Gianluca Geraci

Gianluca is a Principal Member of the Technical Staff in the Optimization and Uncertainty Quantification department at Sandia. His current research interests span uncertainty quantification, multi-fidelity modeling, dimension reduction, and data-driven approaches. Gianluca’s work at Sandia focuses on the development of computational and data-driven algorithms for reliable predictions and analyses for scientific and engineering applications. Since joining Sandia in 2016, Gianluca has worked on a variety of LDRD, NNSA, DARPA, and DOE-funded projects for a wide range of applications (e.g., internal and external aerodynamics, radiation transport, and computer networks). Gianluca received the DOE Early Career Research Award in 2024 to advance his research on enabling scientific data-driven modeling from heterogeneous, distributed, and multi-model datasets.

Gianluca is an active reviewer for several journals and served as a Guest Editor for the International Journal for Uncertainty Quantification from 2019 to 2021. He is currently serving as Chair of the Uncertainty Quantification (formerly Non-Deterministic Approaches) Technical Committee of the American Institute of Aeronautics and Astronautics (AIAA).

He worked at Stanford University (2014-2016) on the PSAAP II project, focusing on uncertainty characterization and numerical simulation confidence for irradiated particle-laden turbulent flows. Gianluca completed his Ph.D. at University of Bordeaux and INRIA (France), where he developed semi-intrusive uncertainty propagation schemes for hyperbolic conservation laws and non-intrusive sensitivity analysis and optimization under uncertainty approaches. His master’s thesis involved developing hybrid finite element/finite volume schemes for Euler equations in curvilinear coordinates.

Uncertainty Quantification and Multi-Fidelity Approaches for Predictive Science