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[Names in bold: lab members]

 Peer-reviewed  Books and Chapters

  1. Cheng, J. and Zhu HT. (2016). Diffusion Magnetic Resonance Imaging (dMRI). In Statistical Methods in Neuroimaging Data analysis. Edited by Ombao, H., Lindquist, M., Thompson,W. and Aston, J. Chapman & Hall/CRC, 65-107.
  2. Zhu HT, Joseph G. Ibrahim, Hyunsoon Cho, and Niansheng Tang (2010). Bayesian Influence Methods. In Frontiers of Statistical Decision Making and Bayesian Analysis (eds. M.-H., Chen,  D.K. Dey, P. Muller, D. Sun, and K. Ye). New York: Springer. pp.219-236.
  3. Bansal, R., …., Zhu HT (in alphabetic order). Neuroimaging methods in the study of childhood psychiatric disorders. Lewis’s Child and Adolescent Psychiatry: A Comprehensive Textbook, Fourth Edition, Edited by Melvin Lewis, 30 pages,  Philadelphia, Lippincott Williams & Wilkins, pp. 214-233, 2007.
  4. Zhu HT, Liang FM, Gu MG, and Peterson B. Stochastic approximation algorithms for estimation of spatial mixed models. Edited by Sik-Yum, Lee.   Handbook of Computing and Statistics with Application, Elsevier Science, pp. 399-421, 2007.
  5. Zhu HT and Zhang HP. Structure mixture regression models. In Development of Modern Statistics and Related Topics, H.P.Zhang and J.Huang (ed.), World Scientific Publisher, New Jersey, pp. 272-287, 2003.
  6. Wei BC, Wang F, and Zhu HT. Translate Bates, D. and Watts, D. (1988).    Nonlinear Regression Analysis and its Applications.  John Wiley and Sons, Inc., New York, into Chinese version. Statistics Publisher, Beijing,  R.China, pp.1-409,  1998.

Refereed Papers/Articles 

Peer-reviewed Papers In Press and Appeared in Journals

Statistical Journals

(Annals of Statistics, Journal of American Statistical Association, Biometrika, and Journal of Royal Statistical Society Series B are the top four statistical journals; Biometrics and Annals of Applied Statistics are the very best applied statistical journals.)

