I am a postdoc in applied analysis with research ambitions located at the intersection of mean-field particle models, network dynamics, mathematics of machine learning and control theory.

Currently, I am a post-doc at the Department of Mathematics at TU Munich in the research group on dynamical systems of Christian Kuehn funded by the Schrödinger program of the Austrian Science Fund. Previously, I was a post-doc in the Chair of Dynamics, Contorl and Numerics at FAU Erlangen headed by Enrique Zuazua. I received my PhD in 2020 at the Institute of Analysis and Scientific Computing at TU Vienna in the group of Anton Arnold. In 2019, I was an invited guest of Prof. Shi Jin at Shanghai Jiao Tong University.

Large-time behaviour of mean-field equations

During my PhD, I focused on obtaining explicit large-time estimates for mean-field equations by means of entropy methods and spectral theory. I am specifically interested in including long-range particle interactions on large networks in these models, which have many fascinating real-world applications, such as synchronisation phenomena and opinion formations.

Mathematics of machine learning

The achievements of machine learning algorithms backed by huge computing resources are reshaping our lives, but still surprisingly little is understood about the fundamental mechanisms involved. As a mathematician, my job is to look inside the black box and extend the vocabulary to describe what at this point seems like magic. I am currently investigating supervised learning procedures of neural networks and their lack of robustness from an optimal control and dynamical system perspective.


"The writers who embellish a language, who treat it as an object of art, make of it at the same time a more supple instrument, more apt for rendering shades of thought."

Henri Poincaré.

In my sparetime I enjoy exploring the world through photography.