Dr Christian Offen MSci PhD

• BSc Mathematics, University of Hamburg (Hamburg, Germany), 2014
• MSc Mathematics, University of Hamburg (Hamburg, Germany), 2016
• PhD in Mathematics, Massey University (Palmerston North, New Zealand), 2020

Dr Offen received a MSc in Mathematics from the University of Hamburg, Germany, in 2016, then moved to Palmerston North, New Zealand, where he was awarded a PhD in Mathematics in 2020 from Massey University. From 2020-2025 he held postdoctoral positions (Postdoctoral Researcher and Senior Researcher) at Paderborn University, Germany. In 2025 Dr Offen was appointed as an Assistant Professor at the University of Birmingham.

Dr Offen has been awarded the 2019 Hatherton Award of the Royal Society of New Zealand for his research as a PhD student. His PhD dissertation has been included in the Dean's List of Exceptional Theses of his alma mater.

Research themes

• Data-driven model identification of dynamical systems (especially Hamiltonian/Lagrangian systems and systems with symmetry)
• Gaussian Processes
• Uncertainty Quantification
• Structure-preserving numerical integration
• Lie group methods
• Backward error analysis

Research activity

Christian Offen's main research consists of topics in Geometric Numerical Integration and machine learning.

In Geometric Numerical Integration, Christian Offen derived a classification of bifurcations that occur in Hamiltonian boundary value problems. Indeed, the symplectic structure of the systems plays an important role in the stability of certain bifurcations. Only symplectic integrators can reproduce such bifurcations qualitatively correctly in a numerical bifurcation analysis. This uncovers a new application of symplectic integrators in addition to the well-known benefits in long-term integration. Furthermore, in the context of Geometric Numerical Integration, Christian Offen has worked on improving Lie group integrators related to applications in quantum mechanics and has worked on integrators that seek to preserve a Lie-Poisson structure on infinite-dimensional Lie groups.

In the context of machine learning, Christian Offen combines methods of geometry, numerical analysis and statistical learning theory to design and analyse machine learning methods to identify dynamical systems and symmetries from data. He derives convergence guarantees or guarantees for the model’s numerical conditioning, designs models that obey first principles and exploit geometric prior knowledge such as energy conservation or spatial-temporal invariances: for instance, an effect of using prior geometric knowledge is that a dynamical systems can be identified from observed solutions and then travelling wave solutions can be predicted to high accuracy even when no travelling wave solutions were present during training. His works include a systematic framework for uncertainty quantification in geometry-informed data-driven models of dynamical systems, which he uses to establish a systematic foundation of the use of geometry in machine learning.

Moreover, Christian Offen has used techniques of backward error analysis and other methods of numerical analysis to improve data-driven system identification methods. His research in this area consists of designing discrete models for learning dynamical systems from data and to relate them to continuous models. Techniques in this context can greatly improve accuracy of system identification methods and can prevent amplification of discretisation errors in data-driven predictions.