  1. Hai Shu, Zhe Qu, and H. Zhu. D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional data. Journal of Machine Learning Research, pp. 7615–7678, 2022.
  2. Li, T., Zhu, H.C., Li, T. and H. Zhu. Asynchronous functional linear regression models for longitudinal data in reproducing kernel Hilbert space. Biometrics, 00, 1– 16, 2022.
  3. Zhengwu Zhang, Yuexuan Wu, Di Xiong, Joseph G. Ibrahim, Anuj Srivastava & Hongtu Zhu. LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures (with discussion). Journal of the American Statistical Association, ACS, Volume 118, Issue 541, pp. 3 – 17, 2023.
  4. Huang, C. and H. Zhu. Functional Hybrid Factor Regression Model for Handling Heterogeneity in Imaging Studies. Biometrika, Volume 109, Issue 4, pp. 1133 – 1148, 2022.
  5. D. Xiong, Ying, Y., and Zhu, H. Intrinsic partially linear models for Manifold-valued Data. Information Processing and Management, Volume 59, Issue 4, p. 102954, 2022.
  6. Xingcai Zhou, Dehan Kong, A. B. Kashlak, Linglong Kong, R. Karunamuni and Hongtu Zhu. Functional Response Quantile Regression Model. Statistica Sinica, in press, 2022.
  7. Meiling Hao, Lianqiang Qu, Dehan Kong, Liuquan Sun, and Hongtu Zhu. Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model. Journal of Machine Learning Research, 22(147):1−39, 2021.
  8. Wang, X., Leo Liu, Y.F., and Hongtu Zhu. Functional finite mixture regression models. Statistica Sinica, in press, 2021.
  9. Pietrosanu, Matthew; Shu, Haoxu; Jiang, Bei; Kong, Linglong; Heo, Giseon; He, Qianchuan; Gilmore, John; Zhu, Hongtu. Estimation for the bivariate quantile varying coefficient model with application to diffusion tensor imaging data analysis. Biostatistics, Volume 24, Issue 2, pp. 465–480, 2021.
  10. Tingting Zou, Shurong Zheng, Zhidong Bai, Jianfeng Yao, and Hongtu Zhu. CLT for linear spectral statistics of large dimensional sample covariance matrices with dependent data, Statistical Paper, 63, pp. 605–664, 2021.
  11. Yang, H., Zhu, H., Ahn, M., and Ibrahim, J.G. Weighted functional linear Cox regression model. Statistical Methods in Medical Research, Volume 30, Issue 8, pp. 1917 – 1931, 2021.
  12. Zhang, Z.W., Wang, X., Kong, L., and Zhu, H. T. High-Dimensional Spatial Quantile Function-on-Scalar Regression. Journal of American Statistical Association, Volume 117, Issue 539, pp. 1563 – 1578, 2021.
  13. Liu, R.J., and Zhu, H.T. Statistical disease mapping for heterogeneous neuroimaging studies (with discussion). The Canadian Journal of Statistics, Volume 49, Issue 1, pp. 10 – 34, 2021.
  14. Bingxin Zhao & Hongtu Zhu. On Genetic Correlation Estimation With Summary Statistics From Genome-Wide Association Studies. Journal of the American Statistical Association, 117:537, 1-11, 2022.
  15. Xiaoqing Wang, Xinyuan Song, and Hongtu Zhu. Bayesian Latent Factor on Image Regression with Nonignorable Missing Data. Statistics in Medicine, Volume 40, Issue 4, pp. 920 – 932, 2020.
  16. Li, T., Li, T.F., Zhu, Z.Y., and Zhu, H.T. Regression Analysis of Asynchronous Longitudinal Functional and Scalar Data. Journal of American Statistical Association, 117:539, 1228-1242, 2020.
  17. Kim, J., Zhu, H., Wang, X. and Do, K. Scalable network estimation with L0 penalty. Statistical Analysis and Data Mining. Volume 14, Issue 1, pp. 18 – 30, 2020.
  18. KY Bak, KR Kim, PT Kim, JY Koo, C Park, H Zhu. Nonparametric matrix regression function estimation over symmetric positive definite matrices. Journal of the Korean Statistical Society, 1-23, 2020.
  19. Feng, X., Li, T., Song, X. and Zhu, H. T. Bayesian Scalar on Image Regression with Non-ignorable Non-response. Journal of American Statistical Association, 115:532, 1574-1597, 2020.
  20. Pan, W., Wang, X., Zhang, H., Zhu, H., and Zhu, J. Ball Covariance: A Generic Measure of Dependence in Banach Space. Journal of American Statistical Association, Volume 115, Issue 529, pp. 307 – 317, 2020.
  21. Wang, J., Ibrahim, J. G., and Zhu, H.T. Partial least squares for functional joint models with applications to the Alzheimer’s disease neuroimaging initiative study. Biometrics 76 (4), 1109-1119, 2020.
  22. Kong, H., An, B. G., Zhang, J. W., and Zhu, H. L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses. Journal of American Statistical Association, Volume 115, Issue 529, pp. 403 – 424, 2020.
  23. Shu, H., Wang, X. and Zhu, H. D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets. Journal of American Statistical Association, Volume 115, Issue 529, pp. 292 – 306, 2020.
  24. Liu,F., Liu, Y. F., and Zhu, H. MCNN: Masked Convolutional Neural Network for Supervised Learning Problems. Stat. 9;e290, 2020.
  25. Zhao, P. Y., Tang, N. S., and Zhu, H.T. Generalized Empirical Likelihood Inferences for Nonsmooth Moment Functions With Nonignorable Missing Values. Statistica Sinica, 30, 217-249, 2020.
  26. R. Zheng, G. Chen, J. Guo, and H. Zhu. Test Statistics for High Dimensional Correlation Matrices. Annals of Statistics. 47, 2887-2921, 2019.
  27. Chen, Z. Q., Gao, Q. B., Fu, B. and Zhu, H. Monotone Nonparametric Regression for Functional/Longitudinal Data. Statistica Sinica, 29 2229-2249, 2019.
  28. Kang, K., Song, X. Y., Hu, X. J., and Zhu, H.T. Bayesian adaptive group lasso for semiparametric hidden Markov models. Statistics in Medicine, 38, 1634-1650, 2019.
  29. Pan, W., Wang, X.Q., Xiao,W. and Zhu, H.T. A Generic Sure Independence Screening Procedure. Journal of American Statistical Association, 114, 928-937, 2019.
  30. Ma, T. Li, H. Zhu, and Z. Zhu. Quantile regression for functional partially linear models in high dimensions. Computational Statistics and Data Analysis, In press, 129, 135-146, 2019.
  31. Luo, S., Song, R., Gilmore, J., Stynder, M., and Zhu, H.T. FSEM: Functional Structural Equation Models  for Twin Functional Data. Journal of American Statistical Association, 114, 344-357, 2019.
  32. Sun, Q., Zhu, H.T., Ibrahim, J.G. Hard Thresholding Regression, Scandinavian Journal of Statistics, 46, 314-328, 2019.
  33. Zhu, H.T., Chen, K., Luo, X., Yuan, Y., and Wang, J. L. FMEM: Functional Mixed Processes Models for Longitudinal Functional Responses. Statistica Sinica, 29, 2007-2033, 2019.
  34. Tengfei Li, Fengchang Xie, Xiangnan Feng, Joseph G. Ibrahim, Hongtu Zhu. Functional linear regression models for non-ignorable missing scalar responses. Statistica Sinica, 28, 1867-1886, 2018.
  35. H. Yang, H. Zhu, and J.G. Ibrahim. MILFM: multiple index latent factor model based on high-dimensionalfeatures. Biometrics, 74, 834-844, 2018.
  36. Miranda, M. F., Zhu, H.T., and Ibrahim, J.G. TPRM: Tensor partition regression models with applications in imaging biomarker detection. Annals of Applied Statistics, 29, 1422-1450, 2018.
  37. Kong, D., Ibrahim, J. G., Lee, E.J. and Zhu, H.T. FLCRM: Functional Linear Cox Regression Model. Biometrics, 74, 109-117,  2018.
  38. Tang, M. L. Tang, N.S., Zhao, P.Y. and Zhu, H.T. Imputation Methods and Efficient Estimation for Linear Models with Missing Responses. Scandinavian Journal of Statistics, 45, 366-381, 2018.
  39. Tang, A.M., Tang, N.S., and Zhu, H.T. Influence analysis for skew-normal semiparametric joint models of multivariate longitudinal and multivariate survival data. Statistics in Medicine, 36, 1476-1490, 2017.
  40. Zhu, HT., Shen, D., Peng, X. W. and Leo Liu YF. MWPCR: Multiscale weighted principal component regression for high-dimensional prediction. Journal of American Statistical Association, 112, 1009-1021, 2017.
  41. Bryant, Zhu, H.T., Mihye Ahn, Joseph G Ibrahim. LCN: A Random Graph Mixture Model for Community Detection in Functional Brain Networks. Statistics and Its Interface, 10, 369-378, 2017.
  42. Lin, L., Thomas, B. S., Zhu, H.T. and Dunson, D. B. Extrinsic local regression on manifold-valued data. Journal of American Statistical Association, 112, 1156-1168, 2017.
  43. Wang, X. and Zhu, H.T. Generalized scalar-on-image regression models via total variation. Journal of American Statistical Association, 112,1156-1168, 2017.
  44. Song, X. Y, Xia, Y.M. and Zhu, H.T. Hidden markov latent variable models with multivariate longitudinal data. Biometrics, 73: 313-323, 2017.
  45. Li, J. L., Huang, C. and Zhu, H.T. A Functional Varying-Coefficient Single Index Model for Functional Response Data. Journal of American Statistical Association, 112, 1169-1181, 2017.
  46. Cornea, E., Zhu, H.T., Kim, P. and Ibrahim, J. G. Intrinsic regression model for data in Riemannian symmetric space. JRSS, Series B, 79, 463-482, 2017.
  47. Luo, X., Zhu, L.X., and Zhu, H.T. Single-index Varying Coefficient Model for Functional Responses. Biometrics, 72, 1275-1284, 2016.
  48. Shen, D., Shen, H., Zhu, H.T., and Marron, J. The statistics and mathematics of high dimension low sample size asymptotics. Statistica Sinica, 26, 1747–1770,
  49. Rao, S., Ibrahim, J. G., Cheng, G. Yap, P.T., and Zhu, H.T. SR-HARDI: Spatially regularizing high angular resolution diffusion Journal of Computational and Graphical Statistics, 25, 1195-1211. 2016.
  50. Lee, E.J. Zhu, H.T., D. Kong, Y. Wang, K. S. Giovanello, and Ibrahim, J. G. BFLCRM: A Bayesian functional linear Cox regression model (BFLCRM) for predicting time to conversion to Alzheimer’s disease. Annals of Applied Statistics, 9, 2153-2178, 2015.
  51. Zhu, H, Chen, M.H., and Ibrahim, J.G. Diagnostic measures for the Cox regression model with missing covariates, Biometrika, 102, 907-923, 2015.
  52. Huang, C., Styner, M., and Zhu, H.T. Penalized mixtures of offset-normal shape factor analyzers with application in clustering high-dimensional shape data. Journal of American Statistical Association, 110, 946-961, 2015.
  53. Mihye, A., Shen, H.P., Lin, W. L., and  Zhu, H.T.  A sparse reduced rank framework for group analysis of functional neuroimaging data. Statistica Sinica, 25, 295-312, 2015.
  54. Gao, Q. B., Mihye, A. and Zhu, H.T. Cook’s distance measures for varying coefficient models with functional response. Technometrics, 57, 268-280, 2015.
  55. Guo, R.X., Ahn Mihye, and Zhu, HT. Spatially weighted principal component analysis for imaging classification, Journal of Computational and Graphical Statistics, 24(1), 274-296, 2015.
  56. Sun, Q., Zhu, H.T., Liu, Y. F., and Ibrahim, J.G. SPReM: Sparse Projection regression model for high-dimensional linear regression.  Journal of American Statistical Association, 110, 289-302, 2015.
  57. Zhu, H.T., Chen, Q. X., and Ibrahim, J. G. Bayesian case-deletion model complexity and information criterion. Statistics and Its Interface, 4, 531-542, 2014.
  58. Zhu, H.T., Fan, J.Q., and Kong, L.L. Spatially varying coefficient models with applications in neuroimaging data with jumping discontinuity. Journal of American Statistical Association, 109, 977-990, 2014.
  59. Tang, N., Zhao, P.Y., and Zhu, H. T. Empirical likelihood for estimating equations with nonignorably missing data. Statistica Sinica, 24,723-747, 2014.
  60. Zhu, HT., Ibrahim, JG., and Tang, NS. Bayesian sensitivity analysis of statistical models with missing data. Statistica Sinica. 24, 871-896, 2014.
  61. Hua, Z.W., Zhu, HT, and Dunson, D. Semiparametric Bayes local additive models for longitudinal data. Statistics in Biosciences, in press, 2013.
  62. Zhou, H., Li, L.X., and Zhu, H.T. Tensor regression with applications in neuroimaging data analysis. Journal of American Statistical Association, 540-552, 2013.
  63. Xu, P., Wang, T., Zhu, H.T., and Zhu, L.X. Double penalized H-likelihood for selection of fixed and random effects in mixed effects models. Statistics in Biosciences, in press, 2013.
  64. Michelle F. Miranda, Zhu, H.T. and Joseph G. Ibrahim. Bayesian Analysis of Spatial Transformation Models with Applications in Neuroimaging Data. Biometrics, 69(4):1074-83. 2013.
  65. Khondker, Z. S., Zhu, H.T., Chu, H. T., Lin, W. L. and Ibrahim, J. G. The Bayesian Covariance Lasso, Statistics and its Interface, 6, 243-259, 2013.
  66. Yuan, Y., Zhu, H.T., Styner, M., H. Gilmore.,  and Marron, J. S.  Varying coefficient model for modeling diffusion tensors along white matter bundles. Annals of Applied Statistics, 7, 102-125, 2013.
  67. Wang, J., Zhu, H.T., Fan, J.Q., Giovanello, K. S., , and Lin, W. L. Multiscale adaptive smoothing models for the hemodynamic response function in fMRI. Annals of Applied Statistics, 7, 904-935, 2013.
  68. Shi, XY, Zhu, HT, Ibrahim, J.G., F. Liang, Styner M. Intrinsic regression models for median representation of subcortical structures. Journal of American Statistical Association, 107, 12-23, 2012.
  69. Yuan, Y., Zhu, H.T., Lin, W. L., and Marron, J. S. Local polynomial regression for symmetric positive definitive matrices. JRSS, Series B, 74, 697-719, 2012.
  70. Zhu, HT., Ibrahim JG, Cho HS. Perturbation and Scaled Cook’s distance. Annals of Statistics, 40, 785-811, 2012.
  71. Zhu, HT., Li, R., and Kong, L. Multivariate varying coefficient model and its application in neuroimaging data. Annals of Statistics, 40, 2634-2666, 2012.
  72. Guo, RX., Zhu, HT., Chow, SM., Ibrahim, JG. Bayesian Lasso for semiparametric structural equation models using spline. Biometrics, 68, 567–577, 2012.
  73. Zhu, HT., Ibrahim, JG., and Tang, NS. Bayesian influence measures for joint models for longitudinal and survival data. Biometrics, 68, 954-64, 2012.
  74. Skup, M., Zhu, H.T., and Zhang HP. Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates. Biometrics, 68,1083-1092, 2012.
  75. Zhu, HT, Ibrahim JG., Cho HS, and Tang, N.S. Bayesian case-deletion measures for statistical models with missing Data, Journal of Computational and Graphical Statistics, 21, 253-271, 2012.
  76. Wang, J., Shen, H. P., and Zhu, H.T. Discussion of the paper “Clustering Random Curves Under Spatial Interdependence with Application to Service Accessibility” by Jiang and Serban. Technometrics, 54, 129-133, 2012.
  77. Li, YM, Zhu HT,  Shen DG, Lin WL, Gilmore J, and Ibrahim JG.   Multiscale adaptive regression models for neuroimaging data. JRSS, Series B, 73, 559-578, 2011.
  78. Zhu, HT., Ibrahim JG, Tang NS. Bayesian  influence approach: a geometric approach. Biometrika, 98, 307-323, 2011.
  79. Shi, XY, Ibrahim JG, Styner M., Yimei Li, and Zhu, HT., Two-stage adjusted exponential tilted empirical likelihood for neuroimaging data. Annals of Applied Statistics, 5, 1132-1158, 2011.
  80. Ibrahim, J. G., Zhu, H.T., Garcia, R. I., Guo, R.X. Fixed and random effects selection for generalized mixed effects models, Biometrics, 67, 495-503, 2011.
  81. Ibrahim, J. G., Zhu, H.T., Tang, N. S. Bayesian local influence for survival models (with discussion). Lifetime Data Analysis, 17, 43-70, 2011.
  82. Ibrahim, J. G., Zhu, H.T., Tang, N. S. Rejoinder to comments on “Bayesian local influence for survival models.” Lifetime Data Analysis, 17, 76-79, 2011.
  83. Garcia, R. I., Ibrahim, J. G., Zhu, H.T. Variable selection for proportional hazard models with missing covariate data, Biometrics, 66, 97–104, 2010.
  84. Garcia, R. I., Ibrahim, J. G., and Zhu, H.T. Variable selection for regression models with missing covariate data, Statistics Sinica, 20, 149-165, 2010.
  85. Clement-Spychala, M. E., Couper, D., Zhu, H.T., and Muller, K. Approximating the Geisser greenhouse sphericity estimator and its applications to diffusion tensor imaging. Statistics and Its Interface, 3,81–90, 2010.
  86. Chen, F, Zhu HT, Song XY, and Lee SY. Perturbation selection and local influence analysis for generalized linear mixed models. Journal of Computational and Graphical Statistics, 19, 826-842, 2010.
  87. Zhu HT, Cheng YS., Ibrahim JG, Li YM, Hall C, Lin WL. Intrinsic regression models for positive definitive matrices with applications in diffusion tensor images. Journal of the American Statistical Association, 104, 1203-1212, 2009.
  88. Zhu HT, Li YM, Ibrahim, J. G., Shi, X.Y., …, Peterson BS. Rician regression models for magnetic resonance images. Journal of the American Statistical Association, 104, 623-637, 2009.
  89. Shi, X Y., Zhu HT, Ibrahim, JG. Local influence for generalized linear models with missing covariates. Biometrics, 65, 1164-1174, 2009.
  90. Zhu HT, Zhou HB, Chen JH, Li YM, Styner M, and Liberman J. Adjusted exponential tilted likelihoods with application to brain morphomotry, Biometrics,  65, 919-927, 2009.
  91. Cho HS, Ibrahim JG, Sinha and Zhu HT. Bayesian Case Influence Diagnostics for Survival Models, Biometrics, 65, 116-124, 2009.
  92. Zhu HT, Ibrahim, JG., Shi, X.Y. Diagnostic measures for generalized linear models with missing covariates.Scandivian Journal of Statistics, 36, 686-712, 2009.
  93. Zhu HT, Tang NS, Ibrahim JG, and Zhang HP. Diagnostic measures for empirical likelihood of general estimating equations. Biometrika, 95, 489-507, 2008.
  94. Ibrahim, JG., Zhu HT, Tang NS. Model selection criterion for missing data problems via the EM algorithm. Journal of the American Statistical Association, 103, 1648-1658, 2008.
  95. Zhu HT, Li YM, Tang NS, Bansal R, Hao XJ, Weissman MM, and Peterson B. Statistical modelling of brain morphometric measures in general pedigree. Statistica Sinica, 18, 1554-1569, 2008.
  96. Zhu HT, He FL, and Zhou J. Auto-multicategorical regression model for the distributions of vegetation types. Statistics and Its Interface, 1, 63-73, 2008.
  97. Zhu HT, Zhang HP, Ibrahim JG, and Peterson BG. Statistical analysis of diffusion tensors in diffusion-weighted magnetic resonance image data (with discussion). Journal of the American Statistical Association, 102, 1081-1110, 2007.
  98. Zhu HT, Zhang HP, Ibrahim JG, and Peterson BG. Rejoinder to comments on “Statistical analysis of diffusion tensors in diffusion-weighted magnetic resonance image data”. Journal of the American Statistical Association, 102, 1110-1113, 2007.
  99. Ibrahim, JG, and Zhu HT. Discussion of “Implementation of estimating function based inference procedures with MCMC samplers” by Tian. L, Liu J., and Wei LJ. Journal of the American Statistical Association, 102, 893-896, 2007.
  100. Zhu HT, Ibrahim, JG, Lee SY, and Zhang HP. Appropriate perturbation and influence measures in local influence. Annals of Statistics, 35, 2565-2588, 2007.
  101. Zhu HT, Gu MG, and Peterson BG. Maximum likelihood from spatial random effects models via the stochastic approximation expectation maximization algorithm. Statistics and Computing, 15, 163-177, 2007.
  102. Zhu HT and Zhang HP. Generalized score test of homogeneity for mixed effects models. Annals of Statistics, 34, 1545-1569, 2006.
  103. Zhu HT and Zhang HP. Asymptotics for estimation and testing procedures under loss of identifiability. Journal of Multivariate Analysis, 97:19-45, 2006.
  104. Zhu HT and Zhang HP. Hypothesis testing in a class of mixture regression models. Journal of the RoyalStatistical Society, Series B, 66:3-16, 2004.
  105. Zhu HT and Zhang HP. A diagnostic procedure based on local influence. Biometrika, 91:579-589, 2004.
  106. Zhang HP, Yu CY, Zhu HT, and Shi J. Identification of linear directions in multivariate adaptive spline models. Journal of American Statistical Association, 98:369-376, 2003.
  107. Zhang HP, Fui R, and Zhu HT. A latent variable model of segregation analysis for ordinal outcome. Journal ofAmerican Statistical Association, 98:1023-1034, 2003.
  108. He FL, Zhou JL, and Zhu HT. Autologistic regression model for the distribution of vegetation. Journal of Agricultural, Biological and Environmental Statistics, 8:205-222, 2003.
  109. Zhou JL and Zhu HT. Robust estimation and design procedures for random effect model. Canadian Journal ofStatistics, 31:99-110, 2003.
  110. Zhu HT and Lee SY. Local influence for generalized linear mixed models. Canadian Journal of Statistics, 31:293-309, 2003.
  111. Zhu HT and Lee SY. Maximizing generalized linear mixed models via a stochastic approximation algorithm with Markov chain Monte Carlo method. Statistics and Computing, 12:175-183, 2002.
  112. Gu MG and Zhu HT. Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation. Journal of the Royal Statistical Society, Series B, 63:339-355, 2001.
  113. Zhu HT and Lee SY. Local influence for models with incomplete data. Journal of the Royal Statistical Society, Series B, 63:111-126, 2001.
  114. Zhu HT, Lee SY, Wei BC, and Zhou JL. Case-deletion measures for models with incomplete data. Biometrika, 88:727-737, 2001.
  115. Zhu HT. Relationship between two eigenmatrices of a (real) symmetric matrix-Solution. Econometric Theory, 16: 793-794, 2000.
  116. Cheung SH and Zhu HT.  Simultaneous one-sided pairwise comparisons in a two-way design.  Biometrical Journal, 40:613-625, 1998.
  117. Zhu HT and Wei BC. Some notes on preferred point alpha-geometry and alpha-divergence function. Statistics & Probability Letters, 33: 427-437, 1997.
  118. Zhu HT and Wei BC. Preferred point alpha-manifold and Amari’s alpha connections. Statistics & Probability Letters, 36:219-229, 1997.
  119. Wei BC and Zhu HT. Some second order asymptotic in exponential family nonlinear regression models (A geometric approach). Australian Journal of Statistics, 39:129-148, 1997.

