My name is Markus Kaiser and I’m a Research Associate at the University of Cambridge and a Research Scientist at Siemens AG. I joined the group of Neil Lawrence, Carl Henrik Ek and Ferenc Huszár in the Computer Laboratory in Cambridge, UK, where we work on principled probabilistic machine learning and systems-design for real-world machine learning. As part of the learning systems group at Siemens AI, I put these thoughts into practice and apply machine learning in safety-critical industrial applications.

I’m excited about encoding expert knowledge into hierarchical probabilistic models to formulate informative prior assumptions. Informative priors facilitate inference and specify what models should learn from data, making them more data efficient and trustworthy. At Siemens, I have worked together with domain experts to create ML systems that are insightful for engineers and that can be relied on. I care about how machine learning software can be formulated to enable practitioners to easily construct probabilistic models and to embed them into existing systems. In my research, I explore how Bayesian non-parametric models can be composed to enforce abstract constraints, yield principled reasoning under uncertainty, and enable scalable and reliable inference.