Program Postdoctoral Fellow
I am primarily interested in the application of non-traditional ideas to engineering analysis and design. At the institute, I will be researching Multi-Information Source Optimization (MISO). Traditional optimization techniques seek to find the minimum of a single objective function (black-box or convex). As complexity increases, there are typically a number of different ways to acquire knowledge about the problem at hand. The objective function may be broken down into systems that each have their own internal structure but are also interdependent, as in multi-disciplinary optimization. Alternatively, there may be a tradeoff between simulation time and accuracy, as in multi-fidelity optimization. The question is how to effectively use all of these sources of information to efficiently find a good design. An answer to this question use techniques from statistics, machine learning, game theory, and single-function optimization. Previously, I have explored the use of these techniques for analyzing epistemic uncertainty in computational fluid dynamic tools and for improving models of turbulence.
I received my B.S. in Mechanical Engineering from the University of Rochester and completed his Ph. D. in Aeronautics and Astronautics from Stanford University. I am a post-doc with the Santa Fe Institute and the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology.