**Research Data Scientist** developing analysis methods to gain better understanding of complex system dynamics under uncertainty

**Board member** @AKPIK (Working Group on Physics, Modern IT and
Artificial Intelligence| German Physical Society)

**Mentoring** women in IT & Data Science @coffeecodebreak

I am a research data scientist working in the field of computational statistics and probabilistic modelling of complex systems, previously
at the non-profit technical safety organisation Gesellschaft für Anlagen- und Reaktorsicherheit (GRS) in Garching near Munich. Before that, I did my PhD in theoretical physics on Bayesian approaches for the investigation of complex climate
time series at the Focus Area for the Dynamics of Complex Systems (DYCOS) and at the **Paleoclimate Dynamics
Group**, both based at the University of Potsdam.

Even though the scientific disciplines - complex natural systems and complex engineered systems - are very different, my research focuses on the general understanding of stochastic transition patterns occuring and influencing all kinds of complex systems. The mutual challenge thereby may be a strikingly simple question: How can I formulate a scientific hypothesis in terms of a probabilistic approach to unravel principles guiding a complex dynamic system? Or in more elegant and ambitious words:

*"If the universe is the answer, what is the question?"*

*L.M. Lederman*

From one perspective I approach this challenge by designing generic probablistic models to analyse observations of natural systems, such as the climate system. The aim is to derive statistical information that helps to decipher the recorded system evolution, e.g. paleoclimate proxy records. From another perspective I approach this challenge by performing ensembles of simulations traversing specific trajectories in the phase space to analyse the behavior of engineered complex dynamic systems in the presence of stochastic transition scenarios. For instance, an important study case is the behavior of a nuclear power plant given failure scenarios assigned with uncertainty. For details, please see my publications.

Common to all these approaches is the demand of high performance scientific computing and programing concepts. Therefore, I am additionally interested in information theoretical principles to implement sustainable and platform-independent (mainly pythonic) programs that are in general designed to be performed in multi-core environments.