Overview
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
Fingerprint
- 1 Similar Profiles
Collaborations and Top Research Areas From the Past 5 Years
-
Conditional Distribution Estimation of Building Characteristics with Diffusion Models for Urban Energy Modeling: Article No. 117392
Sinha, S., Cortiella, A., El Kontar, R., Glaws, A., King, R. & Emami, P., 2026, In: Energy and Buildings. 361, 13 p.Research output: Contribution to journal › Article › peer-review
-
Adaptive Computing for Scale-Up Problems
Griffin, K., Egan, H., Henry de Frahan, M., Mueller, J., Vaidhynathan, D., Wald, D., Chintala, R., Doronina, O., Sitaraman, H., Young, E., King, R., Sanyal, J., Day, M. & Larsen, R., 2025, In: Computing in Science and Engineering. 27, 1, p. 28-38 11 p.Research output: Contribution to journal › Article › peer-review
2 Scopus Citations -
Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy
Davis, O., Geraci, G., Wentz, J., King, R., Cortiella, A., Rybchuk, A., Sanchez Gomez, M., Deskos, G. & Motamed, M., 2025. 22 p.Research output: Contribution to conference › Paper
-
A Neural-Network-Enhanced Parameter-Varying Framework for Multi-Objective Model Predictive Control Applied to Buildings: Article No. 100566
Wald, D., Doronina, O., Johnson, K., King, R., Sinner, M., Griffin, K., Chintala, R., Vaidhynathan, D., Sanyal, J. & Day, M., 2025, In: Energy and AI. 21, 18 p.Research output: Contribution to journal › Article › peer-review
2 Scopus Citations -
Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions: Article No. 125059
Zhang, X., Glaws, A., Cortiella, A., Emami, P. & King, R., 2025, In: Applied Energy. 380, 35 p.Research output: Contribution to journal › Article › peer-review
27 Scopus Citations