I am a postdoctoral associate in the Sandlab at MIT. My research lies at the intersection of dynamical systems theory, data science and fluid mechanics. In particular, I am interested in data-driven methods for analysis, model reduction and control of high-dimensional systems like fluids. Prior to joining MIT, I was a PhD student at UC Santa Barbara, where I worked on numerical approximation of Koopman operator for dynamical systems and its applications to fluid mechanics. In my free time, I enjoy coffee, food and hiking.

Starting in September 2019, I am a visiting scholar at Johns Hopkins hosted by Yannis Kevrekidis.

- H. Arbabi and T. Sapsis, Data-driven modeling of strongly nonlinear chaotic systems with non-Gaussian statistics, submitted, 2019.
- H. Arbabi and I. Mezić, Spectral analysis of mixing in 2D high-Reynolds flows, submitted, preprint, 2019.
- H. Arbabi and I. Mezić, Prandtl-Batchelor theorem for flows with quasi-periodic time dependence, Journal of Fluid Mechanics, preprint, 2018.