Imaging Genetics

  1. Dengdeng Yu, Linbo Wang, Dehan Kong, and Hongtu Zhu. Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease. Journal of American Statistical Association. 117(540):1656-1668, 2022.
  2. Jiawen Chen, Weifang Liu, Tianyou Luo, Zhentao Yu, Minzhi Jiang, Jia Wen, Gaorav, P. Gupta, Paola Giusti, Hongtu Zhu, Yuchen Yang, Yun Li. A comprehensive comparison on cell type composition inference for spatial transcriptomics data. Briefings in Bioinformatics. 23(4):bbac245, 2022.
  3. Chen, T., Mandal, A., Zhu, H. and Liu, R. Imaging genetic based mediation analysis for human cognition. Frontier Neuroscience, Volume 16, p. 824069, 2022.
  4. Bingxin Zhao, Tengfei Li, Stephen,M. Smith, Di Xiong, Xifeng Wang, Yue Yang, Tianyou Luo, Ziliang Zhu, Yue Shan, Nana Matoba, Quan Sun, Yuchen Yang, MadE. Hauberg, Jaroslav Bendl, J.F. Fullard, Panagiotis Roussos, Weili Lin, Yun Li, Jason L. Stein, Hongtu Zhu. Common variants contribute to intrinsic human brain functional networks. Nature Genetics, Volume 54, Issue 4, pp. 508 – 517, 2022.
  5. B Zhao, F Zou, H Zhu. Cross‐trait prediction accuracy of summary statistics in genome‐wide association studies. Biometrics, 00, 1– 13, 2022.
  6. Zhao, Y., Li, T.F., and Zhu, H. Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes. Biostatistics, 23, 2022, 467–484.
  7. B. Zhao, T. Li, Y. Yang, X. Wang, T. Luo, Y.Shan, Z. Zhu, D. Xiong, M. E. Hauberg, J. Bendl, J.F. Fullard, P. Roussos, Y. Li, J. L. Stein, and H Zhu. Common genetic variation influencing human white matter microstructure. Science, vol 372, Issue 6548, eabf3736, 2021.
  8. B. Zhao, Y. Shan, Y. Yang, Z. Yu, T. Li, X. Wang, T. Luo, Z. Zhu, P. Sullivan, H. Zhao, Y. Li, and H. Zhu. Transcriptome-wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into gene-level pleiotropy with complex neuropsychiatric traits. Nature Communication, 12, 2878, 2021.
  9. Zhang, J., Xia, K., Ahn, M., Jha, S.C., Blanchett, R., Crowley, J.J., Szatkiewicz, J.P., Zou, F., Zhu, H., Styner, M., Gilmore, J.H., Knickmeyer, R.C. Genome-Wide Association Analysis of Neonatal White Matter Microstructure. Cerebral Cortex, 31, 933–948, 2021.
  10. B. Zhao, J. Zhang, J.G. Ibrahim, Y.Li, R.Knickmeyer, T. Li, Y. Shan, Z. Zhu, F. Zhou, H. Liao, T.Nichols, and Zhu, H. Large-scale neuroimaging and genetic study reveals genetic architecture of brain white matter microstructure. Molecular Psychiatry, 26:3943-3955, 2020.
  11. Zhou, F., Zhou, H.B., Li, T., and Zhu, H. Analysis of Secondary Phenotypes in Multi-group Association Studies. Biometrics, 76, 606–618, 2020.
  12. Benjamin, R. and Zhu, H.T. ACE of Space: Estimating Genetic Components of High-Dimensional Imaging Data. Biostatistics, 22(1), 131–147, 2020.
  13. B. Zhao, G. Ibrahim, Y. Li, T. LiY. WangY. ShanZ. ZhuF. ZhouJ. ZhangC. HuangH. LiaoL. Yang, Paul M. Thompson, H. Zhu. Heritability of regional brain volumes in large-scale neuroimaging and genetic studies. Cerebra Cortex, 29, 2904-2914, 2019.
  14. B. Zhao, T. Luo, T. Li, Yun Li, J. Zhang, Y. Shan, X. Wang, L. Yang, F. Zhou, Z. Zhu, Hongtu Zhu. GWAS of 19,629 individuals identifies novel genetic variants for regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Featured on the cover of Nature Genetics, 51, 1637-1644, 2019.
  15. X. Bi, L. Feng, S. Wang, Z. Lin, T. Li, B. Zhao, H. Zhu, H. Zhang. Common Genetic Variants Have Associations with Human Cortical Brain Regions and Risk of Schizophrenia. Genetic Epidemiology, 43, 548-558, 2019.
  16. Y. ZhaoH. Zhu, Z. Lu, Rebecca Knickmeyer, and Fei Zou. Bayesian Hierarchical Variable Selection for Structured Genome-wide Association Studies. Genetics, 212, 397-415, 2019.
  17. J. Lee, J.W. Zhang, M. C. Neale, M. Styner, H. Zhu, J.H. Gilmore. Quantitative tract-based white matter heritability in 1- and 2-year-old twins. Human Brain Mapping, 40, 1164-1173, 2019.
  18. F. Nathoo, L. Kong, and H. Zhu. A Review of Statistical Methods in Imaging Genetics. The Canadian Journal of Statistics, 47, 108-131, 2019.
  19. Benjamin, R. and Zhu, H.T. Note on bias from averaging repeated measurements in heritability studies. Proceeding of National Academy of Science, USA. 115, E122, 2018.
  20. Huang, C., Thompson, P., Wang, Y., Yu, Y., Zhang, J., Kong, D., Colen, R., Knickmeyer, R., Zhu, H. T. FGWAS: Functional genome wide association analysis. NeuroImage, 159, 107-121, 2017.
  21. Z. Lu, Z. Khondker, Joseph G Ibrahim, Y. Wang, and H. Zhu. Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies. NeuroImage, 149:305-322, 2017.
  22. R. Knickmeyer, K. Xia, J. Zhang, M. Ahn, S. Jha, J. Crowley, J. Szatkiewicz, T. Li, Fei Zou, H. Zhu, D. Hibar, P. Thompson, P. Sullivan, M. Styner, and J. Gilmore. Genome-Wide Association Analysis Identifies Common Variants Influencing Infant Brain Volumes. Translational Psychiatry, 7, e1188, 2017.
  23. X. Bi, L. Yang, T. Li, H. Zhu, and H. Zhang. Genome-Wide Mediation Analysis of Psychiatric and Cognitive Traits through Imaging Phenotypes. Human Brain Mapping, 38:4088-4097, 2017.
  24. E Lee, KS Giovanello, AJ Saykin, F Xie, D Kong, Y Wang, L Yang, Murali Doraiswamy, JG Ibrahim, H Zhu. SNPs are associated with cognitive decline at AD conversion within MCI patients. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 8, 86-95, 2017.
  25. W Zhu, Y Yuan,  J Zhang,  F Zhou,  RC Knickmeyer, and H Zhu.  Genome-wide Association Analysis of Secondary Imaging Phenotypes from the Alzheimer’s Disease Neuroimaging Initiative Study. NeuroImage, 146:983-1002, 2016.
  26. Allen, G. I., …, Zhu, H., Zhu, S. and ADNI. Crowd sourced estimation of cognitive decline and resilience in Alzheimer’s disease. Alzheimer’s & Dementia. 12:645-653, 2016.
  27. Lu, Z. H., Zhu, H.T., C. Knickmeyer, P.F. Sullivan, W.N. Stephanie, and Fei Zou, Multiple SNP-sets Analysis for Genome-wide  Association Studies through Bayesian Latent Variable Selection. Genetic Epidemiology, 39, 664-677, 2015.
  28. Kong, K. S. Giovanello, Y.L. Wang, W. Lin, E. Lee, Y. Fan, P. M. Doraiswamy,  and Hongtu Zhu, ADNI (2015).   Predicting Alzheimer’s disease using combined imaging-whole genome SNP data.  Journal of Alzheimer’s Disease, 46: 695-702.
  29. Huang, T.Nichols, C.Huang, Y.Yang, Z. Lu, Q. Feng, R.C. Knickmeyer, H. Zhu, and for ADNI. (2015).FVGWAS: Fast Voxelwise Genome Wide Association Analysis of Large-scale Imaging Genetic Data. NeuroImage, 118, 613-627. Winner of Best Paper award in ASA SI Session, 2015.
  30. Lin, J, Zhu, H.T., Ahn, M., Sun, W, and Ibrahim, J. G. Functional mixed effects models for imaging genetic data. Genetic Epidemiology, 38, 680-691, 2014.
  31. Kai Xia, Yang Yu, Mihye Ahn, Hongtu Zhu, Fei Zou, John Gilmore, Rebecca Christine Knickmeyer. Environmental and genetic contributors to salivary testosterone levels in infants. Frontiers in Endocrinology. 2014.
  32. Zhu, H.T., Khondker, Z. S., Lu, Z.H., and Ibrahim, J. G. Bayesian generalized low rank regression models for neuroimaging phenotypes and genetic markers. Journal of American Statistical Association, 109, 1084-1098, 2014.
  33. Lin, J., Zhu, H.T., Knickmeyer, R., Styner, M., Gilmore, J. and Ibrahim, J.G. Projection Regression Models for Multivariate Imaging Phenotype. Genetic Epidemiology, 36, 631-641, 2012.
  34. Zhu HT, Yu CY, and Zhang HP. Tree-based disease classification for the protein data. Proteomics, 3:1673-1677, 2003.


AI for Two-Sided Markets

  1. C Shi , Jin Zhu, Ye Shen, Shikai Luo, Hongtu Zhu, and Rui Song. Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. Journal of American Statistical Association, 2022, Volume ahead-of-print, Issue ahead-of-print, pp. 1 – 12.
  2. Qin, Z., Zhu, H.T., and Jieping Ye. Reinforcement learning for ridesharing: an extended survey. Transportation Research Part C: Emerging Technologies, 2022, 144, p. 103852.
  3. C Shi, Luo S., Y Le, H Zhu, and R Song. Statistically efficient advantage learning for offline reinforcement learning in infinite horizons. Journal of the American Statistical Association, 2022, Volume ahead-of-print, Issue ahead-of-print, pp. 1 – 14.
  4. Shi, C., Wan, R., Song, G., Luo, S., Zhu, H. and Song, R. (2022+). A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets, Annals of Applied Statistics, accepted.
  5. Shi, C., Luo, S.K., Zhu, H., and Song, R. An Online Sequential Test for Qualitative Treatment Effects. Journal of Machine Learning Research, 2021, 22, 13061-13111.
  6. Shi, C., Luo, S.K., Zhu, H., Jieping Ye, and Song, R. Time Dependent Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework. Journal of American Statistical Association, 2022, Volume ahead-of-print, Issue ahead-of-print, pp. 1 – 13.
  7. Fan, Z., Luo, S., Qie, X., Ye, J., and Zhu, H. Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms. Journal of American Statistical Association, 2021, 116 (536), 1688-1699.
  8. Xu, Z., Yin, Y., Chao, X., Zhu, H., & Ye, J. “A generalized fluid model of ride-hailing systems”. Transportation Research Part B, 150, 2021, 587-605.
  9. X Qin, H Yang, Y Wu, H Zhu. Multi-party ride-matching problem in the ride-hailing market with bundled option services. Transportation Research Part C: Emerging Technologies 131, 103287, 2021
  10. Qin,Z., Tang, X., Jiao, Y., Zhang, F., Xu, Z., Zhu, H., Ye, J. Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning. Informs Journal on Applied Analytics. 50. 272-285. 2020.
  11. Z Shou, X Di, J Ye, H Zhu, H Zhang, R Hampshire. Optimal passenger-seeking policies on E-hailing platforms using Markov decision process and imitation learning. Transportation Research Part C: Emerging Technologies. 111, 91-113, 2020.
  12. Xiaoran Qin, Kaixian Yu, Hanqian Li, Feng Dai, Haijiang Liu, Hai Yang, Jieping Ye, Hongtu Zhu. Development of a one-day driving cycle for electric ride-hailing vehicles. Transportation Research Part D: Transport and Environment. in press. 2020.

