Our research
Predicting the behavior of complex systems typically involves using models and a vast amount of data.
Mathematical sciences play a central role in the development of methods for the analysis of high-dimensional data and for the development of efficient simulation tools. For example, differential equations and optimal control theory has been immensely successful tools for modeling and optimizing industrial processes while recent development in analysis of random matrices plays a vital role in modeling antenna systems, genetics, and finance.
The researchers in the Deterministic and Stochastic Modelling (DSM) group focus on developing new theory and methods for:
- Random matrices
- Model-based tools for exploratory analysis and hidden patterns
- Numerical approximation of differential equations
- Optimal control
- Functional data analytics
- Machine learning in the context of complex models
While much of the latest research in the analysis of high-dimensional data is presented in purely theoretical and general manner we aim to take the leap from theory to real-world applications.
