Research Activity per Year


Personal Profile

Dr. Ryan King is a senior scientist in the Complex Systems Simulation & Optimization Group within the Computational Science Center. His research focuses on optimization, machine learning, and uncertainty quantification (UQ) applied to complex energy systems and turbulent flows. Ryan leads projects on physics-informed deep learning, wind farm surrogate modeling and optimization, and multi-fidelity UQ. During his Ph.D., Ryan developed adjoint optimization techniques to improve wind plant design and created a new data-driven machine learning closure for turbulence modeling in large eddy simulations. Prior to graduate school, Ryan worked as an engineer at RES Americas where he was involved in the design and construction of over 750 MW of operational wind energy. 

Research Interests

Turbulent flows

Deep learning

Uncertainty quantification 

Stochastic optimization

Adjoint methods

Professional Experience

Energy Resource Engineer & Turbine Engineer, Renewable Energy Systems Americas Inc. (2009–2012) 

Education/Academic Qualification

Bachelor, Mechanical Engineering, Massachusetts Institute of Technology

PhD, Mechanical Engineering, University of Colorado


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