Journals for Cancer Genetics/Genomics

  1. Shaolong Cao, Jennifer R. Wang, Shuangxi Ji, Peng Yang, Yaoyi Dai, Shuai Guo, Matthew D. Montierth, John Paul Shen, Xiao Zhao, Jingxiao Chen, Jaewon James Lee, Paola A Guerrero, Nicholas Spetsieris, Nikolai Engedal, Sinja Taavitsainen, Kaixian Yu, Julie Livingstone, Vinayak Bhandari, Shawna M Hubert, Najat C. Daw, P. Andrew Futreal, Eleni Efstathiou, Bora Lim, Andrea Viale, Jianjun Zhang, Matti Nykter, Bogdan A Czerniak, Powel H. Brown, Charles Swanton, Pavlos Msaouel, Anirban Maitra, Scott Kopetz, Peter Campbell, Terence P. Speed, Paul C. Boutros, Hongtu Zhu, Alfonso Urbanucci, Jonas Demeulemeester, Peter Van Loo and Wenyi Wang. Estimating tumor cell total mRNA expression reveals the molecular and clinical phenotype of cancer. Nature BioTechnology, 40, 1624–1633, 2022.
  2. S.C. Dentro, I. Leshchiner, K.Haase, M. Tarabichi, J. Wintersinger, A.G. Deshwar, K. Yu, Y. Rubanova, G. Macintyre, J. Demeulemeester, I. Vázquez-García, K. Kleinheinz, D. G. Livitz, S. Malikic, N. Donmez, S. Sengupta, P. Anur, C. Jolly, M. Cmero, D. Rosebrock, S.E. Schumacher, Y. Fan, M. Fittall, R.M. Drews, X. Yao, T. B. K. Watkins, J. Lee, M. Schlesner, H. Zhu, D.J. Adams, N. McGranahan, C. Swanton, G.Getz, P.C. Boutros, M. Imielinski, R. Beroukhim, S. C. Sahinalp, Y. Ji, M. Peifer, I. Martincorena, F. Markowetz, V. Mustonen, K. Yuan, M. Gerstung, P. T. Spellman, W. Wang, Q. D. Morris, D.C. Wedge, P. Van Loo, on behalf of the PCAWG Evolution and Heterogeneity Working Group and the PCAWG consortium. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239–2254.e39, 2021.
  3. Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Mitchell TJ, Rubanova Y, Anur P, Rosebrock D, Yu K, Tarabichi M, Deshwar A, Wintersinger J, Kleinheinz K, Vazquez-Garcia I, Haase K, Sengupta S, Macintyre G, Malikic S, Donmez N, Livitz DG, Cmero M, Demeulemeester J, Schumacher S, Fan Y, Yao X, Lee J, Schlesner M, Boutros PC, Bowtell DD, Zhu H, Getz G, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Markowetz F, Mustonen V, Yuan K, Wang W, Morris QD, Spellman PT, Wedge DC, Van Loo P for PCAWG Evolution and Heterogeneity Working Group, and PCAWG network. The evolutionary history of 2,658 cancers. Nature, 578(7793):122-128, 2020.
  4. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature, 578, 82-93, 2020.
  5. Rongjie Liu, Hesham Elhalawani, Abdallah Sherif Radwan Mohamed, Baher Elgohari, Laurence Court, Hongtu Zhu, Clifton David Fuller. Stability analysis of CT radiomic features with respect to segmentation variation in oropharyngeal cancer. Clinical and translational radiation oncology, 21, 11-18.
  6. Jian Wang, Rongjie Liu, Yu Zhao, Chonnipa Nantavithya, Hesham Elhalawani, Hongtu Zhu, Abdallah Sherif Radwan Mohamed, Clifton David Fuller, Danita Kannarunimit, Pei Yang, Hong Zhu. A predictive model of radiation-related fibrosis based on the radiomic features of magnetic resonance imaging and computed tomography. Translational Cancer Research, 9, 4726-4738, 2020.
  7. Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Mitchell TJ, Rubanova Y, Anur P, Rosebrock D, Yu K , Tarabichi M, Deshwar A, Wintersinger J, Kleinheinz K, Vazquez-Garcia I, Haase K, Sengupta S, Macintyre G, Malikic S, Donmez N, Livitz DG, Cmero M, Demeulemeester J, Schumacher S, Fan Y, Yao X, Lee J, Schlesner M, Boutros PC, Bowtell DD, Zhu H, Getz G, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Markowetz F, Mustonen V, Yuan K, Wang W, Morris QD, Spellman PT, Wedge DC, Van Loo P for PCAWG Evolution and Heterogeneity Working Group, and PCAWG network. The evolutionary history of 2,658 cancers. Nature, 578(7793):122-128, 2020.
  8. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature, 578, 82-93, 2020.
  9. Li Wang, Liuqing Yang,Shichao Han, Jinming Zhu, Yuting Li, Zeming Wang, You Hong Fan, Eric Lin, Ruiping Zhang, Narayan Sahoo, Yupeng Li, Michael Gillin, Xiaodong Zhang, Xiaochun Wang, Tengfei Li, Xiaorong Ronald Zhu, Hongtu Zhu, John V. Heymach, Jeffrey N. Myers, Steven J. Frank. Patterns of protein expression in human head and neck cancer cell lines differ after proton versus photon radiotherapy. Head & Neck, 42, 289-301, 2020.
  10. Cmero, ,…, …, Macintyre, G. and PCAWG Consortium (including Zhu, H). Inferring structural variant cancer cell fraction. Nature Communication, 11, 730, 2020.
  11. Salcedo, A. …, Boutros, P. C. and PCAWG Consortium (including Zhu, H.). A community effort to create standards for evaluating tumor subclonal reconstruction. Nature Biotechnology 38(1):97-107, 2020.
  12. Rubanova, Y.,…, Morris, Q. and PCAWG Consortium (including Zhu, H.). Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. Nature Communications 11(1):731, 2020.
  13. Kelly Flentie, Caleb Gonzalez, Brandon Kocher, Yue Wang, Hongtu Zhu, Jayne Marasa, and David Piwnica-Worms. Nucleoside Diphosphate Kinase-3 (NME3) Enhances TLR5-Induced NF-κB Activation. Molecular Cancer Research. 2018, 16, 986-999, 2018.
  14. Dentro SC, Leshchiner I, Haase K, Wintersinger J, Yu K, Tarabichi M, Deshwar A, Rubanova Y, Vzquez-Garcia I, Kleinheinz K, Livitz DG, Macintyre J, Malikic S, Donmez N, Sengupta S, Demeulemeester J, Anur P, Jolly C, Cmero M, Rosebrock D, Schumacher S, Fan Y, Fittall M, Yousif F, Yao X, Lee J, Schlesner, M, Zhu H, Getz G, Boutros P, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Markowetz F, Peifer M, Martincorena I, Mustonen V, Yuan K, Gerstung M, Wang W, Morris Q, Spellman PT, Wedge DC, Van Loo P, for the PCAWG Evolution and Heterogeneity Working Group26 and the PCAWG network. Pervasive intra-tumour heterogeneity and subclonal selection across cancer types, Submitted to Nature.
  15. Yu K , Tarabichi M, Deshwar A, Dentro S, Leshchiner I,Wedge D, Li J, PCAWG11 Evolution and HeterogeneityWorking Group, Zhu H, Morris Q, Van Loo P,Wang, W. Consensus methods for the robust reconstruction of subclonal architecture from whole-genome sequencing data. Submitted.
  16. Yu K, Shin SJ, Zhu H, Wang W, CliP: fast subclonal architecture reconstruction from whole-genome sequencing data. Submitted.


Quantitative Psychology Journals

  1. Kang, K., Cai, J., Song, X. and Zhu, H. Bayesian Hidden Markov Models for Delineating the Pathology of Alzheimer’s Disease. Statistical Methods in Medical Research. 28, 2112-2124, 2019.
  2. Tang, N.S., Chow, S. M., Ibrahim, J.G., H. Zhu. Bayesian Sensitivity Analysis of Dynamic Factor Analysis Models with Nonparametric Prior and Possible Nonignorable Missingness, Psychometrika, 82,  875–903, 2017.
  3. A. Maisto, F.C.Xie, K.Witkiewitz, C.K.Ewart, G.J. Connors, H. Zhu, G.Elder, M.Ditmar, S.M. Chow. How chronic self-regulatory stress, poor anger regulation, and momentary affect undermine treatment for alcohol use disorder: integrating social action theory with the dynamic model of relapse. Journal of Social and Clinical Psychology, 36,  238-263, 2017.
  4. Chow, S. M., Lu, Z.H., Sherwood, Zhu, HT. Fitting nonlinear ordinary differential equation models with random effects using the stochastic approximation expectation-maximization (SAEM) algorithm.  Psychometrika,81, 102-134, 2016.
  5. Lu, Z.H., Chow, S. M., Sherwood, A. and Zhu, H.T. Bayesian analysis of nonlinear latent stochastic differential equations with application to human dynamics. Annals of Applied Statistics, 9, 1601-1620, 2015.
  6. Chow, SM., Tang, NS, Yuan, Y., Song, X.Y., and Zhu HT. Semiparametric nonlinear dynamic latent variable models. British Journal of Mathematical and Statistical Psychology, 64, 69–10, 2011.
  7. Chen, F, Zhu HT, and Lee SY. Perturbation selection and local influence analysis for nonlinear structural equation model.  Psychometrika, 493-516, 2009.
  8. Lee SY and Zhu HT. Maximum likelihood estimation of nonlinear structural equation models. Psychometrika, 67:189-210, 2002.
  9. Zhu HT and Lee SY. Bayesian analysis of finite mixtures in the LISREL model. Psychometrika, 66:133-152, 2001.
  10. Song XY, Lee SY, and Zhu HT. Model selection in structural equation models with continuous and polytomous data.    Structural Equation Modeling,  8:378-396, 2001.
  11. Lee SY and Zhu HT. Statistical Analysis of nonlinear structural equation models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology, 53:209-232, 2000.
  12. Zhu HT and Lee SY. Statistical analysis of nonlinear factor analysis models. British Journal of Mathematical and Statistical Psychology, 52:225-242, 1999.


Medical Imaging/Neuroscience Journals

(NeuroImage, IEEE Transactions on Medical Imaging, and Human Brain Mapping are the very best neuroimaging and medical imaging journals; Cerebral Cortex, Nature Neuroscience, Journal of Neuroscience, and PNAS are the very best neuroscience journals.)

