Studies:

• 2018 MSc. Physical and Theoretical Chemistry from TU München
• 2022 PhD in Theoretical Chemistry from TU München (partially at Fritz-Haber-Institut der MPG)
• 2022-2024 Postdoctoral researcher at Fritz-Haber-Institut der MPG
• Since 2024 Postdoctoral researcher at Technische Universität München and Data Steward for the e-conversion cluster of excellence

Highlighted Papers:

https://doi.org/10.1016/j.cej.2021.134121 Here we show how to automatically idfentify effective kinetic models in a data-driven manner

https://dx.doi.org/10.2139/ssrn.4423187 Here we show how to extend classical experimental design to non-smooth problems including phase transitions

Professional career and research interests:

Coming from a theoretical chemistry and computational material science background my research interest is in method development for the accelerated development of novel materials, especially for heterogeneous catalysis. I see great potential in coupling automated experimentation techniques with statistical design of experiments to maximize the information output. I am convinced that a combination of classical experimental design and modern machine learning algorithms can help to achieve this.