I am interested in statistical methodology as it applies to all of Engineering and Science. 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 application today, there is a considerable number of fields in both engineering and science where a response of interest is made of hundreds or thousands observations, given the wide availability of different type of sensors and scanners (in industry), microarrays (Biology) or satellite data (Geosciences). Small sample inferential techniques are not always useful to answer the type of questions large data arrays collected with modern technology require. Large databases have heterogenous data types, for instance, they contain text data. How to control or optimize a process where large heterogeneous datasets are available is one of my main research interests. I am currently interested in building “big data”-based mathematical models for the control and optimization of engineering systems or that provide helpful information for scientists. This includes diverse problems in process control (Time Series Control), Experimental Design and Response Surface Optimization methods. In all these fields, the traditional paradigm was that of a reduced number of observations. 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 cloud point data, in work at the intersection of Statistical methodology and Machine Learning methods. I am a past recipient of a National Science Foundation CAREER Award (1996-2001), past editor-in-chief of the Journal of Quality Technology, where I currently serve in its editorial board, past Associate Editor of the Technometrics journal, a past Fulbright Scholar, and a past Associate Editor of IIE Transactions. My graduate education is from Cornell (Operations Research), the National University of Mexico (UNAM, Operations Research) and Arizona (IE and Statistics). I have been fortunate to have my research funded by the National Science Foundation (NSF), General Motors R&D Corporate Center, Intel Corporation, Minitab and NATO. This funded research has totaled over 1.8 million dollars overall (1.4 M as PI share). At PSU's IME department, I am the director of the Engineering Statistics and Machine Learning Laboratory, and I have a joint appointment with Penn State's Department of Statistics. If you are a Statistics Ph.D. student with interests in Engineering or an Engineering Ph.D. student with interests in statistical modeling you ought to stop by my office to talk with me. My Erdos Number is 3, if you are curious about this kind of thing.
Some Selected Recent Publications
Del Castillo, E., Baretta, A., and Semeraro, Q., “Analysis and Optimal Targets Setup of a Multihead Weighing Machine”,
accepted in European J. of Operational Research (2016)
Del Castillo, E., Colosimo, B., and Tajbakhsh, S. ``Geodesic Gaussian Processes for the Reconstruction of a 3D Free-Form Surface", 57(1), 87-99, Technometrics (2015).
Woodall, W.H., and Del Castillo, E. ``An overview of George Box's contributions to process monitoring and feedback adjustment", Applied Stochastic Models in Business and Industry, 30(1), pp. 53–61, (2014).
Alshraideh, H., and Del Castillo, E., ``Statistical performance of tests for factor effects on the shape of objects with application in manufacturing ", IIE Transactions, 45, 2, pp. 121-131, (2013). Featured summary in the IE Magazine (vol. 45(1), p. 48) ``Research Highlights: when the response is the shape of a part".
Smucker, B., Del Castillo, E., and Rosenberger, J., ``Model-Robust Two-Level Designs Using Coordinate Exchange Algorithms and a Maximim Criterion”, Technometrics, 54(4), pp. 367-375, (2012).
Del Castillo, E., Colosimo, B.M. , and Alshraideh, H., ``Bayesian Modeling and Robust Optimization of Functional Responses affected by Noise Factors", Journal of Quality Technology, 44, 2, pp. 117-135, (2012).
Del Castillo, E., and Colosimo, B.M., ``Statistical Shape Analysis of Experiments for Manufacturing Processes", Technometrics, 53(1), pp. 1-15, (2011).
Del Castillo, E., and Santiago, E. ``A matrix-T approach to the sequential design of optimization experiments'', IIE Transactions, 43(1), pp. 54-68, (2010).
B. Bettonvil, E. del Castillo, and J.P.C. Kleijnen. ``Statistical testing of optimality conditions in multiresponse simulation-based optimization," European Journal of Operational Research, 199(2), 448-458, (2009).
Vanli, O.A. and Del Castillo, E., “Closed loop system identification for small samples with constraints”, Technometrics, 49, 4, 382-394, (2007).
