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My research focus is on sequential design of experiments and modeling. I am currently working on designing a method that will balance searching for features of the response and exploring the entire space. Output from earlier points are used to keep an updated model of the surface which helps guide the search. We incorporate ideas from designs that have desirable properties, minimum energy, and Gaussian process modeling.

An example of what I’m working on can be seen on this Shiny app.


Comparison of Gaussian process modeling software

C. B. Erickson, B. E. Ankenman, S. M. Sanchez, Comparison of Gaussian process modeling software, In European Journal of Operational Research, 2017, ISSN 0377-2217,
[pdf] [arXiv]


Gaussian process fitting, or kriging, is often used to create a model from a set of data. Many available software packages do this, but we show that very different results can be obtained from different packages even when using the same data and model. We describe the parameterization, features, and optimization used by eight different fitting packages that run on four different platforms. We then compare these eight packages using various data functions and data sets, revealing that there are stark differences between the packages. In addition to comparing the prediction accuracy, the predictive variance—which is important for evaluating precision of predictions and is often used in stopping criteria—is also evaluated.