The mission of the Simulation Experiments and Efficient Designs (SEED) Center for Data Farming is to advance the collaborative development and use of simulation experiments and efficient designs to provide decision makers with timely insights on complex systems and operations. The SEED approach has been to advance the state-of-the-art in conducting large-scale simulation studies, by developing and disseminating experimental designs that facilitate the exploration of complex simulation models.
The SEED philosophy is that there are three basic goals for simulation experiments:
My personal research interests lie in the area where artificial intelligence and operations research intersect. I have been particularly interested in complex adaptive systems, agent-based modeling, the application of evolutionary algorithms to simulation, and in studying the dynamics of co-evolution. In addition, I have an interest in machine learning techniques as they can be applied to agent learning and behavior. Additionally, I am interested in applying visualization and data mining techniques, as a complement to statistical modeling techniques, to increase understanding of the high-dimensional data that results from our large-scale simulation experiments.
I enjoy working with our thesis students, applying our methods to specific problems, as well as teaching and research.
M.S. Applied Mathematics (Operations Analysis Track), NPS 1997
SEED (Simulation Experiments & Efficient Designs) Center
mlmcdona <at> nps.edu