I am a research scientist working in the field of probabilistic modelling of complex systems at the non-profit technical safety organisation Gesellschaft für Anlagen- und Reaktorsicherheit (GRS) in Garching near Munich. Previously, 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?"
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 probabilisitic information that helps to decipher the recorded system evolution, e.g. paleoclimate proxy records. From another perspective I approach this challenge by performing deterministic 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.
IUCliD workhop on Uncertainties in Data Analysis
The workshop will consist of 5 lectures and hands-on tutorials conducted by experts from different applied fields that deal with uncertainties, e.g. my lecture will be about
The tutorials will cover various topics related to uncertainty, such as applied Bayesian statistics, palaeoclimate age uncertainties, ice core uncertainties, nonlinear time series analysis, and how uncertainties in data can be modeled in a theoretical or applied sense.
The sessions are primarily intended for postgraduate, doctoral and post-doctoral researchers. Attendees are invited to contribute to the workshop with presentations of their own research, covering uncertainties or challenging investigations in (palaeo-)climate research.
Workshops on Data Analysis & Machine Learning with Python
I am looking forward to share my experience as a research data scientist in the related workshops
at the summer school Informatica Feminale 2020. Please follow the link to the individual workshops for a detailed description!
Due to the pandemic situation the event - planned to take place at the Technical University Freiburg in July 2020 - had to be postponed. By now it is planned to take place as a regular summer school in September 2020 at the University Furtwangen and will be re-organized as an online only event if necessary.