  1. Hongtu Zhu, Tengfei Li, and Bingxin Zhao. Statistical learning methods in neuroimaging data analysis with applications. Annual Review of Biomedical Data Science, Volume 6, Issue 1, 2023.
  2. Liangjun Chen, Ya Wang, Zhengwang Wu, Yue Shan, Tengfei Li, Sheng-Che Hung, Lei Xing, Hongtu Zhu, Li Wang, Weili Lin, Gang Li. Four-dimensional mapping of dynamic longitudinal brain subcortical development and early learning functions in infants. Nature Communication, 2023.
  3. Wang, X., Rao, S., Cheng, J., Wu, Y., Yap, P.T., Ibrahim, J. G., and Zhu, H. Spatially Robust Adaptive Ensemble Average Propagator Reconstruction via Spherical Polar Fourier Imaging. Statistics and its Interface, in press, 2023.
  4. Ya Wang, Liangjun Chen, Zhengwang Wu, Tengfei Li, Yue Sun, Jiale Cheng, Hongtu Zhu, Weili Lin, Li Wang, Wenhua Huang, Gang Li. Longitudinal development of the cerebellum in human infants during the first 800 days. Cell Reports, 2023, Volume 42, Issue 4, p. 112281.
  5. Spann, Marisa N., Jessica L. Wisnowski, Banu Ahtam, Wei Gao, Hao Huang, Mary Beth Nebel, Elizabeth S. Norton et al. including Zhu, H. The art, science, and secrets of scanning young children. Biological Psychiatry, Volume 93, Issue 10, pp. 858 – 860, 2022.
  6. C. Huang, Zhenlin Xu, Zhengyang Shen, Tianyou Luo, Tengfei Li, Daniel Nissman, Amanda Nelson, Yvonne Golightly, Marc Niethammer, Hongtu Zhu. Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative. Medical Image Analysis, Volume 77, p. 102343, 2022.
  7. Yin, W., Li, T., Mucha, P.J., Cohen, J. R., Zhu, H., Zhu, Z. and Lin, W. Altered neural flexibility in children with attention-deficit/hyperactivity disorder. Molecular Psychiatry, Volume 27, Issue 11, pp. 4673 – 4679, 2022.
  8. Li, T., Samuel, T. M., Zhu, Z., Howell, B., Cho, S., Baluyot, K., … Zhu, H. & Lin, W.. Joint analyses of human milk fatty acids, phospholipids, and choline in association with cognition and temperament traits during the first 6 months of life. Frontier in Nutrition, Volume 9, p. 919769, 2022.
  9. W.Yin, T. Li, SC. Hung, H. Zhang, L. Wang, D. Shen, H. Zhu,  P. J. Mucha, J. R. Cohen, Weili Lin. The Emergence of a Functionally Flexible Brain During Early Infancy. PNAS, 117, 38, 23904 – 23913, 2020.
  10. Jonas Bianchi, Antônio Carlos de Oliveira Ruellas, João Roberto Gonçalves, Beatriz Paniagua, Juan Carlos Prieto, Martin Styner, Tengfei Li, Hongtu Zhu, James Sugai, William Giannobile, Erika Benavides, Fabiana Soki, Marilia Yatabe, Lawrence Ashman, David Walker, Reza Soroushmehr, Kayvan Najarian, Lucia Helena Soares Cevidanes. Osteoarthritis of the temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning. Scientific Reports, Volume 10, Issue 1, p. 8012, 2020.
  11. Liming Zhong; Tengfei Li; Hai Shu; Chao Huang; Jason Michael Johnson; Ho-Ling Liu; Donald F. Schomer; Qianjin Feng; Wei Yang, Zhu, H. (TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients. NeuroImage, Volume 223, p. 117368, 2020.
  12. Zhengwu Zhang,Genevera Allen; Hongtu Zhu; David Dunson, Tensor network factorizations:  Relationships between brain structural connectomes and trait. NeuroImage, 197, 330-343, 2019.
  13. W. Wang, Y.C. Lee, E.Calista, F. Zhou, H. Zhu, R.Suzuki, D. Komura, S.Ishikawa, S.P. Cheng (2018). A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays. Bioinformatics, 34, 1767-1773.
  14. B.R. Howell, Ahn, Y. Shi, J.R.  Godfrey,  X. Hu, H. Zhu,  M.A Styner,  M.M. Sanchez, Disentangling the Effects of Early Caregiving Experience and Heritable Factors on Brain White Matter Development in Rhesus Monkeys. NeuroImage, 197, 625-642, 2019.
  15. Kurt G Schilling, Vishwesh Nath, Colin Hansen, Prasanna Parvathaneni, Justin Blaber, Yurui Gao, Peter Neher, Dogu Baran Aydogan, Yonggang Shi, Mario Ocampo-Pineda, Simona Schiavi, Alessandro Daducci , Gabriel Girard, Muhamed Barakovic, Jonathan Rafael-Patino, David Romascano, Gaëtan Rensonnet, Marco Pizzolato, Alice Bates, Elda Fischi, Jean-Philippe Thiran, Erick J. Canales-Rodríguez, Chao Huang, Hongtu Zhu, Liming Zhong, Ryan Cabeen, Arthur W Toga, Francois Rheault, Guillaume Theaud, Jean-Christophe Houde, Jasmeen Sidhu, Maxime Chamberland, Carl-Fredrik Westin, Tim B. Dyrby, Ragini Verma, Yogesh Rathi, M Okan Irfanoglu, Cibu Thomas, Carlo Pierpaoli, Maxime Descoteaux, Adam W Anderson, Bennett A Landman. “Limits to the anatomical accuracy of diffusion tractography using modern approaches”. NeuroImage, 185, 1-11, 2018.
  16. Brittany R. Howell, Martin A. Styner, Wei Gao, Pew-Thian Yap, Li Wang, Kristine Baluyot, Essa Yacoub, Geng Chen, Taylor Potts, Andrew Salzwedel, Gang Li, John H. Gilmore, Joseph Piven, J. Keith Smith, Dinggang Shen, Kamil Ugurbil, Hongtu Zhu, Weili Lin, Jed T. Elison (2018). The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. NeuroImage, 185, 891-905, 2018.
  17. Y. Liu, Y. Liu, and H. Zhu. (2018). SMAC: Spatial Multi-category Angle-based Classifier for High-dimensional Neuroimaging Data. NeuroImage, 172, 130-145.
  18. Zhang, M.  Descoteaux, J.  Zhang, G.  Girard, M.  Chamberland, D. Dunson, A.  Srivastava, and H. Zhu. (2018).  Mapping Population based Structural  Connectomes. NeuroImage, 172, 130-145.
  19. Guinney, J. et al. including Zhu, H.”Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.” The Lancet Oncology, 18, 946-961, 2017.
  20. Yu, Y. Zhang, Y. Yu, C. Huang, R. Liu, T.Li, L.Yang, J.S. Morris, V.Baladandayuthapania, and H.Zhu (2017). Radiomic analysis in prediction of Human Papilloma Virus status. Journal: Clinical and Translational Radiation Oncology. 7, 49-54.
  21. A. Broadwatera, S.H. Lee, Y. Yu, H. Zhu, F.T. Crews, D.L. Robinsona, and Y.Y. I. Shih。Adolescent alcohol exposure decreases frontostriatal resting state functional connectivity in adulthood. Addition Biology, 23,810-823, 2018.
  22. Tomasz, J. H., M. Caughey, Y.Wang, H. Zhu, B. Huang, E. Lee, C.Zamora, M.Farber, J.Fulton, P.Ford, W.Marston, R. Vallabhaneni, Ti. Nichols, and C. Gallippi. (2017). Performance of acoustic radiation force impulse ultrasound imaging for carotid plaque characterization with histological validation. Journal of Vascular Surgery, 66, 1749-1757.
  23. Smith, I. T., Townsend, L.B., Huh, R. Zhu, and Smith, S. L. (2017) Stream-dependent development of higher visual cortical areas. Nature Neuroscience, 20,  200–208.
  24. C. Hazlett, H. Gu, B.C. Munsell, S.H.Kim, M.Styner, J.J. Wolff, J.T. Elison, M.R. Swanson, H. Zhu, K.N. Botteron, L. Collins, J.N. Constantino, S.R. Dager, A.M. Estes, A.C. Evans, V. Fonov, G.Gerig, P. Kostopoulos, R.C. McKinstry, J. Pandey,   S. Paterson, J. R. Pruett, Jr., R.T. Schultz, D.W. Shaw, L. Zwaigenbaum, and J. Piven, for the IBIS Network. Early brain development in infants at high risk for autism spectrum disorder. Nature, 542, 348-351, 2017.
  25. Jin, Yan; Huang, Chao; Daianu, Madelaine; Zhan, Liang; Dennis, Emily; Reid, Robert; Jack, Clifford; Zhu, Hongtu; Thompson, Paul. 3-D Tract-Specific Local and Global Analysis of White Matter Integrity in Alzheimer’s Disease. Human Brain Mapping, 38:1191-1207, 2017.
  26. Rebecca Knickmeyer, Kai Xia, Zhaohua Lu, Mihye Ahn, Shaili Jha, Fei Zou, Hongtu Zhu, Martin Styner, John Gilmore. Impact of Demographic and Obstetric Factors on Infant Brain Volumes: A Population Neuroscience Study. Cerebra Cortex, 27,5616-5625,
  27. W. Hyun, Y. Li, C. Huang, M. Styner, W. Lin, and H. Zhu. STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data. NeuroImage, 134, 550-562, 2017.
  28. C. Jha, S. Meltzer-Brody, R. J. Steiner, E. Cornea, S. Woolson, M. Ahn, A. R. Verde, R. M. Hamer, H. Zhu, M. Styner, J. H. Gilmore, R. C. Knickmeyer. Antenatal Depression, Treatment with Selective Serotonin Reuptake Inhibitors, and Neonatal Brain Structure: A Propensity-Matched Cohort Study. Psychiatry Research: Neuroimaging, 253:43-53, 2016.
  29. N. Kornegay, D.J. Bogan,  J.R. Bogan,  J.L. Dow,  J. Wang, Z. Fan, N. Liu, L, Warsing, R. W. Grange, M. Ahn, C. J.Balog-Alvarez, S. W. Cotten,  M.S. Willis,  C. Brinkmeyer-Langford,  H. Zhu, J. Palandra, C. A. Morris,  M.A. Styner,  K. R. Wagner.  Dystrophin-Deficient Dogs with Reduced Myostatin have Unequal Muscle Growth and Greater Joint Contractures. Skeletal Muscle, 6, 14, 2016.
  30. J. Lee, R.J. Steiner,  Y. Yu,  M.C. Neale,  M. Styner,  H. Zhu, and J.H. Gilmore. Common and heritable components of white matter microstructure predict cognitive function from birth to 2 years. Proceedings of the National Academic Sciences, USA,  114, 148-153, 2017.
  31. Geng, X., Li, G., Lu, Z., Wang, L., D. Shen, Zhu, H. and J. H. Gilmore. Structural and Maturational Covariance in Early Childhood Brain Development. Cerebra Cortex, 27, 1795–1807, 2017.
  32. Gang Li, Li Wang, Feng Shi, Amanda E. Lyall, Mihye Ahn, Ziwen Peng, Zhu, Weili Lin, John H. Gilmore, Dinggang Shen. Cortical Thickness and Surface Area in Neonates at High Risk for Schizophrenia, Brain Structure and Function, 221, 447-461, 2016.
  33. Huang, C., Liang, S., Niethammer, M., Zhu, H.T. (2015). Disease Region Detection of Longitudinal Knee MRI data. IEEE Transaction on Medical Imaging, 34, 1914-1927. Winner of Best Paper award in ASA SI Session, 2014.
  34. An H, Ford AL, Chen Y, Zhu H, Ponisio R, Kumar G, Modir-Shanechi A, Khoury N, Vo KD, Williams JA, Derdeyn CP, Diringer MN, Panagos P, Powers WJ, Lee JM, Lin W. Defining the Ischemic Penumbra using Magnetic Resonance Oxygen Metabolic Index. Stroke, 46, 982-988, 2015.
  35. J. Lee, R. J. Steiner, S. Luo, M. C. Neale, M. Styner, H. Zhu, J.H. Gilmore. (2015). Quantitative tract-based white matter heritability in twin neonates. NeuroImage, 111, 123–135.
  36. N Kornegay, J.M. Peterson, D.J. Bogan, W.Kline, J.R Bogan, J.L.Dow Z.Fan, J.Wang, Mihye Ahn, H. Zhu, M. Styner and D.C. Guttridge. NBD delivery improves the disease phenotype of the golden retriever model of Duchenne muscular dystrophy. Skeletal Muscle. 4:18, 2014.
  37. Lucile Bompard, Shun Xu, 
Martin Styner, 

