Emily Williams

Biography

Emily is an O'Donnell Fellow at the Oden Institute working with Prof. Karen Willcox. She is interested in improving stochastic modeling capabilities using real-world data. Previously, she was a Department of Energy Computational Science Graduate Fellow at the Massachusetts Institute of Technology. She earned her PhD in Computational Science and Engineering working with Prof. David Darmofal. Her thesis was on stochastic and generative modeling for multiscale chaotic differential equations. She also collaborates with the Computational Mathematics Group at the Pacific Northwest National Laboratory.

Computational science provides a unifying framework for understanding, predicting, and assessing complex systems, many of which are governed by partial differential equations (PDEs). However, in many scientific, engineering, and societal applications, we don’t always have a complete mathematical description of the mechanisms governing system behavior. Emily's research focuses on developing stochastic and generative modeling approaches for these systems by using high-fidelity simulation and experimental data to learn unresolved dynamics and improve predictive capabilities. She is particularly interested in data-driven models that can be continually updated using new simulation and observational data, allowing digital twins to adapt in real time and support improved prediction, decision-making, and scientific discovery across a broad range of application domains.