Xinwei Deng

Associate Professor

Department of Statistics

Virginia Tech

211 Hutcheson Hall

Blacksburg, VA 24061

Phone: 540-231-5638

Email: xdeng"at"

Curriculum Vitae

Research Interests

·  Interface between design of experiments and machine learning

·  Model and analysis of high-dimensional data

·  Covariance matrix estimation and its applications

·  Statistical methods to Nanotechnology

·  Design and analysis of computer experiments

·  Statistical modeling with applications in financial services

Publications and Reports

·  Deng, X., Yuan, M. and Sudjianto A. (2007), A Note on Robust Kernel Principal Component Analysis, Contemporary Mathematics, 443, 21-33.pdf


·  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.pdf


·  Deng, X., and Yuan, M. (2009), Large Gaussian Covariance Matrix Estimation with Markov Structure, Journal of Computational and Graphical Statistics, 18(3), 640-657.pdf


·  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.pdf


· Mai, W., and Deng, X. (2010). Applications of Statistical Quantification Techniques in Nanomechanics and Nanoelectronics, Nanotechnology, 21(40), 405704.pdf


·  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.pdf


·  Morgan, J.P. and Deng, X. (2011). Experimental Design, WIREs Data Mining and Knowledge Discovery, 2, 164-172. pdf


· Shao, J., and Deng, X. (2012). Estimation in High-Dimensional Linear Models with Deterministic Covariates, Annals of Statistics, 40(2), 812-831. pdf


·  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. pdf


·  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. pdf


·  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 17.4%) pdf


·  Jiang, H. J., Deng, X., Lopez, V., and Hamann, H. (2013). Online Updating and Scheduling of Computer Model with Application to Data Center Thermal Management, Proceedings of ASME IPACK2013, 73042. pdf 


·  Moon, J. Y., Chaibub Neto, E., Deng. X., and Yandell, B. S. (2014). Bayesian Causal Phenotype Network Incorporating Genetic Variation and Biological Knowledge, in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics, Oxford University Press.pdf


·  Yeo, I-K, Johnson, R. A., and Deng, X. (2014). An Empirical Characteristic Function Approach to Selecting a Transformation to Normality, Communications for Statistical Applications and Methods, 21(3), 213-224. pdf


·  Li, H., Deng, X., Kim, D-Y and Smith. E. (2014). A Varying Coefficient Model for Daily Stream Temperatures, Water Resource Research, 50(4), 3073-3087. pdf


·  Jin, R. and Deng, X. (2015). Ensemble Modeling for Data Fusion in Manufacturing Process Scale-up, IIE Transactions, 47(3), 203–214. pdf


·  Deng, X., Hung, Y. and Lin, C. D. (2015). Design for Computer Experiments with Qualitative and Quantitative Factors, Statistica Sinica, 25, 1567–1581. pdf


·  Deng, X. and Jin, R. (2015). QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems, Technometrics, 57(3), 320–331. pdf


·  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. pdf


·  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. pdf


· 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 and Air Temperature Relationship, Environmetrics, 27(1), 15-26. pdf


·  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. pdf


·  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. pdf


· Deng, X., Lin, C. D., Liu, K-W, and Rowe, R. K. (2016). Additive Gaussian Process for Computer Models with Qualitative and Quantitative Factors, Technometrics, to appear. pdf


· Li, H., Deng, X., and Smith, E. P. (2016). Missing Data Imputation for Paired Stream and Air Temperature Sensor Data, Environmetrics, to appear. pdf



· Kang, L., Deng, X., and Jin, R. (2015). Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses, revision for Journal of the American Statistical Association.


· Deng, X. and Qian, P. Z. G. (2016). Designs of Simulation Experiments for Estimating Error Rate of a Classification Rule, revision submitted to Technometrics.


· Zhang, A., Deng, X., Wang, J., and Hobart, J. (2015). A Two-stage Risk Model Construction and Evaluation in Reject Inference, submitted to Annals of Applied Statistics.


· Jin, R. and Deng, X.  (2015). Dynamic Quality Models for Manufacturing Systems Considering Equipment Degradation, submitted to Journal of Quality and Technology.


· Zhang, A. and Deng, X. (2015). A Regularized Approach to Sparse Linear Discrimination Analysis for Two-class Classification, submitted to Computational Statistics and Data Analysis.


· Lozano, A. C., Jiang, H. J., and Deng, X. (2015). Log-Nonlinear Formulations for Robust High-dimensional Modeling, submitted to Log-Linear Models, Extensions and Applications, MIT Press.


· Kang X., Deng X., Tsui K. and Pourahmadi, M (2016). Order-Averaged Cholesky-GARCH Models: Comparison of Asset Ordination Methods, submitted to Journal of Business & Economic Statistics.


· Wu, H., Deng, X., and Ramakrishnan, N.  (2016). Sparse Estimation of Multivariate Poisson Log-Normal Model and Inverse Covariance for Counting Data, submitted to AISTATS 2016.


· Zeng, L., Deng, X., and Yang, J.  (2016). Constrained Gaussian Process with Application in Tissue-engineering Scaffold Biodegradation, revision submitted to IIE Transactions.


· Nino-Ruiz, E. D., Sandu, A., and Deng, X. (2016). A Parallel Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition, submitted to Journal of Computational Science.

· Deng, X., Hung, Y., and Lin, C. D. (2016). Design and Analysis of Computer Experiments, submitted to book chapter of Handbook of Research on Applied Cybernetics and Systems Science, IGI Global.


· Nino-Ruiz, E. D., Sandu, A., and Deng, X. (2016). An Ensemble Kalman Filter Implementation Based On Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation, submitted to SIAM Journal on Scientific Computing.


· Sun, H., Rao, P. K., Kong, Z., Deng, X., and Jin, R. (2016). Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process, submitted to IEEE Transactions on Automation Science and Engineering.


· Peng, T., Jiang, H., Kim, H., and Deng, X. (2016) Robust Estimation of Sparse Gaussian Graphical Model by a Minimum Distance Criterion, submitted to Journal of Nonparametric Statistics.


· Chu, S., Deng, X., and Marathe, A. (2016). A Latent Process Approach for Change-Point Detection of Mixed-Type Observations, submitted to Journal of Quality Technology.


·  Stat 5525: Data Analytics I, Fall 2011

·  Stat 5304: Statistical Computing, Spring 2012, Spring 2014

·  Stat 5504: Multivariate Methods, Fall 2012, Fall 2014, Fall 2015, Fall 2016

·  Stat 6404: Advanced Multivariate Analysis, Spring 2013

·  Stat 5526: Data Analytics II, Spring 2014, Spring 2016

·  Stat 5204: Experimental Design and Analysis, Spring 2015, Spring 2016


Acknowledgement: Research Supported by NSF-CMMI-1435996, NSF-CMMI-1233571, NSF-CMMI- 1634867, CCAM, and P&G.