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, optimization, and operator-theoretic analysis. Along these lines, they serve as a unifying language for identifying structure, developing efficient algorithms, and linking data with underlying mathematical models.

Tensor Methods	Quantum Computing	Dynamical Systems
Machine Learning	Reaction Networks	Continuous Optimization

New Paper:  On low-rank tensor train approximability for linear . . .

Scikit-TT:  Open-source tensor-train library for Python on GitHub

IOL Lab:  Interactive Optimization and Learning at TUB/ZIB