Markus’s research seeks to encode expert knowledge into hierarchical probabilistic models to formulate informative prior assumptions. At Siemens, he works with domain experts to create reliable machine learning systems that are insightful for engineers. In his research, he explores how Bayesian non-parametric models can be composed to enforce abstract constraints, yield principled reasoning under uncertainty, and enable scalable and reliable inference.