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I am currently a Postdoctoral Researcher in the field of applied mathematics in the research group of Machine Learning and Uncertainty Quantification (Prof. Dr. Clemens Heitzinger) at the Institute of Analysis and Scientific Computing, TU Wien. My research focuses on mathematical modeling, analysis and simulation of stochastic PDE systems with applications in engineering and medicine including nanotechnological sensors. My current research deals with uncertainty quantification and Bayesian inversion for PDE models, as well as optimal Bayesian experimental design and probabilistic learning methods in emerging technologies including electrical impedance tomography for medical imaging.


  • My PhD thesis has been awarded the Hannspeter-Winter-Award 2020 of TU Wien. This award is given for the best PhD thesis of a female student among all areas of science at TU Wien. (Press release by TU Wien)

Research Interests

  • Uncertainty Quantification and Bayesian Inversion
  • Numerical Methods for Stochastic Partial Differential Equations
  • Multilevel Monte-Carlo Methods
  • Analysis and Simulation of PDE Models in Nanosensors and Semiconductor Devices
  • Electrical-Impedance Tomography in Medical Imaging
  • Optimal Bayesian Experimental Design
  • Reinforcement Learning