About me

I am interested in computational protein crystallization, soft matter, polymer and statistical physics. I  graduated from Leipzig University, Germany with a Ph.D. in Physics in 2011. In Leipzig I worked with Klaus Kroy. I moved to the US to do a Postdoc with David Morse at the University of Minnesota, and since 2013 I am a Postdoc with Sharon Glotzer at the University of Michigan.

What drives me

I am intrigued by solving complex many-body physical problems on the computer. This includes block copolymers, colloidal particles and biological macromolecules. I am particularly fascinated by the elegance with which the founding father of Soft Matter, Pierre Gilles de Gennes introduced deep theoretical methods into the study of matter that surrounds us in our daily lives, like polymers, foams, red blood cells, tooth paste ...  I am also indebted to Doi and Edwards' for showing how these problems can be attacked with rigor. One of my main interests is understanding the role of entropy in various self-assembly processes, both in nature and in the lab. Entropy explains the many fascinating processes by which block copolymers arrange into mesophases, liquid crystals become nematic, colloids order into soft solids, and ordered crystalline materials grow from proteins.

My background

While I am trained as a theoretical physicist, I realize that the most challenging problems are of highly interdisciplinary nature. Throughout my Ph.D. I worked with experimentalists who could make semiflexible actin networks in the lab, and we tested predictions I made for the distribution of entanglements in these materials. Back then I mainly worked with analytical methods, but I also did some computer simulations on Graphics Processing Units (GPUs). GPUs are highly efficient, parallel processors for simulations with extreme performance. My fascination with this hardware continues, as new generations of GPUs occur every 2-3 years, and often that means doubling the performance of the simulations by factors of two or more, out of the box. GPUs therefore help us to tackle ever more complex problems using computer simulations, when conventional CPUs are effectively not getting faster anymore.

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