The group contributes to the theoretically well-founded development of novel data analysis techniques, and to their implementation and application. For example:
- Machine learning methods for robust classification and regression of high-dimensional, multivariate data, in particular using support vector machines (SVMs), and artificial neural nets.
- Clustering methods and algorithms for outlier detection.
- Signal processing methods, in particular for the analyis of non-linear and non-stationary time series.
- EEG-based Brain-Computer Interface (BCI)
- Mental workload-detection of car drivers
- Handwritten character recognition (OCR)
- Intrusion detection in computer networks
- Classification of data coming from high-energy physics
- Analysis of DNA and protein structure
- Analysis of finanical data
- Acoustic source separation (cocktail-party problem)
- Computational chemistry