Faculty Directory
Daniel Apley

Professor of Industrial Engineering and Management Sciences


2145 Sheridan Road
Tech M235
Evanston, IL 60208-3109

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Professor Apley's Website


Industrial Engineering and Management Sciences


Master of Science in Analytics Program

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Ph.D. Mechanical Engineering, University of Michigan, Ann Arbor, MI

M.S. Electrical Engineering, University of Michigan, Ann Arbor, MI

M.S. Mechanical Engineering, University of Michigan, Ann Arbor, MI

B.S. Mechanical Engineering, University of Michigan, Ann Arbor, MI

Research Interests

Statistical modeling and analysis of engineering and industrial systems; statistical learning and predictive analytics; quality engineering and six sigma; manufacturing process diagnosis and control

Selected Publications

    • Apley, D. W. and Zhu, J., "Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models", Journal of the Royal Statistical Society, Series B (Statistical Methodology), 82(4), pp. 1059–1086, DOI: 10.1111/rssb.12377, June, 2020.
    • Zhang, Y., Tao, S., Chen, W., and Apley, D. W., "Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors," Technometrics,62:3, pp. 291-302, DOI: 10.1080/00401706.2019.1638834, August, 2020.
    • Bui, A. and Apley, D. W., "Monitoring for Changes in the Nature of Stochastic Textured Surfaces," Journal of Quality Technology, DOI: 10.1080/00224065.2018.1507559, 50(4), pp. 363-378, 2018. Received the Lloyd S. Nelson Award
    • Park, C. and Apley, D. W., "Patchwork Kriging for Large-Scale Gaussian Process Regression," Journal of Machine Learning Research, 19(1), pp. 269-311, 2018.
    • Bui, A. and Apley, D. W., "A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces," Technometrics, 60(1), pp. 1—13, DOI: 10.1080/00401706.2017.1302362, 2018.
    • Ouyang, L., Apley, D. W., and Mehrotra, S., "A Design of Experiments Approach to Validation Sampling for Logistic Regression Modeling with Error-Prone Medical Records," Journal of the American Medical Informatics Association, 23, e71-e78, DOI: 10.1093/jamia/ocv132, 2016.
    • Shi, Z., Apley, D. W., and Runger, G. C., "Discovering the Nature of Variation in Nonlinear Profile Data," Technometrics, 58(2), pp. 371-382, 2016.
    • Zhang, N. and Apley, D. W., "Brownian Integrated Covariance Functions for Gaussian Process Modeling: Sigmoidal Versus Localized Basis Functions," Journal of the American Statistical Association, 111(515), pp. 1182-1195, 2016.
    • Bostanabad, R., Bui, A. T., Xie, W., Apley, D. W., and Chen, W., "Stochastic Microstructure Characterization and Reconstruction via Supervised Learning," Acta Materialia, doi:10.1016/j.actamat.2015.09.044, 103(15), pp. 89—102, 2016.
    • Gramacy, R. B. and Apley, D. W., “Local Gaussian process approximation for large computer experiments,” Journal of Computational and Graphical Statistics, DOI:10.1080/10618600.2014.914442, 24(2), pp. 561—578, 2015
    • Sun, Y. Apley, D. W., and Staum, J., "Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation," Operations Research, 59(4), pp. 998–1007, 2011.
    • Shan, X. and Apley, D. W., "Blind Identification of Manufacturing Variation Patterns by Combining Source Separation Criteria," Technometrics, 50(3), pp. 332-343, 2008. Received the Wilcoxon Prize