Rachel Kuske

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Research Interests

My expertise is focused on applications in nonlinear dynamics in complex settings that include noise,
heterogeneity, and complex transitions. I develop novel computational and analytical methods for models that
explain physical and biological phenomena. These approaches reveal the applicability of the models to
observed phenomena, as well as the mechanisms described by them. The innovation and power from these
approaches follows from knowledge transfer across areas of application through a dynamical perspective,
allowing new perspectives from one field to explain novel and unexpected behavior in another. Over my career
I have regularly collaborated with theoretical biologists, (bio-)physicists, mechanical engineers, and
atmospheric scientists. Such collaborations have provided the opportunity both to focus on application-relevant
results while also facilitating the cross-fertilization across domain science boundaries. Some examples of this
knowledge transfer relevant to this proposal are reflected in the publications below. They include expanding
ideas from stochastic resonance, commonly studied in neuroscience, to explain phenomenon in epidemiology
and control in pattern forming systems; pioneering analysis of tipping points – rapid transitions between states
– applied in climate and neuronal spiking patterns, and dynamical perspectives from control switches applied
to novel PDE-constrained optimization approaches for model identification.