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Audrey Gaymann earned her PhD in Aeronautics from Imperial College London, specializing in topology optimization for fluid-structure interaction. Her research focused on developing a novel solver to optimize engineering component shapes based on performance, addressing uncertainty quantification in input parameters (e.g., fluid velocity) and the presence of competing fluids, particularly in valve designs. In collaboration with GE Aviation, she worked on valve design optimization and spent several months at NASA to work on uncertainty quantification and failure probability estimation, concentrating on both aleatoric and epistemic uncertainties in a Sandia challenge. Her PhD was sponsored by GE Oil and Gas and the Smith Institute of Mathematics.

Following her PhD, Audrey received an EPSRC Doctoral Prize Fellowship, allowing her to continue her research at Imperial College London for a year. During this postdoctoral work, she explored heuristic methods that incorporated machine learning and neural networks for topology optimization.

In 2019, Audrey co-founded the start-up TOffeeX, which commercialized her research, as well as that of her lab at ICL, into a software solution for industry. Under her leadership as co-founder, the company successfully raised $1.1 million in pre-seed funding and $6.7 million in a Series A round. She also served as the principal investigator (PI) on two Innovate UK projects totaling $700k and as a co-PI on another $1.1 million project.

After this entrepreneurial success, Audrey decided to return to academia, focusing on her passion for uncertainty quantification and machine learning techniques in real-world applications. She is currently part of NASA's ACCESS project, analyzing re-entry systems into Titan’s atmosphere. Her research involves estimating the probability of failure for the forebody heatshield, specifically focusing on bondline temperature under high-dimensional conditions with epistemic uncertainty affecting the data.