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

My research addresses the analysis, simulation, and data-driven modeling of complex dynamical systems, with a particular focus on scalable representations for high-dimensional problems. It combines ideas from numerical mathematics, machine learning, and quantum mechanics.

My methodological work centers on tensor networks as versatile frameworks for learning, simulation, and operator-theoretic analysis. In my work, they serve as a unifying language for identifying structure, developing efficient algorithms, and linking data with underlying mathematical models.

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
Tensor Decomposition for Non-Clifford Gate Minimization	Open-source tensor-train library for Python	Interactive Optimization and Learning Research Group