I am a Postdoctoral Researcher at the Zuse Institute Berlin, working in the Department for AI in Society, Science, and Technology, and Research Area Lead of iol.QUANT.

My scientific agenda centers on the development of tensor-based methods for the analysis, simulation, and data-driven modeling of complex dynamical systems. I integrate concepts from numerical mathematics, machine learning, and quantum mechanics, with a focus on scalable representations for high-dimensional problems.

I pursue research on tensor decompositions and tensor networks as unifying frameworks for learning, simulation, and operator-theoretic analysis, while advancing their application to quantum computing and dynamical systems theory.



Tensor-based methods and decompositions


Development and application of tensor-based 

representations for high-dimensional problems

in scientific computing and data analysis



Quantum computing and simulation


Tensor-network methods for the scalable

simulation and efficient description of

many-body quantum systems



Machine learning and data-driven modeling


Data-driven learning methods and kernel-

based techniques for the analysis and

modeling of complex systems



Dynamical systems and transfer operators


Operator-theoretic analysis of nonlinear

dynamical systems based on Koopman

and Perron–Frobenius operators



Chemical reaction networks and catalytic systems


Numerical solution of master equations

for chemical reaction networks and

heterogeneous catalytic systems    




I received my diploma degree in 2013 and defended my PhD thesis in 2017 the Freie Universität Berlin. Since then, I have worked on a range of research projects centered on complex dynamical systems, transfer operator theory, and quantum computation, with a strong emphasis on rigorous mathematical foundations and reproducible numerical methods. 

A significant part of my research is accompanied by open-source software. Methods developed in my publications are implemented in the publicly available toolbox Scikit-TT, which provides efficient algorithms for tensor decompositions and their applications.