhu, J (2015) Regularized estimating equations for model selection of clustered spatial point processes. Statistica Sinica, 25, 173-188.

Chu, T, Wang, H, and Zhu, J (2014) On semiparametric inference of geostatistical models via local Karhunen-Loeve expansion. Journal of the Royal Statistical Society Series B, 76, 817-832.

Curtis, KJ, Reyes, PE, O’Connell, H, and Zhu, J (2013) Assessing the spatial concentration and temporal persistence of poverty: Industrial structure, racial/ethnic concentration, and the complex links to poverty. Spatial Demography, 1, 178-194.

Sambaraju, KR, Carroll, AL, Zhu, J, Moore, D, Stahl, K, and Aukema, BH (2012) Climate change could alter the distribution of mountain pine beetle outbreaks in western Canada. Ecography, 35, 211-223.

Chu, T, Zhu, J, and Wang, H (2011). Penalized maximum likelihood estimation and variable selection in geostatistics. Annals of Statistics, 39, 2607-2625.

Zhu, J, Huang, H-C, and Reyes, PE (2010) On selection of spatial linear models for lattice data. Journal of the Royal Statistical Society Series B, 72, 389-402.

Zheng, Y, Zhu, J, and Roy, A (2010) Nonparametric Bayesian inference for the spectral density function of a random field. Biometrika, 97, 238-245.

Chi, G and Zhu, J (2008) Spatial regression models for demographic analysis. Population Research and Policy Review, 27, 17-42.

Zhu, J, Rasmussen, JG, Møller, J, Aukema, BH, and Raffa, KF (2008). Spatial-temporal modeling of forest gaps generated by colonization from below- and above-ground bark beetle species. Journal of the American Statistical Association, 103, 162-177.

Lahiri, SN and Zhu, J (2006) Resampling methods for spatial regression models under a class of stochastic designs. Annals of Statistics, 34, 1774-1813.

Ives, AR and Zhu, J (2006) Statistics for correlated data: phylogenies, space, and time. Ecological Applications, 16, 20-32.

Zhu, J, Huang, H-C, and Wu, J (2005). Modeling spatial-temporal binary data using Markov random fields. Journal of Agricultural, Biological, and Environmental Statistics, 10, 212–225.

Zhu, J., Eickhoff, JC, and Yan, P (2005). Generalized linear latent variable models for repeated measures of spatially correlated multivariate data. Biometrics, 61, 674-683.

Zhu, J, Morgan, CLS, Norman, JM, Yue, W, and Lowery, B (2004) Combined mapping of soil properties using a multi-scale tree-structured spatial model. Geoderma, 118, 321–334.


Chi, G. and Zhu, J. (2019). Spatial Regression Models for the Social Sciences. SAGE, Thousand Oaks, CA.

Courses Taught

Statistics 571: Statistical Methods for Bioscience I
Statistics 572: Statistical Methods for Bioscience II
Statistics 575: Statistical Methods for Spatial Data
Statistics 701: Applied Time Series Analysis, Forecasting, and Control I
Statistics 849: Theory and Application of Regression and Analysis of Variance I
Statistics 992: Statistics for Spatial Data: Theory and Methods
Statistics 998: Statistical Consulting
Entomology 901: The Tao of Statistics