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.

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.

Tensor-based methods

Tensor-based methods

Development and application of tensor-based representations for high-dimensional problems in scientific computing and data analysis

Quantum computing

Quantum computing

Tensor-network methods for the scalable simulation and efficient description of many-body quantum systems

Dynamical systems

Dynamical systems

Operator-theoretic analysis of nonlinear dynamical systems based on Koopman and Perron–Frobenius operators

Machine learning

Machine learning

Data-driven learning methods and kernel-based techniques for the analysis and modeling of complex systems

Chemical reaction networks

Chemical reaction networks

Numerical solution of master equations for chemical reaction networks and heterogeneous catalytic systems

Continuous optimization

Continuous optimization

Continuous optimization methods for discrete and combinatorial problems, with a focus on structure-exploiting and scalable algorithms

New Paper!	Scikit-TT	IOL Lab
Faster Algorithms for Structured Matrix Multiplication via Flip Graph Search	Open-source tensor-train library for Python	Interactive Optimization and Learning Research Group