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Xinwei Deng

Professor

Education

  • Ph.D., School of Industrial and Systems Engineering, Georgia Institute of Technology (Aug. 2004 – Aug. 2009)

    Advisors: Professor C. F. Jeff Wu and Professor Ming Yuan
    Major concentration: Statistics.
    Minor: Optimization.

  • B.S., Mathematics, Nanjing University, China (Sept. 1999 – July 2003).                

Awards & Honors

  • Data Science Faculty Fellow, Virginia Tech, 2022-present.
  • College of Science Faculty Fellow, Virginia Tech, 2019-2022.
  • Top-cited Article in Canadian Journal of Statistics, 2020-2021. 
  • IISE Transactions Best Application Paper Honorable Mention on Quality and Reliability Engineering, 2021.
  • Recognized by “Thank-a-Teacher” from Center for Excellence in Teaching and Learning, Virginia Tech, 2018
  • Top-downloaded Article in Statistical Analysis and Data Mining, 2017-2018.
  • Elected Member of the International Statistical Institute (ISI), 2017.
  • IISE Transactions Best Paper Award on Quality and Reliability Engineering, 2017. 

Professional Membership

  • Member of American Statistical Association (ASA)
  • Member of American Society for Quality (ASQ)
  • Member of International Chinese Statistical Association (ICSA)
  • Member of Institute for Operations Research and the Management Sciences (INFORMS)

 

  • Stat 6504: Experimental Design and Analysis II, Spring 2021
  • Stat 5204: Experimental Design and Analysis, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019
  • Stat 4204/5204G: Design of Experiments: Concepts and Applications, Spring 2017, Spring 2021, Fall 2021, Fall 2022
  • Stat 5526/CS5526: Data Analytics II, Spring 2014, Spring 2016, Spring 2018, Spring 2020, Spring 2022
  • Stat 5525/CS 5525: Data Analytics I, Fall 2011
  • Stat 6404: Advanced Multivariate Analysis, Spring 2013, Spring 2019
  • Stat 5504: Multivariate Methods, Fall 2012, Fall 2014, Fall 2015, Fall 2016, Fall 2019
  • Stat 4504/5504G: Applied Multivariate Analysis, Spring 2022
  • Stat 6984: Causality Learning, Spring 2017
  • Stat 5304: Statistical Computing, Spring 2012, Spring 2014
  • Interface between experimental design and machine learning
  • Model and analysis of high-dimensional data
  • Covariance matrix estimation and its applications
  • Statistical learning and uncertainty quantification
  • Design and analysis of computer experiments
  • Statistical methods for Nano and emerging areas
  • Statistical modeling in financial services

A full list of publications can be found from my homepage.
 
  • 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., 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.
  • Deng, X. and Yuan, M. (2009). Large Gaussian Covariance Matrix Estimation with Markov Structures, Journal of Computational and Graphical Statistics, 18(3), 640–657.
  • 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.
  • Shao, J. and Deng, X. (2012). Estimation in High-Dimensional Linear Models with Deterministic Covariates, Annals of Statistics, 40(2), 812–831.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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, 114 (528), 1466-1480.
  • Shen, S., Kang, L., and Deng, X. (2020). Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments, Technometrics, 62(2), 265-276.  
  • Kang, X., Deng, X., Tsui, K. and Pourahmadi, M. (2020). On Variable Ordination of Modified Cholesky Decomposition for Estimating Time-Varying Covariance Matrices, International Statistical Review, 88(3), 616-641.
  • Wu, Q., Deng, X., Wang, S., and Zeng, L. (2021) Constrained Varying-Coefficient Model for Time-Course Experiments in Soft Tissue Fabrication, Technometrics, 63(2), 249-262.
  • Chu. S., Jiang, H., Xue. Z., and Deng, X. (2021). Adaptive Convex Clustering of Generalized Linear Models with Application in Purchase Likelihood Prediction, Technometrics, 63(2), 171-183.
  • Kang, X., and Deng, X. (2021). On Variable Ordination of Modified Cholesky Decomposition for Sparse Covariance Matrix Estimation, Canadian Journal of Statistics, 49(2), 283-310.
  • Li, Y. and Deng, X. (2021). A Sequential Algorithm for Elastic I-Optimal Design of Generalized Linear Models, Canadian Journal of Statistics, 49(2), 438-470.
  • Xie., W. and Deng, X. (2020). Scalable Algorithms for the Sparse Ridge Regression, SIAM Journal on Optimization, 30(4), 3359-3386.
  • Li, Y., Deng, X., Ba, S., Myers, W. R., Brenneman, W. A., Lange, S. J., Zink, R., and Jin, R. (2022). Clustering-based Data Filtering for Manufacturing Big Data Systems, Journal of Quality Technology, 54(2), 290-302.
  • Xiao, Q., Mandal, A., Lin, C. D., and Deng X. (2021). EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with both Quantitative and Qualitative Factors, SIAM/ASA Journal of Uncertainty Quantification, 9(2), 333-353.
  • Li, Y., Kang, L., and Deng, X. (2022). A Maximin Φp-Efficient Design for Multivariate Generalized Linear Models, Statistica Sinica, 32, 1-23.
  • Peng, T., Jiang, H., Kim, H., and Deng, X. (2021) Robust Estimation of Sparse Precision Matrix using Adaptive Weighted Graphical Lasso Approach, Journal of Nonparametric Statistics, 33(2), 249-272.
  • Lian, J., Freeman, L., Hong, Y., and Deng, X. (2021). Robustness with Respect to Class Imbalance in Artificial Intelligence Classification Algorithms, Journal of Quality Technology, 53(5), 505-525.
  • Kang, X., Kang, L., Chen, W., and Deng, X. (2022). A Generative Approach to Modeling Data with Qualitative and Quantitative Responses, Journal of Multivariate Analysis, 190, 104952.
  • Peng, X., Salado, A., and Deng, X. (2022). A Parallel Tempering Approach for Efficient Exploration of the Verification Tradespace in Engineered Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press.
  • Liang, Q., Ranganathan, S., Wang, K., and Deng, X. (2022). JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment Prediction, Technometrics, in press.
  • Xiao, Q., Wang, Y., Mandal, A., and Deng X. (2022). Modeling and Active Learning for Experiments with Quantitative-Sequence Factors, Journal of the American Statistical Association, accepted.
Xinwei Deng
Xinwei Deng, Statistics

Professor