Interface between experimental design and machine learning
Model and analysis of high-dimensional data
Covariance matrix estimation and its applications
Design and analysis of computer experiments
Statistical methods for Nano and emerging technology
Deng, X., Yuan, M. and Sudjianto A. (2007).
A Note on Robust Kernel Principal Component Analysis,
Contemporary Mathematics,
443, 21-33.
Deng, X., Joseph, V. R., Sudjianto A. and Wu, C. F. J. (2009).
Active Learning via Sequential Design with Applications to Detection of Money Laundering,
Journal of the American Statistical Association,
104(487), 969-981.
Deng, X. and Yuan, M. (2009).
Large Gaussian Covariance Matrix Estimation with Markov Structure,
Journal of Computational and Graphical Statistics,
18(3), 640-657.
Deng, X., Joseph V. R., Mai, W., Wang, Z. L. and Wu, C. F. J. (2009).
A Statistical Approach to Quantifying the Elastic Deformation of Nanomaterials,
Proceedings of the National Academy of Sciences,
106(29), 11845-11850.
Mai, W., and Deng, X. (2010).
Applications of Statistical Quantification Techniques in Nanomechanics and Nanoelectronics,
Nanotechnology,
21(40), 405704.
Shao, J., Wang, Y., Deng, X., and Wang, S. (2011).
Sparse Linear Discriminant Analysis by Thresholding for High Dimensional Data,
Annals of Statistics,
39(2), 1241-1265.
Morgan, J.P. and Deng, X. (2011).
Experimental Design,
WIREs Data Mining and Knowledge Discovery,
2, 164-172.
Shao, J., and Deng, X. (2012).
Estimation in High-Dimensional Linear Models with Deterministic Covariates,
Annals of Statistics,
40(2), 812-831.
Zhang, Q., Deng, X., Qian, P. Z. G., and Wang, X. (2013).
Spatial Modeling for Refining and Predicting Surface Potential Mapping with Enhanced Resolution.,
Nanoscale,
5, 921-926.
Deng, X. and Tsui, K. W. (2013).
Penalized Covariance Matrix Estimation using a Matrix-Logarithm Transformation,
Journal of Computational and Graphical Statistics,
22(2), 494-512.
Li, H., Deng, X., Kim, D-Y and Smith. E. (2014).
Modeling Maximum Daily Temperature Using a Varying Coefficient Regression Model.,
Water Resource Research,
50(4), 3073-3087.
Jin, R. and Deng, X. (2015).
Ensemble Modeling for Data Fusion in Manufacturing Process Scale-up.,
IIE Transactions,
47(3), 203-214.
Deng, X., Hung, Y. and Lin, C. D. (2015).
Design for Computer Experiments with Qualitative and Quantitative Factors,
Statistica Sinica,
25, 1567-1581.
Deng, X. and Jin, R. (2015).
QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems,
Technometrics,
57(3), 320-331.
Wang, X., Wu, S., Wang, K., Deng, X., Liu, L., and Cai, Q. (2016).
Spatial Calibration Model for Nanotube Film Quality Prediction,
IEEE Transactions on Automation Science and Engineering,
13(2), 903-917.
Zeng, L., Deng, X., and Yang, J. (2016).
Constrained Hierarchical Modeling of Degradation Data in Tissue-engineered Scaffold Fabrication,
IIE Transactions,
48(1), 16-33.
Jiang, H. J., Deng, X., Lopez, V., and Hamann, H. (2016).
Online Updating of Computer Model Output Using Real-time Sensor Data,
Technometrics,
58(4), 472-482.
Li, H., Deng, X., Dolloff, A., and Smith E. (2016).
Bivariate Functional Data Clustering: Grouping Streams based on a Varying Coefficient Model of the Stream Water an Air Temperature Relationship,
Environmetrics,
27(1), 15-26.
Sun, H., Deng, X., Wang, K., and Jin, R. (2016).
Logistic Regression for Crystal Growth Process Modeling through Hierarchical Nonnegative Garrote based Variable Selection,
IIE Transactions,
48(8), 787-796.
Deng, X., Lin, C. D., Liu, K-W, and Rowe, R. K. (2017)
Additive Gaussian Process for Computer Models with Qualitative and Quantitative Factors,
Technometrics,
59(3), 283-292.
Li, H., Deng, X., and Smith, E. P. (2017).
Missing Data Imputation for Paired Stream and Air Temperature Sensor Data,
Environmetrics,
28(1), e2426.
Zheng, H., Tsui, K-W, Kang, X. and Deng, X. (2017).
Cholesky-based Model Averaging for Covariance Matrix Estimation,
Statistical Theory and Related Fields,
1(1), 48-58.
Nino-Ruiz, E. D., Sandu, A., and Deng, X. (2017).
A Parallel Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition,
Journal of Computational Science,
36, 100654.
Sun, H., Rao, P. K., Kong, Z., Deng, X., and Jin, R. (2018).
Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process,
IEEE Transactions on Automation Science and Engineering,
15(1), 393-403.
Wu, H., Deng, X., and Ramakrishnan, N. (2018).
Sparse Estimation of Multivariate Poisson Log-Normal Model and Inverse Covariance for Counting Data
,
Statistical Analysis and Data Mining,
11, 66-77.
Nino-Ruiz, E. D., Sandu, A., and Deng, X. (2018).
An Ensemble Kalman Filter Implementation Based On Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation,
SIAM Journal on Scientific Computing,
40(2), A867-A886.
Zeng, L., Deng, X., and Yang, J. (2018).
A Constrained Gaussian Process Approach to Modeling Tissue-engineered Scaffold Degradation,
IISE Transactions,
50(5), 431-447.
Kang, L., Kang X., Deng, X., and Jin, R. (2018).
Bayesian Hierarchical Models for Quantitative and Qualitative Responses,
Journal of Quality Technology,
50(3), 290-308.
Jin, R., Deng, X., Chen, X., Zhu, L., and Zhang, J. (2019).
Dynamic Quality Models in Consideration of Equipment Degradation,
Journal of Quality and Technology,
51(3), 217-229.
Shen, S., Mao, H., and Deng, X. (2019).
An EM-Algorithm Approach to Open Challenges on Correlation of Intermediate and Final Measurements,
Quality Engineering,
31(3), 505-510.
Xie, Y., Xu L., Li, J., Deng, X., Hong, Y., and Kolivras, K. N. (2019).
Spatial Variable Selection via Elastic Net with an Application to Virginia Lyme Disease Case Data,
Journal of the American Statistical Association,
in press.
Li, Y., Jin, R., Sun, H., Deng, X., and Zhang, C. (2019).
Manufacturing Quality Prediction Using Smooth Spatial Variable Selection Estimator with Applications in Aerosol Jet Printed Electronics Manufacturing,
IISE Transactions,
in press.
Shen, S., Mao, H., and Deng, X. (2019).
Rejoinder: An EM-Algorithm Approach to Open Challenges on Correlation of Intermediate and Final Measurements,
Quality Engineering,
31(3), 516-521.
Shen, S., Kang, L., and Deng, X. (2019).
Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments,
Technometrics,
in press.
Mao, H., Deng, X., Lord, D., Guo, F. (2019).
Adjusting Finite Sample Bias for Poisson and Negative Binomial Regression in Traffic Safety Modeling,
Accident Analysis and Prevention,
in press.
Shen, S., Zhang, Z., and Deng, X. (2019).
On Design and Analysis of Funnel Testing Experiments in Webpage Optimization,
Journal of Statistical Theory and Practice,
in press.
Kang, X., Deng, X., Tsui, K. and Pourahmadi, M. (2019).
On Variable Ordination of Modified Cholesky Decomposition for Estimating Time-Varying Covariance Matrices,
International Statistical Review,
in press.
Kang, X., and Deng, X. (2019).
An Improved Modified Cholesky Decomposition Method for Inverse Covariance Matrix Estimation,
Journal of Statistical Computation and Simulation,
in press.
Lozano, A. C., Jiang, H. J., and Deng, X. (2013).
Robust Joint Sparse Estimation of Multiresponse Regression and Inverse Covariance Matrix,
19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2013),
293-301. (Acceptance rate 25%).
Cadena, J., Basak, A., Vullikanti, A., and Deng, X. (2018).
Graph Scan Statistics with Uncertainty,
32nd AAAI Conference on Artificial Intelligence (AAAI-18),
2771-2778. (Acceptance rate 25%).
Ren, Y., Cedeno-Mieles, V., Hu, Z., Deng, X., et al. (2018).
Generative Modeling of Human Behavior and Social Interactions Using Abductive Analysis,
10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018),
413-420. (Acceptance rate 15%).
Cedeno-Mieles, V., Hu, Z., Deng, X., Ren, Y., et al. (2019).
Mechanistic and Data-Driven Agent-Based Models to Explain Human Behavior in Web-Based Group Anagrams Games,
11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)
(Acceptance rate 14%).
Stat 5504: Multivariate Methods, Fall 2012, Fall 2014, Fall 2015, Fall 2016, Fall 2019
Stat 5204: Experimental Design and Analysis, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019
Stat 5526/CS5526: Data Analytics II, Spring 2014, Spring 2016, Spring 2018
Stat 6404: Advanced Multivariate Analysis, Spring 2013, Spring 2019
Stat 4204/5204G: Design of Experiments: Concepts and Applications, Spring 2017
Stat 6984: Causality Learning, Spring 2017
Stat 5304: Statistical Computing, Spring 2012, Spring 2014
Stat 5525/CS 5525: Data Analytics I, Fall 2011
Deng's Research supported by NSF-CMMI-1435996, NSF-CMMI-1233571, NSF-CMMI- 1634867, NSF-CMMI- 1745207, IAPRA, Safe-D National UTC, CCAM, VT-ICTAS, and P&G.