Beatriz Paniagua, 
Mihye Ahn, Ying Yuan, Valerie Jewells, Wei Gao, Dinggang Shen, Hongtu Zhu, Weili Lin. Multivariate Longitudinal Shape Analysis of Human Lateral Ventricles during the First Twenty-Four Months of Life. PLOS ONE, 2014.
  38. Wei Gao, Amanda Elton, Hongtu Zhu, Sarael Alcauter, J. Smith, John H Gilmore, and Weili Lin. Inter-subject Variability of and Genetic Effects on the Brain’s Functional Connectivity during Infancy. Journal of Neuroscience,34: 11288-11296, 2014.
  39. Cevidanes LH, Walker D, Schilling J,  Sugai J, Giannobile WV,Paniagua B, Benavides E, Zhu H, Marron JS, Jung B,Baranowski D, Rhodes J, Ludlow JB, Nackley A, Lim PF,Nguyen T, Goncalves J, Wolford L, Kapila S,  Styner M. 3D Osteoarthritic Changes in TMJ Condylar Morphology Correlates with Specific Systemic and Local Biomarkers of Disease.  Osteoarthritis and Cartilage,  221657-67, 2014.
  40. W. Hyun, Li, Y. M., Gilmore, J., Lu, Z.H., Styner, M., and Zhu, H.T. SGPP:  Spatial Gaussian Predictive Process Models for   Neuroimaging Data. NeuroImage, 89, 70–80, 2014.
  41. Tao, X. Zhang, M. Chopra, M.J. Kim, K. R. Buch, D. Kong, J. Jin, Y. Tang, H. Zhu, Valerie Jewells, and Silva Markovic-Plese. The Role of Endogenous IFNβ in the Regulation of Th17 Responses in Patients with Relapsing-Remitting Multiple Sclerosis, Journal of Immunology, 192:5610-5617, 2014.
  42. Chen, Y. S., Zhu, H. T., An, H. Y., and Weili Lin. More insights into early brain development through statistical analyses of eigen-structural elements of diffusion tensor imaging using multivariate adaptive regression splines. Brain Structure and Function, 219(2):551-69, 2014.
  43. Fan, Z., Wang, J. H., Ahn, M., Shiloh-Malawsky, Y., Chahin, N., Elmore, S., Bagnell, R., Wilber, K., An, H. Y., Lin, W., Zhu, H.T., Styner, M., Kornegay, J. N. Characteristics of MRI biomarkers in a natural History study of golden retriever muscular dystrophy. Neuromuscular Disorder, 24(2):178-91, 2014.
  44. Ford AL, An H, Kong L, Zhu H, Vo KD, Powers WJ, Lin W, and Lee JM.  Clinically-relevant reperfusion in acute ischemic stroke:  MTT performs better than Tmax and TTP.  Translational Stroke Research, 5, 415-421, 2014.
  45. C. Knickmeyer, J.P. Wang, H.T. Zhu, , X. Geng, S. Woolson, R.M. Hamer, T.Konneker, M.Styner, and J. H. Gilmore, M.D. Impact of Sex and Gonadal Steroids on Neonatal Brain Structure. Cerebra Cortex, 24:2721-31, 2014.
  46. Knickmeyer, R. C., Wang, J. P., Zhu, H.T., Geng, X., Woolson, S., Hamer, R. M., Konneker, T., Lin, W. L., Styner, M., and Gilmore, J. H. Common variants in psychiatric risk genes predict brain structure at birth. Cerebra Cortex. 24(5):1230-46, 2014.
  47. Nguyen, T., Cevidanes, L., Paniagua, B., Zhu, H.T., de Paula, L. K., and De Clerck, H. Use of shape correspondence analysis to quantify skeletal changes associated with bone-anchored class III correction. The Angle Orthodontist, 84(2):329-36, 2014.
  48. R. Verde, F. Budin, J.B. Berger, A. Gupta, M.Farzinfar, A. Kaiser, M. Ahn, H.J Johnson, J. Matsui, H.C. Hazlett, A. Sharma, C. Goodlett, Y. Shi, S. Gouttard, C. Vachet, J. Piven, H. Zhu, G. Gerig, M. A.Styner. UNC-Utah NA-MIC Framework for DTI Fiber Tract Analysis, Frontiers in Neuroinformatics, 7:51,2013.
  49. Yuan, Y., Gilmore, J., Geng, X. J., Styner, M., Chen, K. H., Wang, J. L., and Zhu, H.T. A longitudinal functional analysis framework for analysis of white matter tract statistics. NeuroImage, 23:220-31,
  50. Zhang, X., Tao, Y., Chopra, M, Marcus, K. L., Choudhary, N., Ahn, M., Zhu, H.T. and Markovic-Plese, S. Differential Reconstitution of T-Cell Subsets Following Immunodepleting Treatment with Alemtuzumab (αCD52 Mab) in Patients with Relapsing-remitting Multiple Sclerosis. Journal of Immunology, 191:5867-5874,
  51. Nguyen, T., Cevidanes, L., Paniagua, B., Zhu, H., de Paula, L. K., and De Clerck, H. Use of shape correspondence analysis to quantify skeletal changes associated with bone-anchored class III correction. The Angle Orthodontist, 84:329-36, 2013.
  52. Nguyen, T., Cevidanes, L., Zhu, H., LeCornu, M. and Larson, B. Three-dimensional treatment outcomes in class II patients treated using Herbst: pilot study. American Journal of Orthodontics & Dentofacial Orthopedics, 144(6):818-30, 2013.
  53. Bryant, C., Giovanello, K. S., Ibrahim, J. G., Shen, D. G., Peterson, B. S., and Zhu, H.T. Mapping the heritability of regional brain volumes explained by all common SNPs from the ADNI study. PLOS ONE, in press, 2013.
  54. B, Russell., C, Tomasz, Wu, C.D, N, Timothy, Zhu, H.T., Jonathon, H., Elizabeth, M. and Gallippi, C. Acoustic Radiation Force Beam Sequence Performance for Detection and Material Characterization of Atherosclerotic Plaques Part II: Preclinical, Ex Vivo Results. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 60, 2471-2487, 2013.
  55. Li, YM, Gilmore, J.H., Styner, M., Lin, W. L., Shen, D. G., and Zhu, HT. Spatial adaptive generalized estimating equations for longitudinal neuroimaging data. Neuroimage, 72, 91-105,
  56. Gao, J. H Gilmore, D. Shen, J. K. Smith, Zhu HT, and Lin, W. The synchronization within and interaction between the default and dorsal attention networks in early infancy. Cerebra Cortex. 23: 594-603, 2013.
  57. Shi, Y., Short, S.J., Knickmeyer, R. C., Wang, J. P., Coe, C. L., Gilmore, H. J., Zhu, H.T., and Styner, M. A. Diffusion Tensor Imaging Based Characterization of Neurodevelopment in Primates. Cerebral Cortex, 23:36-48, 2013.
  58. Li, YM, John Gilmore, JP Wang, Styner, Weili Lin, and Zhu, HT. Two-stage spatial adaptive analysis of twin neuroimaging data. IEEE Transactions on Medical Imaging. 31, 1100-12, 2012.
  59. Z Liu, M Farzinfar, L. M. Katz, HT Zhu, C. B. Goodlett, G. Gerig, M. Styner, and B. L. Marks. Automated voxel-wise brain DTI analysis of fitness and aging. The Open Medical Imaging Journal, 6, 80-88, 2012.
  60. Hua, Z.W., Dunson, D., Gilmore, J.H., Styner, M., and Zhu, HT. Semiparametric Bayesian Local Functional Models for Diffusion Tensor Tract Statistics.  NeuroImage, 63, 460-674, 2012.
  61. John H. Gilmore, Feng Shi, Sandra Woolson, Rebecca C. Knickmeyer , Sarah J. Short , Weili Lin, Zhu, HT., Robert M. Hamer, Martin Styner, and Dinggang Shen. Longitudinal Development of Cortical and Subcortical Gray Matter from Birth to 2 Years. Cerebral Cortex, 22(11):2478-85,
  62. Zhu, HT., Kong, L., Li, R., Styner, M.,  Gerig, G., Lin, W. and  Gilmore, J. H.  FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics, NeuroImage, 56, 1412-1425, 2011.
  63. R. Scola, T.C. Nichols, H Zhu, M.C. Caughey, E.P. Merricks, R.A. Raymer, P. Margaritis, K.A. High, and C. M. Gallippi. ARFI Ultrasound Monitoring of Hemorrhage and Hemostasis In Vivo in Canine von Willebrand Disease and Hemophilia. Ultrasound in Medicine and Biology, 37,2126-32, 2011.
  64. Wei Gao, John H Gilmore, Kelly S Giovanello, Jeffery Keith Smith, Dinggang Shen, Hongtu Zhu, and Weili Lin, “Temporal and Spatial Evolution of Brain Network Topology during the First Two Years of Life”, PLoS One, 6(9): e25278. 2011.
  65. Chen, Y.S, An, H.Y., Zhu, HT, Shen, D.G., Gilmore, J.H. and Lin, W. L. Longitudinal regression analysis of spatial-temporal growth patterns of geometrical diffusion measures in early postnatal population with diffusion tensor imaging, NeuroImage, 58, 993-1005, 2011.
  66. Skup, M., Zhu, HT., Wang, YP., Giovanello, K.S., Lin, J.A. Shen, D.G., Shi, F., Wang, J.P., Gao, W., Lin, W., Fan, Y., Zhang, H. and Sex Differences in Grey Matter Atrophy Patterns Among AD and aMCI patients: Results from ADNI. NeuroImage, 56, 890-906, 2011.
  67. Paniagua, B., Cevidanes, L., Walker, D., Zhu, H., Guo, RX., Styner, M. Clinical application of SPHARM-PDM to qualify temporomandibular joint osteoarthritis. Computerized Medical Imaging and Graphics, 35, 345-352, 2011.
  68. Paniagua, B., Cevidanes, L., Zhu, H.T., Styner, M. Surgical Outcome Quantification using SPHARM-PDM Toolbox in orthognathic surgery. International Journal of Computer Assisted Radiology and Surgery, 6, 617-626, 2011.
  69. Zhu, H.T., Styner, M., Tang, N.S., Liu, Z.X., Lin, W.L., Gilmore, J.H. FRATS: functional regression analysis of DTI tract statistics. IEEE Transactions on Medical Imaging, 29, 1039-1049, 2010.
  70. Yap PT, Wu GR, Zhu HT, Lin W, Shen DG. Fast Tensor Image Morphing for Elastic Registration, IEEE Transactions on Medical Imaging, 29, 1192 – 1203, 2010.
  71. Cevidanes LHS, Hajati A-K, Paniagua B, Lim, PF, Walker DG, Palconet G, Nackley AG, Ludlow JB, Styner MA, Zhu H, Phillips C. Quantification of Condylar Resorption in TMJ Osteoarthritis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 110, 110-117,  Winner of Best Paper award
  72. Grauer, D., Cevidanes, L., Styner, M., Heulfe, I., Harmon, E., Zhu, HT., Proffit, W. R.  Accuracy and landmark error calculation using CBCT generated cephalograms, Angle Orthod, 80  286-94, 2010.
  73. Gao W, Zhu HT, Lin WL. DTI imaging parameters optimization using prior information of fiber orientation, NeuroImage, 44, 729-741, 2009.
  74. Gao, W., Zhu, HT., Giovanello, K. S., Smith, J. K., Shen, D. G., Gilmore, J. H., and  Lin, W.L. Emergence of the brain’s default network: Evidence from two-week-old to four-year-old healthy pediatric subjects. Proceedings of the National Academic Sciences, USA, (PNAS Direct Submission) 106, 6790-6795, 2009.
  75. Peterson BS, Warner V, Bansal R, Zhu HT, Hao X, Xu D, Liu J, Weissman MM. Right hemisphere cortical thinning in persons at risk for major depression. Proceedings of the National Academic Sciences, USA, (PNAS Direct Submission) 106, 6273-6278, 2009.
  76. Posner, J., …, Zhu HT, Peterson BS. The neurophysiological bases of emotion: An fMRI study of the affective circumplex using emotion-denoting words. Human Brain Mapping,  30, 883-895, 2009.
  77. Yap, P. T., Wu, G.R., Zhu, HT.,  Lin, W.L., Shen, DG.. TIMER: Tensor Image Morphing for Elastic Registration. Neuroimage, 47, 549-563, 2009.
  78. Chen, Y.S., An, H. , Zhu, HT., Smith, K.,  Hall C,  Robertson K., Robertson W, Gao, F., Bullit, E., Lin, W. L.,  Assessing white matter abnormalities in three clinical stages of HIV with diffusion tensor imaging. Neuroimage, 47,1154-1162, 2009.
  79. Behler, R. H., Scola, MR., Nichols, TC., Caughey, MC., Fisher, M.W., Zhu, H.T., Gallippi, CM.  ARFI Ultrasound for in vivo hemostasis assessment postcardic catheterization, Part II: pilot clinical results. Ultrasonic Imaging, 31, 159-171, 2009.
  80. Behler, R. H., Nichols, T.C., Zhu, H.T., Merricks, E. P., Gallippi, C. M. ARFI Imaging for noninvasive material characterization of atherosclerosis part II: toward In vivo characterization. Ultrasound in Medicine and Biology, 35, 278-295, 2009.
  81. Yuan Y, Zhu HT, Ibrahim J, Lin WL, and Peterson B.G.. A note on bootstrapping uncertainty of diffusion tensor parameters, IEEE Transactions on Medical Imaging, 27, 1506-1514, 2008.
  82. Colibazzi, T., Zhu HT, Bansal, R., and Peterson, B. S. Exploratory and confirmatory factor analysis of cortical and subcortical gray matter volumes in healthy individuals. Human Brain Mapping, 29, 1302-1312,  2008.
  83. Mauldin, F. W., Zhu HT, Behler, R. H., and Gallippi, C. M. Robust principal component analysis and clustering methods for automated ARFT segmentation. Journal of Ultrasound in Medicine and Biology, 34, 309-325, 2008.
  84. Sowell ER, Peterson BG, Kan E, Woods RP, Yoshii J, Bansal R, Xu DR, Zhu HT, Thompson PM, and Toga AW.  Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cerebral Cortex, 17, 1550-1560, 2007.
  85. Bansal R, Staib LH, Xu DR, Zhu HT, and Peterson B. Statistical analyses of brain surfaces using Gaussian random fields on manifolds. IEEE Trans Med Imaging, 26, 46-57, 2007.
  86. Zhu HT, Ibrahim JG, Tang NS, Hao XJ, Bansal R, and Peterson BG. A wild bootstrap method for statistical analysis of brain  morphometric measures. IEEE Trans Med Imaging, 26, 954-967, 2007.
  87. Marsh, R., Zhu, HT., Quackenbush, G., Royal. J., Skudlarski P, and Peterson BG. A developmental fMRI study of self-regulatory control. Human Brain Mapping, 27, 848-863, 2006.
  88. Zhu HT, Xu DR, Amir R, Hao XJ, Zhang HP, Alayar K, Bansal R, and Peterson BG. A statistical framework for the classification of tensor morphology in diffusion tensor images. Magnetic Resonance Imaging, 24, 569-582, 2006.


