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To enable—or accelerate—computationally expensive engineering tasks, Kramer develops and analyzes new methods and algorithms based on models that reduce computational complexity. His research contributions are in multifidelity and data-driven modeling, optimization and control, uncertainty quantification, reliability-based design and design under uncertainty, with a strong focus on fluid flows.

Capsule Bio:

Prior to joining Â鶹´«Ã½, he spent four years as a Postdoctoral Associate (PostDoc) in the department of Aeronautics and Astronautics and the Aerospace Computational Design Lab (ACDL) at the Massachusetts Institute of Technology (MIT) working with Professor Karen Willcox. He received his M.Sc. (2011) and Ph.D. (2015) in Mathematics from Virginia Tech under the supervision of John A. Burns. Prior to that, he studied Mathematics in Technology and Mechanical Engineering at the University of Karlsruhe (now KIT), Germany.  He is a member of the Society for Industrial and Applied Mathematics (SIAM), and has been recognized for exceptional service as a SIAM Student Chapter president in 2014.