My broad interests are in Statistics and Machine Learning methods and their application to all of Engineering and to some areas in Science. The "big data" revolution has resulted not only in larger datasets but in data that have a more complex structure. The revolution has been driven not only by faster and more capable computers but also by the facility with which vast amounts of data can be collected over social networks and by internet-based companies, by better and faster non-contact sensors in industry, by micro-arrays, better optics, and increasingly more powerful mass spectrometers in science (the ``omics" revolution), and by better remote sensing and optical equipment in geophysics and astronomy. In industry, while the traditional paradigm in statistics developed by Fisher, “Student” and Neyman, characterized by small samples obtained in expensive experiments, is very powerful and still of great application today, there is a considerable number of fields in both engineering and science where a response of interest is made of thousands of inexpensive observations, given the wide availability of different type of sensors and scanners. 

My research over the years has focused on how to control or optimize an industrial process based on heterogeneous datasets that may be available, and it has evolved through time as the nature of the data that are available (either experimental or observational) has evolved. I am interested in building data-based statistical models and the associated methodology for the control and optimization of engineering systems or that provide helpful information for scientists. This includes diverse problems in process control (Statistical and Time Series Control), Experimental Design, and Response Surface Optimization methods. In recent years I have worked in these areas dealing with complex, large geometrical (or geometrical-spatial) datasets, specifically, functional, shape and surface data (i.e., data that occurs in 1D or 2D-manifolds), image data (2 and 3D) and general high dimensional data that may be concentrated in lower dimensional manifolds.

Dr. Enrique del Castillo is a Distinguished Professor in the Industrial and Manufacturing Engineering Department at Penn State with a joint appointment in the Department of Statistics in the Eberly college of Science.His research has been funded by the NSF, General Motors R&D Corporate Center, Intel Corporation, Netflix, Minitab and NATO, and has totaled over 2.3 million dollars. He is a past recipient of a National Science Foundation (NSF) CAREER Award, a fellow of the Royal Statistical Society, a former editor-in-chief of the Journal of Quality Technology, where he currently serves in its editorial board, a past Associate Editor of the Technometrics journal, and a past Associate Editor of IISE Transactions. At PSU's IME department he is the director of the Engineering Statistics and Machine Learning Laboratory, and at PSU he is a member of the Operations Research Program Committee, an affiliated member of the Institute for Computational and Data Sciences, and a member of the Computational Science Minor Faculty group. If you are an Engineering Ph.D. student with interests in "Data Sciences", Machine Learning, or Statistics, or a Statistics Ph.D. student with interests or background in "Industrial" statistics or in Engineering, in particular, in Optimization and Control of industrial processes, you can contact Dr. Castillo by sending an e-mail to exd13@psu.edu or stop by his office to talk with him in Leonhard building. Dr. Castillo's Erdos Number is 3, if you are curious about that kind of thing.

• IE 433, Regression Analysis and Design of Experiments.  Offered in the Fall semesters. A web version of this course is offered through the Office of Digital Learning sometimes in the summer.

• IE 511, Design of Engineering Experiments . Offered in the Fall semester. 

• IE 532, Reliability Engineering

• IE 583 Statistical and Machine Learning Methods for Response Surface Optimization

• IE 584 Time Series Statistical Learning and Control  - -offered Spring 2024 

IE 532, IE 583, and IE 584 are graduate courses currently offered each once every 3 years in the Spring semester.

 Statistics, Machine Learning, and applications in Engineering

• Li, Hang, and Del Castillo, E., "Optimal design of experiments on Riemannian Manifolds",  Journal of the American Statistical Association, (2022).

• Zhao, X., and Del Castillo, E., ``An Intrinsic Geometrical Approach for Statistical Process Control of Surface and Manifold Data", Technometrics, (2020)

• Del Castillo, E., and Zhao, X., ``Industrial Statistics and Manifold Data" (with discussion),  Quality Engineering, 32(2), pp. 155-167, (2020). Rejoinder, Quality Engineering, 32(2), pp. 176-180, (2020).

• Li, H., Del Castillo, E., and Runger, G., ``On Active Learning Methods for Manifold data", (with discussion). Invited paper, Test, 29, pp. 1-33, (2020). Rejoinder, Test, pp. 42-29, (2020).

• Zhang, L., del Castillo, E., Berlung, A., Tingley, M., Govind, N., ``Computing confidence intervals from massive data via penalized quantile smoothing splines",   Computational Statistics and Data Analysis, 144 (April), (2020) 

• Tajbakhsh, S., Aybat, S., and Del Castillo, E., ``On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach",  J. of Machine Learning, 21 (217), (2020).

• Del Castillo, E., and Reis, M., ``Bayesian Predictive Optimization of Profiles and Multiple-Response Systems in the Process Industry: a review and extensions",  to appear in Chemometrics and Intelligent Laboratory Systems, 206, (2020)

  Collaborations in Science 

• House, C., Tunstall, P, Rapkin, J., Janicot, M., Gage, M., del Castillo, E., and Hunt, J., ``Multivariate stabilizing sexual selection and the evolution of male and female genital morphology in the red  flour beetle",  Evolution, 74(5), pp. 883-896, (2020)

• Rapkin, J., Archer, R., House, C.M., Skaluk, S.K., del Castillo, E., and Hunt, J., ``The geometry of nutritionally based life-history trade-offs: sex differences in the effect of macronutrient intake on the trade-off between encapsulation ability and reproductive effort in decorated crickets", The American Naturalist, Vol. 191 (no. 4), pp. 452-474, (2018).

• Del Castillo, E., Chen, P., Meyer, A., Hunt, J., and Rapkin, J., `` Confidence regions for the location of Response Surface Optima: the R package OptimaRegion", accepted in Communications in Statistics, Simulation and Computation, (2021).

For further publications see the Engineering Statistics and Machine Learning Lab publications website.

For software (codes) that accompany the papers above and many others, see the ESAMLab software website.