Psychiatry Journals

(Biological Psychiatry, Archives of General Psychiatry  and American Journal of Psychiatry are among the best five journals in psychiatry)

  1. Styner, M., Shi, X., …, and Zhu, HT. Localized differences in caudate and hippocampal shape are associated with schizophrenia but not antipsychotic type. Psychiatric Research: Neuroimaging, 211(1):1-10,
  2. Raz, A., Schweizer, H.R., Zhu, H.T., and Bowles, E. N. Hypnotic dreams as a lens into hypnotic dynamics. International Journal of Clinical and Experimental Hypnosis, 58, 69-81, 2010.
  3. Miller, A., Bansal, ,   Hao, X.,  Sanchez-Pena, J.P.,   Miller, L.J., Liu, J, Xu, D. R., Zhu, H.T., Chakravarty, M. M.,  Durkin, K, Ivanov I., Plessen, K.J., Kellendonk,C.B., Peterson, B. S.   Enlargement of thalamic nuclei in persons with Tourette Syndrome. Archives of General Psychiatry, 67,955-964, 2010.
  4. Yeh PH, Zhu HT, Nicoletti MA, Hatch JP, Soares JC. Structural equation modeling of gray matter volumes in major depressive and bipolar disorders: differences in latent volumetric structure. Psychiatric Research: Neuroimaging, 184, 177-185, 2010.
  5. Colibazzi, T., …, Zhu, HT.,…, Peterson, B. Neural systems subserving valence and arousal during the experience of induced emotions. Emotion, 10, 377-289, 2010.
  6. Iliyan Ivanov, Ravi Bansal, Xuejun Hao, Zhu HT, …, Bradley S. Peterson. Morphological abnormalities of the thalamus in youth with ADHD.  American Journal of Psychiatry, 167:397-408,
  7. Peterson BS, Potenza, M.N.,  Wang,  , Zhu HT,  Martin A., Marsh R, Plessen KJ., Yu, S. A functional MRI study of the effects of psychostimulants on default-mode processing during performance of the word-color stroop task in youth with ADHD. American Journal of Psychiatry, 166, 1286-1299, 2009.
  8. Raz, A., Packard, M.G., Alexander, G. M., Buhle, J.T., Zhu, HT., Yu, S. and Peterson, B. A slice of : An exploratory neuroimaging study of digit encoding and retrieval in a superior memorist. Neurocase, 15, 361-372, 2009.
  9. Lewis, M., Smith,, Styner, M., Gu, H., Poole, R., Zhu, H., Li, Y., et al., Huang, X. Asymmetrical lateral ventricular enlargement in Parkinsons disease. European Journal of Neurology, 16,  475-481, 2009.
  10. Raz A, Zhu HT, …, Peterson BS. Neural substrates of self-regulatory control in children and adults with Tourette Syndrome. Can J Psychiatry, 54, 579-588,
  11. Gerber AJ, …, Zhu HT, Russell J, Peterson BS. An affective circumplex model of neural systems subserving valence, arousal, & cognitive overlay during the appraisal of emotional faces. Neuropsychologia, 46, 2129–2139, 2008.
  12. Peterson, B., Choi, H.A., Hao, X.J., Amat, J., Zhu HT, Whiteman, R., Liu, J., Xu, D.R., and Bansal, R. Morphology the amygdale and hippocampus in children and adults with tourette syndrome,  Archives of General Psychiatry,  64, 1281-1291, 2007.
  13. Rachel M., Zhu HT, Wang ZS, Skudiarski P., and Peterson, BG. A developmental fMRI study of self-regulatory control in tourette syndrome. American Journal of Psychiatry, 164, 955-966, 2007.
  14. Raz, A, Moreno-Iñiguez M, Martin L, and Zhu HT. Deautomatizing an automatic process: suggestion and the stroop effect. Consciousness and Cognition, 16, 331-338, 2007.
  15. Wiedenmayer, C.P., Bansal, R., Anderson, G.M., Zhu, H.T., Amat, J., Whiteman, R, and Peterson, B.G. Cortisol levels and hippocampus volumes in healthy preadolescent children. Biological Psychiatry, 60, 856-861, 2006.
  16. Gorman D, Zhu HT, Anderson G, Davies M, and Peterson BG. Peripheral iron indices in Tourette’s syndrome and their association with basal ganglia and regional cortical volumes. American Journal of Psychiatry, 163, 1264-72, 2006.
  17. Amat J, Bronen R, Saluja, S., Sato, N., Zhu HT, Gorman, D.A., Royal, J. and Peterson BG. Increased number of subcortical hyperintensities on MRI in children and adolescents with Tourette’s syndrome, obsessive-compulsive disorder, and attention deficit hyperactivity disorder.  American Journal of Psychiatry,  163, 1106-1108, 2006.
  18. Kerstin JP, Bansal  R, Zhu HT, Whiteman R, Amat J, Quackenbusch G,  Martin L, Durkin K, Blair C, Royal J, Hugdahl K, and Peterson BG. Hippocampus and Amydala morphology in attention-deficit/hyperactivity disorder.  Archives of General Psychiatry, 63, 795-807, 2006.
  19. Marsh R, Alexander GM, Packard MG, Zhu HT, and Peterson BG. Perceptual motor skill learning in tourette syndrome. Neuropsychologia, 43:1456-65, 2005.
  20. Bloch MH, Leckman JF, Zhu HT, and Peterson BG. Caudate volumes in childhood predict the severity of tic and OCD symptoms in adulthood. Neurology, 65:1253-1258, 2005.
  21. Marsh R, Alexander GM, Packard MG, Zhu HT, Wingard JC, Quackenbush G, Stein V, and Peterson BG. Impaired habit learning in children and adults with Tourette syndrome. Archives of General Psychiatry, 61:1259-1268, 2004.



  1. Villarreal-Calderon, R. Torres-Jardón, J. Palacios-Moreno, N. Osnaya, B. Pérez-Guillé, R. R Maronpot, W. Reed, H. Zhu, L. C. Garcidueñas .  Urban air pollution targets the dorsal vagal complex and dark chocolate offers neuroprotection. International Journal of Toxicology, 2010, 604-615.
  2. Calderón-Garcidueñas, A. Serrano-Sierra, R.Torres-Jardón, H. Zhu, Y.Yuan, D. Smith, R. Delgado-Chávez, J. V. Cross, H. Medina-Cortina, M. Kavanaugh, T. R. Guilarte.  The impact of environmental metals in young urbanites’ brains. Experimental and Toxicologic Pathology, 65:503-11, 2013.
  3. O’Neill, S. S., Gordon, C.J., Guo, RX, Zhu,  HT., McCudden, C.R. Multivariate analysis of clinical, demographic, and laboratory data for classification of patients with disorders of calcium homeostasis. American Journal of Clinical Pathology,  135:100-107, 2011.
  4. Hongyu An, Andria L. Ford, Katie Vo, Cihat Eldeniz, Rosana Ponisio, Hongtu Zhu, Yimei Li, Yasheng Chen, William J. Powers, Jin-Moo Lee, and Weili Lin. Early changes of tissue perfusion after tPA in hyperacute ischemic. Stroke, 42:65-72, 2011.
  5. Calderón-Garcidueñas, M. Kavanaugh, M. Block, A. D’Angiulli, R Delgado-Chávez, R. Torres-Jardón, A. González-Maciel, R. Reynoso-Robles,  N. Osnaya, R. Villarreal-Calderon, R. Guo, Z. Hua, H. Zhu, G. Perry, Philippe Diaz. Neuroinflammation, Alzheimer’s-associated pathology and down-regulation of the prion-related protein in air pollution exposed children and young adults. Journal of Alzheimer Disease, 2012, 93-107.
  6. L  Calderón-Garcidueñas, R   Engle, A. ‪Mora-Tiscareño, M.  Styner, G.  Gómez-Garza, HT  Zhu, V.  Jewells, R. Torres-Jardón, L.  Romero, M.  E. ‪Monroy-Acosta, C. Bryant, L. O.  González-González, and H.  Medina-Cortina. ‪Exposure to severe urban air pollution influences cognitive ‪outcomes, brain volume and systemic inflammation in clinically healthy ‪children. Brain and Cognition, 2011, 345-55.
  7. Calderón-Garcidueñas, A. Mora-Tiscareño, M. Styner, H. Zhu, R.Torres-Jardón, E. Carlos, E. Solorio-López, H. Medina-Cortina, M. Kavanaugh,  A. D’Angiulli. White matter hyperintensities, systemic inflammation, brain growth and cognitive functions in children exposed to air pollution, Journal of Alzheimer’s Disease, 2012, 183-91.
  8. Villarreal-Calderon R, Dale G, Delgado-Chávez R, Torres-Jardón R, Zhu H, Herritt L, Gónzalez-Maciel A, Reynoso-Robles R, Yuan Y, Wang J, Solorio-López E, Medina-Cortina H, Calderón-Garcidueñas L. Intra-city Differences in Cardiac Expression of Inflammatory Genes and Inflammasomes in Young Urbanites: A Pilot Study. J Toxicol Pathol. 2012 Jun;25(2):163-73.
  9. Rodolfo Villarreal-Calderon, Maricela Franco-Lira, Angélica González-Maciel, Rafael Reynoso-Robles, Lou Harritt, Beatriz Pérez-Guillé, Lara Ferreira-Azevedo, Dan Drecktrah, Hongtu Zhu, Qiang Sun, Ricardo Torres-Jardón, Philippe Diaz, Lilian Calderón-Garcidueñas. Up-regulation of ventricular cellular prion protein in air pollution highly exposed young urbanites: Endoplasmic reticulum stress and the impact of nano size particles. J Mol Sciences, 14, 23471-23491,2013.
  10. Lilian Calderón-Garcidueñas, Antonieta Mora-Tiscareño, Maricela Franco-Lira, Janet V. Cross, Randall Engle, Gilberto Gómez-Garza, Valerie Jewells, Humberto Medina-Cortina, Edelmira Solorio, Chih-kai Chao, Hongtu Zhu, Partha Sarathi Mukherjee, Lara Ferreira-Azevedo, Ricardo Torres-Jardón, Amedeo D’Angiulli. Flavonol-rich dark cocoa significantly decreases plasma endothelin-1 and improves cognitive responses in urban children. Frontiers in Pharmacology, 4:104,
  11. Calderón-Garcidueñas, A. Mora-Tiscareño, R. Torres-Jardón, B. Peña-Cruz, C. Palacios-López,  H. Zhu, L. Kong, N. Mendoza-Mendoza, H. Montesinos-Correa, L. Romero, G. Valencia-Salazar, M. Kavanaugh,  H. Medina-Cortina, S. Frenk. Exposure to urban air pollution and bone health in clinically healthy 6y old children. Arh Hig Rada Toksikol. 64(1):23-34, 2013.
  12. Rodolfo Villarreal-Calderon, Arturo Luévano-González,Mariana Aragón-Flores, Hongtu Zhu, Ying Yuan, Qun Xiang, Benjamin Yan, Kathryn Anne Stoll, Janet V. Cross,Kenneth A. Iczkowski, Alexander Craig Mackinnon Jr. Antral atrophy, intestinal metaplasia, and pre-neoplastic markers in Mexican children with Helicobacter pylori-positive and negative gastritis. Annals of Diagnostic Pathology, 18(3):129-35, 2014.