Vanli, O.A., Patel, N.S., Janakiram, M., and Del Castillo, E., ``Model-context selection for run-to-run control”, IEEE Transactions on Semiconductor Manufacturing, 20, 4, 506-516, (2007).
Del Castillo, E. “Statistical Process Adjustment: a brief retrospective, current status and future research” Statistica Neerlandica , 60(3), pp. 309-326, (2006).
Lian, Z., and Del Castillo, E. “Adaptive Deadband control of a Drifting Process With Unknown Parameters”, Statistics & Probability Letters, 77(8), 843-852, (2007).
Lian, Z., Colosimo, B.M., and Del Castillo, E., ``Setup Adjustment of Multiple Lots Using a Sequential Monte Carlo Method", Technometrics, 48(3), pp. 373-385, (2006).
Lian, Z., and Del Castillo, E., “Setup Adjustment under Unknown Process Parameters and Fixed Adjustment Cost”, Journal of Statistical Planning and Inference, 136, pp. 1039-1060, (2006).
Rajagopal, R., and Del Castillo, E., “A Bayesian Method for robust Tolerance Control and Parameter Design”, IIE Transactions, 38(8), pp. 685-697, (2006).
Ragajopal, R., and Del Castillo, E., ``Model-Robust Process Optimization using Bayesian Model Averaging", Technometrics, 47,2,pp. 152-163, (2005).
Miro-Quesada, G., and Del Castillo, E. ``A Dual Response Approach to the Multivariate Robust Parameter Design Problem", Technometrics, 46(2), pp. 176-187, (2004).
Peterson, J.J., Cahya, S., and Del Castillo, E., "A General Approach to Confidence Regions for Optimal Factor Levels of Response Surfaces," Biometrics, 58, pp. 422-431, (2002).
Textbooks and books edited by EDC:
Del Castillo, E., Process Optimization, a Statistical Approach. Springer, August 2007. Publisher Listing.
Matlab programs accompanying the book (zipped): download here
Del Castillo, E., Statistical Process Adjustment for Quality Control, John Wiley & Sons (Probability and Statistics Series),
2002. ISBN 0-471-43574-0. Publisher listing & reviews
Files accompanying the book (zipped): download here.
Solutions manual (password-protected): download here.
Errata in first printing: download here. (updated January 2010)
Colosimo, B. and Del Castillo, E. (eds.). Bayesian Process Monitoring, Control and Optimization. CRC/Taylor & Francis,
November 2006. Publisher listing
Moyne, J., Del Castillo, E., and Hurwitz, A. (eds.) Run to Run Control in Semiconductor Manufacturing. CRC Press, 2001. Publisher Listing
Courses frequently taught
IE 433 Regression and Design of Experiments.
IE 511 Design of Experiments
IE 532 Reliability Engineering
IE 584 Time Series Control & Process Adjustment
IE 583 Response Surface Methods & Process Optimization
Awards and honors
Distinguished Professor of Industrial Engineering, Dec. 2007—present. This is an honorary title which does not carry any endowment.
Fulbright Scholar, The Netherlands (2005).
National Science Foundation Early CAREER Faculty Development Award, 1996-2001.
IIE Transactions Best Paper of the Year Award (2000).
National Council of Science of Technology of Mexico Scholarship (1989-1992)
Bank of Mexico Scholarship (1988)
Graduate tuition and academic scholarships, Arizona State University (1990-92)
Listed, who's who on Science and Engineering, 1998-to date.
CV in PDF format (updated 10/2016)
Current Funded Projects
High Dimensional Statistical Inference in Flexible Response Surface Models for Product Formulation, National Science Foundation, Principal Investigator (8/15/2016 to 7/31/2019)
Active Statistical Learning: Ensembles, Manifolds, and Optimal Experimental Design. National Science Foundation, Principal Investigator (9/1/2015 to 8/31/2018)
Old computer codes. (For newer research-related software, go to the Engineering Statistics and Machine Learning Laboratory web page.)
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