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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, and S. M. Sanchez
Submitted to European Journal of Operational Research


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. Seven different fitting packages that run on four different platforms are compared using various data functions and data sets that reveal 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.