DANA LAVACOT

I'm a postdoctoral researcher with research interests in data driven methods for computational fluid dynamics. I earned my B.S. in mechanical engineering at UC Berkeley in 2019, where I studied aerodynamic shape optimization with deep learning in the CFD Lab. At Stanford University, I earned my M.S. in 2021 and my Ph.D. in 2025 under the advisement of Ali Mani. My Ph.D. dissertation was on turbulence modeling for Rayleigh-Taylor instability, and I also worked on forced turbulence simulations for tuning Reynolds Stress models. In my free time, I enjoy drawing, gardening, and 3D printing.

My research interests are in computational fluid dynamics (CFD), specifically in turbulence modeling. My Ph. D. thesis forcuses on turbulence modeling for Rayleigh-Taylor instability using the Macroscopic Forcing Method. As an undergraduate, I have also done research on machine learning for CFD.

Nonlocality in RTI. Lavacot et al. (2024).

Machine learning for geometric signals. Jiang & Lansigan et al. (2019).