Four professorships will be or are installed in areas that have already been identified as highly relevant for our future research:
The professor, Jun. Prof. Marco Oesting, will develop and analyze novel and computationally efficient statistical methods for complex and heterogeneous data collected from a variety of sources, specifically large-scale or high-dimensional data. This addresses the following shared cross-disciplinary challenges of statistical analysis, machine learning, and high-performance computing: inverse statistical problems, functional data, stochastic control or optimization, Bayesian non-parametric statistics, and statistical aspects of machine learning. The professorship will contribute to PN 5 and PN 6.
The professor, Jun. Prof. Benedikt Ehinger, will work toward a computational foundation of human-computer interaction and advance simulation methods and computational approaches for interactive systems. Techniques to be developed include datadriven modeling and simulation of human behavior, machine learning for the automation of interface design tasks, optimization of complex multimodal interactive systems and intelligent user interfaces, and improving interaction through computational methods. The professorship will contribute to PN 2, PN 4, and PN 7.
The professor (Prof. Dr.-Ing. Felix Fritzen) will aim to incorporate data into predictive processes for challenging nonlinear engineering problems, such as architected and functional materials. Starting from the analysis of problem-specific data, novel methods and algorithms are to be developed for reducing computational complexity and improving the quality of the predictions. Research topics include model reduction via data compression, data-assisted simulation schemes, in silico data-provisioning strategies, data-guided validation and adaption of simulation models. The professorship will contribute to PN 1 and PN 3.
The professor will focus on simulation methods from a combined HPC and data perspective. This includes the merger of data analytics and machine learning with classical HPC systems, but also with future data-driven architectures like quantum computers. Computational techniques for data assimilation, data reduction, and algorithmic contributions to handle huge amounts of data will focus on flow, porous media, and particles. The professorship will contribute to PN 1 and PN 5.