Biography

Joanna Slawinska has a comprehensive multidisciplinary background, including a Masters degree in Physics with a focus on theoretical astrophysics and stellar pulsations, a PhD in Computational Fluid Dynamics for geophysical flows, and postdoctoral research training in Applied Mathematics. Joanna is a postdoctoral associate working on a range of topics, from theoretical development of data-driven methods for dynamical systems, to their subsequent application to various fields of physics. In particular, the current focus of her work is on machine learning techniques for analysis of spatiotemporal patterns of ultrafast spectroscopical data, complex turbulent flows, and more to come.

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Research Interest

Joanna has a comprehensive multidisciplinary background, including a Masters degree in Physics with a focus on theoretical astrophysics and stellar pulsations, a PhD in Computational Fluid Dynamics for geophysical flows, and postdoctoral research training in Applied Mathematics. Joanna is a postdoctoral associate working on a range of topics, from theoretical development of data-driven methods for dynamical systems, to their subsequent application to various fields of physics. In particular, the current focus of her work is on machine learning techniques for analysis of spatiotemporal patterns of ultrafast spectroscopical data, complex turbulent flows, and more to come.