About Us

Mission Statement

The Analysis of Patterns in data poses one of the central problems in natural science and engineering today. Large scale experiments and computer simulations produce a huge amount of highly complex data, e.g., in proteomics of the cell, remote sensing or acoustic and visual scene analysis.

The Machine Learning methodology extracts hidden structures from these data sources with the help of a teacher signal or often also without any supervision. For example, the mass spectra from thousands of molecular biology experiments are  exploited to train complex probabilistic hidden Markov models for peptide sequencing - a data analysis approach pioneered in our group. As another example, combinations of image patches, edges and texture features are grouped by graphical models to train vision systems for image categorization and scene understanding. All these statistical models are determined by optimization algorithms which should reliably predict the hidden patterns despite experimental noise. The challenge is to develop algorithms with good generalization performance rather than greedy minimizers of empirical costs.

We currently study statistical models for data clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data. A major effort of our algorithmic and modeling work is devoted to quantify the robustness of the learned structures, i.e., to provide the data analyst with uncertainty estimates of models and methods.