Connor Klarkowski is a data scientist in the Uncertainty Quantification and Optimization group within the Center for Applied Scientific Computing (CASC).  His work focuses on building workflows and creating machine learning surrogate models to facilitate verification, validation, and uncertainty quantification (VVUQ) efforts.

 

Before joining the lab, he completed his graduate studies in aerospace engineering at the University of Michigan where his research focused on reduced-order and multi-fidelity modeling of computational fluid dynamics simulations with an emphasis on modeling hypersonic flows.

M.S. Aerospace Engineering, University of Michigan

B.S. Mechanical Engineering and Applied Mathematics, University of Wisconsin