HUANG, Jianhua

Presidential Chair Professor
Associate Dean (Faculty Affairs and Strategic Operations)

Education Background

Ph.D. in Statistics, University of California at Berkeley, 1997
M.S. in Probability and Statistics, Beijing University, 1992
B.S. in Probability and Statistics, Beijing University, 1989

Research Field
Computational and Bayesian Statistics, Functional and Longitudinal Data Analysis, Nonparametric Statistics, Semi-parametric Inference, Statistical Machine Learning, Statistical Methods for Big Data Sets, Spatial Statistics
Room 605, Daoyuan Building

Dr. Huang received his Ph.D. in Statistics (1997) from University of California at Berkeley. He worked in the Wharton School, University of Pennsylvania and in Texas A&M University before joining Chinese University of Hong Kong, Shenzhen. At Texas A&M University, he has served as Acting Associate Dean for Graduate Studies of College of Science, Interim Department Head of Department of Statistics, and Associate Director for Education of Texas A&M Institute of Data Science. He has supervised more than 20 Ph.D. students. He has published over 100 refereed papers and is among the top 2% most cited statisticians around the world. His research has been funded by National Science Foundation of the United States. He is a Fellow of American Statistical Association and a Fellow of Institute of Mathematical Statistics, and an Elected Member of International Statistical Institute. He has served on the editorial board for Journal of American Statistical Association, STAT (The ISI's Journal for the Rapid Dissemination of Statistics Research), Journal of Multivariate Analysis, and Chemometrics and Intelligent Laboratory Systems.

Academic Publications

A. Asymptotic theory

1.Huang, J. Z. (1998). Projection estimation in multiple regression with application to functional ANOVA models. Annals of Statistics, 26, 242-272.

2.Huang, J. Z., Kooperberg C., Stone, C. J. and Truong, Y. K. (2000). Functional ANOVA modeling for proportional hazards regression. Annals of Statistics, 28, 960-999.

3.Huang, J. Z. (2001). Concave extended linear modeling: A theoretical synthesis. Statistica Sinica, 11, 173-197.

4.Huang, J. Z. (2003). Local asymptotics for polynomial spline regression. Annals of Statistics, 31, 1600-1635.

5.Xu, G. and Huang, J.Z. (2012). Asymptotic optimality and efficient computation of the leave-one-subject-out cross-validation. Annals of Statistics, 40, 2765-3175.

6.Huang, J.Z. and Su, Ya (2021). Asymptotic properties of penalized spline estimators in concave extended linear models: rates of convergence. Annals of Statistics, to appear.

B. Functional data analysis

1.Zhou, L., Huang, J.Z. and Carroll, R. J. (2008). Joint modeling of paired sparse functional data using principal components. Biometrika, 95, 601-619.

2.Huang, J.Z., Shen, H. and Buja, A. (2009). The analysis of two-way functional data using two-way regularized singular value decompositions. Journal of American Statistical Association, 104, 1609-1620.

3.Zhou, L., Huang, J.Z., Martinez, J.G., Maity, A., Baladandayuthapani, V. and Carroll, R. J. (2010). Reduced rank mixed effects models for spatially correlated hierarchical functional data. Journal of American Statistical Association, 105, 390-400.

4.Maadooliat, M., Zhou, L., Najibi S.M., Gao, X. and Huang, J.Z. (2016). Collective estimation of multiple bivariate density functions with application to angular-sampling-based protein loop modeling. Journal of the American Statistical Association, 111, 43-56.

5.Li, G., Shen H., and Huang, J.Z. (2016). Supervised sparse and functional principal component analysis. Journal of Computational and Graphical Statistics, 25, 859-878.

6.Li, G., Huang J.Z., Shen H. (2018). Exponential family functional data analysis via a low-rank model. Biometrics, 2018, 1301-1310.

C. Gaussian process and smoothing splines

1.Ma, P., Huang, J.Z. and Zhang, N. (2015). Efficient computation of smoothing splines via adaptive basis sampling. Biometrika, 102 (3), 631--645.

2.Park, C. and Huang, J.Z. (2016). Efficient computation of Gaussian process regression for large spatial datasets by patching local Gaussian processes. Journal of Machine Learning Research, 17(174), 1-29.

3.Zhang, N., Ma, P., Zhong, W. and Huang, J.Z. (2017). Adaptive basis selection for exponential family smoothing splines with application in joint modeling of multiple sequencing samples. Statistica Sinica, 27, 1757-1777.

D. Longitudinal data analysis

1.Huang, J. Z., Liu, N., Pourahmadi, M., and Liu, L. (2006). Covariance selection and estimation via penalised normal likelihood. Biometrika, 93, 85-98.

2.Huang, J.Z., Wu., C.O., Zhou, L. (2002). Varying coefficient models and basis function approximations for the analysis of repeated measurements. Biometrika, 89, 111-128.

3.Huang, J. Z., Wu, C. O. and Zhou, L. (2004). Polynomial spline estimation and inference for varying coefficient models with longitudinal data. Statistica Sinica, 14, 747-772.

4.Wang L., Li, H. and Huang, J.Z. (2008). Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements. Journal of American Statistical Association, 103, 1556-1569.

E. Multivariate analysis and statistical learning

1.Shen, H. and Huang, J.Z. (2008). Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis, 99, 1015-1034.

2.Lee, S., Huang, J.Z. and Hu, J. (2010). Sparse logistic principal components analysis for binary data.  Annals of Applied Statistics, 4, 1579--1601.

3.Lee, M., Shen, H., Huang, J. Z. and Marron J. S. (2010). Biclustering via sparse singular value decomposition. Biometrics, 66, 1087-1095.

4.Chen, L. and Huang, J.Z. (2012). Sparse reduced-rank regression for simultaneous dimension reduction and variable selection. Journal of American Statistical Association, 107, 1533-1545.

5.L. Zhang, H. Shen, and J. Z. Huang (2013). Robust regularized singular value decomposition with application to mortality data. Annals of Applied Statistics, 7, 540-1561.

6.He, K., Lian, H., Ma, S. and Huang, J.Z. (2018). Dimensionality reduction and variable selection in multivariate varying-coefficient models with a large number of covariates. Journal of American Statistical Association, 113, 746--754.

F. Semiparametric inference

1.Huang, J.Z., Zhang, L. and Zhou. L. (2007). Efficient estimation in marginal partially linear models for longitudinal/clustered data using splines. Scandinavian Journal of Statistics, 34, 451-477.

2.Cheng G. and Huang, J.Z. (2010). Bootstrap consistency for general semiparametric M-estimation. The Annals of Statistics, 38, 2884—2915.

G. Spatial statistics

1.Huiyan Sang, Mykyoung Jun, Jianhua Z. Huang (2011). Covariance approximation for large multivariate spatial datasets with an application to multiple climate model errors. Annals of Applied Statistics, 5, 2519--2548.

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

3.Bohai Zhang, Huiyan Sang, Jianhua Z. Huang (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25, 99-114.

4.Bohai Zhang, Huiyan Sang, Jianhua Z. Huang (2019). Smoothed full-scale approximation of Gaussian process models for computation of large spatial datasets. Statistica Sinica, 29, 1711-1737.

H. Statistics and astronomy

1.S. He, W. Yuan, J.Z. Huang, J. Long and L.M. Macri (2016). Period estimation for sparsely-sampled quasi-periodic light curves applied to Miras. The Astronomical Journal, 152(6), 164.

2.Shiyuan He, Lifan Wang, Jianhua Z. Huang (2018). Characterization of Type Ia Supernova Light Curves Using Principal Component Analysis of Sparse Functional Data. The Astrophysical Journal, 857(2), 110.

3.Shiyuan He, Zhenfeng Lin, Wenlong Yuan, Lucas Macri, Jianhua Z. Huang (2021). Simultaneous inference of periods and period-luminosity relations for Mira variable stars. Annals of Applied Statistics, to appear.

I. Statistics and business

1.Shen, H. and Huang, J.Z. (2005). Analysis of call center data using singular value decomposition. Applied Stochastic Models in Business and Industry, 21, 251-263.

2.Shen, H. and Huang, J.Z. (2008). Interday forecasting and intraday updating of call center arrivals. Manufacturing & Service Operations Management, 10, 391-410.

3.Shen, H. and Huang, J.Z. (2008). Forecasting of inhomogeneous Poisson processes with applications to call center workforce management. The Annals of Applied Statistics, 2, 601-623.       

4.G. Li, J.Z. Huang, H. Shen (2018). To Wait or Not to Wait: Two-way Functional Hazards Model for Understanding Waiting in Call Centers. Journal of American Statistical Association, 113, 1503-1514.

J. Statistics and engineering

1.Park, C., Huang, J. Z., Huitink, D., Kundu, S., Mallick, B., Liang, H. and Ding, Y. (2012) A Multi-stage, Semi-automated procedure for analyzing the morphology of nanoparticles. IIE Transactions, Special issue on Nanomanufacturing, 44, 507-522. Best Application Paper for IIE Transactions (Quality/Reliability Engineering Focus Issue).

2.Park, C., Huang, J.Z., Ji, J. and Ding, Y. (2012). Segmenting, inference and classification of partially overlapping nanoparticles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 507-522.

3.Qian, Y., Huang, J.Z., Li, X. and Ding, Y. (2016). Robust nanoparticles detection from noisy background by fusing complementary image information. IEEE Transactions on Image Processing, 25(12), 5713-5726.

4.Pourhabib, A., Huang, J. Z. and Ding, Y. (2016). Short-term wind speed forecast using measurements from multiple turbines in a wind farm. Technometrics, 58(1), 138-147.

5.Qian, Y., Huang, J.Z. and Ding, Y. (2017). Identifying Multi-stage Nanocrystal Growth Using in-situ TEM Video. IISE Transactions, 49, 532-543.

6.Y. Qian, J.Z. Huang, C. Park, Y. Ding (2019). Fast dynamic nonparametric distribution tracking in electron microscopic data. Annals of Applied Statistics, 1537-1563.

K. Time series

1.Huang, J.Z. and Yang, L. (2004). Identification of nonlinear additive autoregressive models.  Journal of Royal Statistical Society, Series B, 66, 463-477.

2.Huang, J.Z. and Shen, H. (2004). Functional coefficient regression models for nonlinear time series: a polynomial spline approach. Scandinavian Journal of Statistics, 31, 515-534.

3.Hays, S., Shen, H. and Huang, J.Z. (2012). Functional dynamic factor models with application to yield curve forecasting. Annals of Applied Statistics, 6, 870-894.

4.W. Lin, T.S. McElroy, J.Z. Huang (2019). Time series seasonal adjustment using regularized singular value decomposition. Journal of Business & Economic Statistics, 1-23.