Peer-reviewed Full Papers in Conference Proceedings

 (MICCAI and IPMI are the most preeminent medical imaging conferences

KDD, NeurIPS, AAAI and ICDM are the most preeminent data mining, machine learning,  and artificial intelligence conferences)

  1. Shuai Li, Ziqi Chen, Hongtu Zhu, Christina Dan Wang, Wang Wen. Nearest-Neighbor Sampling Based Conditional Independence Testing. AAAI 2023.
  2. Y Sun, Y. Huang, H. Zhu, F. Zhou. Adversarial learning of distributional reinforcement learning. ICML 2023.
  3. Wu, G., Song, G., Lv, X., Luo, S., Shi, C. and Zhu, H. DNet: Distributional network for distributional individualized treatment effects. KDD, 2023.
  4. X Tang, F Zhang, Y Wang, D Shi, B Song, Y Tong, H Zhu, J Ye. Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms, KDD 2021, pp. 3605–3615. Oral presentation.
  5. Liu, C., Feng, H., Xu, J., Qin, Z. and Zhu, H. Optimizing bike-share repositioning: networked inventory management with spatiotemporal modeling. 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 1438-1448.
  6. Fan Zhou, Xiaocheng Tang, Chenfan Lu, Fan Zhang, Zhiwei Qin, Jieping Ye, and Hongtu Zhu. “Multi-Objective Distributional Reinforcement Learning for Large-Scale Order Dispatching”, 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, 2021, pp. 1541-1546.
  7. Qin, Z., Zhu, H.T., and Jieping Ye. Reinforcement learning for ridesharing: a survey. IEEE Intelligent Transportation Systems Conference 2021.
  8. Hai Shu, Ronghua Shi, Hongtu Zhu, Ziqi Chen. mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks. The 2022 International Joint Conference on Neural Networks, 01-08. Oral presentation.
  9. Guojun Wu, Yanhua Li, Shikai Luo, Ge Song, Qichao Wang, Jing He, Jieping Ye, Xiaohu Qie and Hongtu Zhu. A Joint Inverse Reinforcement Learning and Deep Learning Model for Drivers’ Behavioral Prediction. CKIM 2020.
  10. Xiaocheng Tang, Zhiwei Qin, Fan Zhang,  Zhaodong Wang, Zhe Xu, Yintai Ma, Hongtu Zhu, Jieping Ye. A Deep Value-network Based Approach for Multi-Driver Order Dispatching. KDD 2019 (acceptance rate <15%).
  11. Tao Huang, Yintai Ma, Zhiwei Tony Qin, Jianfeng Zheng, Henry X Liu, Hongtu Zhu, Jieping Ye. Origin-destination Flow Prediction with Vehicle Trajectory Data and Semi-supervised Recurrent Neural Network. 2019 IEEE International Conference on Big Data (Big Data), 1450-1459.
  12. Zhou, F., Li, T. F., Zhou, H.B., Ye, J. P. and Zhu, H.T.  Graph-Based Semi-Supervised Learning with Non-ignorable Non-response. NeurIPS 2019 (acceptance rate <20%).
  13. Haipeng Chen, Yan Jiao, Zhiwei Qin, Xiaocheng Tang, Hao Li, Bo An, Hongtu Zhu, and Jieping Ye. InBEDE: Integrating Contextual Bandit with TD Learning for Joint Pricing and Dispatch of Ride-Hailing Platforms.  IEEE International Conference on Data Mining (ICDM), 2019 (acceptance rate <9%).
  14. Lin, Z. H. and Zhu, H.T. MFPCA: Multiscale Functional Principal Component Analysis. AAAI 2019 (acceptance rate <18%).
  15. Zhang, J.W., Ibrahim, J. G., Li, T.F., and Zhu, H.T. A Powerful Global Test Statistic for Functional Statistical Inference. AAAI 2019 (acceptance rate <18%).
  16. Shu, H. and Zhu, H.T. Sensitivity analysis of deep neural networks. AAAI 2019. (acceptance rate <18%)
  17. Tengfei Li, Xifeng Wang, Tianyou Luo, Yue Yang, Bingxin Zhao, Liuqing Yang, Ziliang Zhu, Hongtu Zhu (2019). Adolescentfluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost.In Proceedings of the First Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction (ABCD-NP), held in conjunction with MICCAI 2019, 11791,167-175.
  18. Wang, Z. (Tony) Qin, X. Tang, J. Ye, and H. Zhu. Deep Reinforcement Learning with Knowledge Transfer for Online Rides Order Dispatching. IEEE International Conference on Data Mining (ICDM), 2018.
  19. Dai, ,  Li,  T.,Shu, H.,  Zhong,  L.,  Shen,  H.,  and Zhu, H.  Automatic brain tumor segmentation with domain adaptation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2018).
  20. Tengfei Li, Fan Zhou, Ziliang Zhu, Hai Shu, and Hongtu Zhu. A Label-Fusion-Aided Convolutional Neural Network for Isointense Infant Brain Tissue Segmentation. ISBI 2018.
  21. Liu, C. Huang, T. Li, L. Yang, and Hongtu Zhu. Statistical Disease Mapping for Heterogeneous Neuroimaging Studies. ISBI 2018.
  22. Zhang, J.W., Ibrahim, J.G., C. Knickmeyer, M. Styner, Gilmore, J. H., and H. Zhu. HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics. IPMI 2017.
  23. Zhou, T. Li, H. Li, and H. Zhu. TPCNN: Two-phase Patch-based convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction.  The Multimodal Brain Tumor Segmentation Challenge: MICCAI BRATS 2017.
  24. Pan, W. L., Styner, M., and Zhu, H. Conditional local distance correlation for manifold-valued data. IPMI 2017. Oral.
  25. Yang, Vladimir Jojic, Jun Lian, Ronald Chen, Hongtu Zhu, and Ming Lin. Classification of Prostate Cancer Grades and T-Stages based on Tissue Elasticity Using Medical Image Analysis. MICCAI 2016.
  26. Y. Zhao, F. Zou, Z. Lu, R.C. Knickmeyer, and H. Zhu. Bayesian Feature Selection for Ultra-high Dimensional Imaging Genetics Data. MICCAI Workshop on Imaging Genetics, 2015.
  27. Luo, X. C., Zhu, L. X., Kong, L. and Zhu, H.T. Functional Nonlinear Mixed Effects Models For Longitudinal Image Data. Information Processing in Medical Imaging (IPMI) (acceptance rate <28%)
  28. Shen, D. Zhu, H.T. MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction. Information Processing in Medical Imaging (IPMI) (acceptance rate <28%)
  29. Huang, C., Niethammer, M., Liang, S., Zhu, H.T. Segmentation of longitudinal knee MRI data. Information Processing in Medical Imaging (IPMI) (acceptance rate <32%)
  30. Yuan, Y., Gilmore, J., Geng, X. J., Styner, M., Chen, K. H., Wang, J. L., and Zhu, H.T. A longitudinal functional analysis framework for analysis of white matter tract statistics. Information Processing in Medical Imaging (IPMI) (acceptance rate <32%)
  31. Wang, G. Li, M. Ahn, J. Nie, H. Zhu, D. Shen, L. Guo. Mapping longitudinal cerebral cortex development using diffusion tensor imaging. SPIE Medical Imaging, 2013. Oral presentation.
  32. R. Verde, J.B. Berger, A. Gupta, M. Farzinfar, A. Kaiser, V. W. Chanon, C. A. Boettiger, C. Goodlett, Y. Shi, G. Gerig, S. Gouttard, C. Vachet, H. Zhu, M.A. Styner,  The UNC-Utah NA-MIC DTI framework: atlas based fiber tract analysis with application to a study of nicotine smoking addiction.  SPIE Medical Imaging, 2013. Oral presentation.
  33. E. Lyall, B. Paniagua, Z. Lu, H. Zhu, F. Shi, W. Lin, D. Shen, J. H. Gilmore and M. Styner. Longitudinal lateral ventricle morphometry related to prenatal measures as a biomarker of normal development, MICCAI workshop on Pediatric and Perinatal Imaging (PaPI) 2012, Nice, France, Oct. 1, 2012.
  34. Wang, H. Zhu, J.Q. Fan, K. Giovanello,, and Lin, W. L. Multiscale Adaptive Smoothing Model for the Hemodynamic Response Function in fMRI, MICCAI, LNCS 6892, 269-276, 2011. (acceptance rate <30%)
  35. Li, YM, J. Gilmore, P. Wang, M. Styner, W. Lin, and Zhu, HT. Two-stage spatial adaptive analysis of twin neuroimaging data. Multimodal Brain Image Analysis. Lecture Notes in Computer Science, 2011, Volume 7012/2011, 102-109.
  36. Zhu, H.T., Styner, M., Li, Y.M., Kong, L. N., Shi, W., Lin, W., Coe, C., and J. H. Gilmore. Multivariate Varying Coefficient Models for DTI Tract Statistics. MICCAI, 690-697, (acceptance rate <32%)
  37. Gao, W., Zhu, H.T., Giovanello, K. S., and Lin, W. Multivariate network-level approach to detect interactions between large-scale functional systems. MICCAI, 298-295, (acceptance rate <32%)
  38. Chen, Y.S., Ji, S., Wu, X., An, H.Y. Zhu, H.T., Shen, D. G., and Lin, W.  Simulation of brain mass effect with an arbitrary lagrangian and eulerian FEM. MICCAI, 274-281, (acceptance rate <32%)
  39. Zhu, H.T., Li, Y. M., Ibrahim, J. G., Lin, W., Shen, D. MARM: multiscale adapative regression for neuroimaging data. Information Processing in Medical Imaging (IPMI), 314-325, 2009. (acceptance rate <32%)
  40. Shi, X., Styner, M., Liberman J., Ibrahim, J. G.,  Lin, W., and Zhu, H.T.  Intrinsic regression models for manifold-value data.  International Conference on Medical Imaging Computing and Computer Assisted Intervention (MICCAI),192-199, (acceptance rate <32%)
  41. Yap, P.T., Wu, G.R, Zhu HT, Lin W, Shen DG. Fast Tensor Image Morphing for Elastic Registration, MICCAI, 721-729,  2009. (acceptance rate <32%)
  42. Chen, Y. , Zhu, H.T., Shen, D.G., An, H.Y., Gilmore, J., Lin, W.L. Mapping growth patterns and genetic influences on early brain development in twins. MICCAI, 232-239, 2009. (acceptance rate <32%)
  43. Liu, Z., Zhu, H.T., B.L. Marks, L.M. Katz, C.B. Goodlett, G.Gerig, M. Styner, Voxel-wise group analysis of DTI, Proceedings of the 6th IEEE International Symposium on Biomedical Imaging ISBI: From Nano to Macro 2009; 807-810. (50% acceptance rate).
  44. Tang, S. Y, Fan, Y., Zhu, HT, Shen, D. Regularization of Diffusion Tensor Field Using Coupled Robust Anisotropic Diffusion Filters. Mathematical Methods in Biomedical Image Analysis (MMBIA) 2009.
  45. Yap, P. T., Wu, G.R.,  Zhu, HT.,  Lin, W.L., Shen, DG.. TIMER: Tensor Image Morphing for Elastic Registration. MMBIA 2009.
  46. Liu Z, Zhu HT, Marks BL, Katz LM, Goodlett CB., Gerig G, Styner M. Voxel-wise group analysis of DTI. ISBI 2009.
  47. Zhu HT, Hao X,  Xu DR, Amir R, and Peterson BS.  Theoretical analysis of the effects of noise on isotropic diffusion tensors. MMBIA 2006.
  48. Chen, Y.S., Shen, D. G., Zhu, H. T., An, H. Y., Gilmore, J. H., Lin, W. Hierarchical unbiased group-wise registration for atlas construction and population comparison. SPIE 2009 on Medical Imaging.
  49. Li, Y.M., Zhu, H.T.,  Chen, Y.S., An, H.Y.,  Gilmore, J. H.,  Lin, W.,  Shen, D.    Longitudinal analysis of neuroimaging data.  SPIE 2009 on Medical Imaging.
  50. Li, Y.M.,  Zhu, H.T.,  Chen, Y.S., Ibrahim, J. G., An, H.Y.,  Lin, W.,  Shen, D. Regression analysis of diffusion tensor. SPIE 2009 on Medical Imaging.