Max obtained his MSc in mathematics from Technical University of Munich (TUM) and Royal Institute of Technology Stockholm (KTH) in 2016. His thesis dealt with anomaly detection in robot sequence data. During his PhD, this developed into a quest for ever better neural sequence models for understanding dynamical systems. He is also interested in applying these models, which led him into examining the representation and processing of beliefs over system states.
- probabilistic sequence models
- system identification
- belief representation
- a Bayesian approach to all elements of a dynamical system
- variational inference