I’m a postdoctoral researcher in the MOSAIC Group in the Institute of Theoretical Computer Science at ETH Zurich. I’m interested in all aspects of continuous optimization and applications thereof. I’m especially intrigued by variable-metric black-box (aka gradient-free, derivative-free, direct search) algorithms that come with theoretical guarantees AND work in practice. I prefer stochastic methods over deterministic ones due to their often superior robustness in practical applications. I’m also interested in and contribute to closely related research fields such as adaptive Markov Chain Monte Carlo, Design Centering, Approximate Bayesian Computation, and Sequential Monte Carlo methods.

I have always been extremely fascinated by the beauty and complexity of biological systems. I thus try to solve problems from the field of biology. I’m especially interested in understanding configuration spaces of proteins, parameter inference in metabolic and signal transduction networks, and certain aspects in protein evolution.

I am convinced that the landscape metaphor is one of the most powerful imageries in modern science: Describing and characterizing the geometry and topography of energy landscapes, fitness landscapes, control landscapes, epigenetic landscapes, or cost landscapes is often key for understanding complex phenomena in natural, social, and theoretical science. I devoted parts of my PhD thesis to this topic and continue to work on this.

I’m also a member of the consortium of the EU FP-7 project Computational Geometric Learning. Within this project I help elucidate theoretical aspects of adaptive gradient-free algorithms and accurate high-dimensional descriptions of sampled cost and energy landscapes.