Professor Christos Georgakis presented a distinguished lecture on data-driven modeling


Dr. Christos Georgakis is a Professor of Chemical and Biological Engineering at Tufts University where he has also been the Bernard M. Gordon Senior Faculty Fellow in Systems Engineering. He described two generalizations of the classical design of experiments (DoE) methodology, the long-standing data-driven modeling methodology of choice. The first generalization enables the design of experiments with time-varying inputs, called Design of Dynamic Experiments (DoDE). The second generalization enables the development of a dynamic response surface model (DRSM) when time-resolved measurements are available. He discussed how both advances are able to contribute significantly to the modeling, optimization, and understanding of processes for which a knowledge-driven model is not easily at hand. He also argued that such approaches can be widely used in developing reduced-size meta-models, for online use in existing processes.

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