357 Leonhard Building
University Park, PA 16802
Phone: 814-863-6408

Fax: 814-863-4745

(remove XXX)

Dr. Enrique del Castillo

Distinguished* Professor of Industrial & Manufacturing Engineering and

Professor of Statistics

(If you wonder what is this painting, it is Diego Rivera's Detroit Industry mural)

* Distinguished professorships at PSU's college of engineering are non-endowed; this is exclusively an honorary position.


Research Interests

Selected publications

Engineering Statistics and Machine Learning Laboratory




Current Funded Projects

Books by Dr. Castillo

Old computer codes


Research Interests

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


















Textbooks and books edited by EDC:


Matlab programs accompanying the book (zipped): download here

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)

November 2006. 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


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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This page last modified 10/28/2016