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About us Faculty of Science and Engineering Data Science & Systems Complexity (DSSC) DSSC COFUND programme

Supervisor: Prof. dr. Michael Biehl (JBI)

With a background in theoretical and computational physics, my own research has always been centered on interdisciplinary areas such as the theory and application of neural networks or the simulation of complex dynamical systems.

Many scientific disciplines are experiencing an impressive increase of the rate at which data are acquired. Several methodological challenges can be identified immediately: High-dimensional, complex data sets need to be made accessible by means of compression and visualization techniques. Problems related to clustering, classification, or regression trigger the search for efficient practical schemes for the selection of relevant features in the data. The integration of information from various sources poses the question of how to combine and relate heterogeneous data sets efficiently.

These challenges are also in the focus of my own research interests. My recent activities are centered on computational modeling in general and, more specifically, the machine learning based analysis of complex, high-dimensional data sets. Three major, inter-dependent aspects of my work can be identified: Theoretical studies, algorithm development, and applications. The objective of theoretical investigations and simulation of machine learning processes is to achieve a thorough understanding of phenomena and problems, which can occur in practical applications. The obtained insights facilitate the development of efficient computational approaches and algorithms for the analysis of real world data. Ultimately, real world applications serve as a test-bed for newly developed methods.

Recently, the application of prototype-based models in the bio-medical domain have played an important role. The success of this line of research clearly hinges on the availability of challenging data sets and practical problems. Close contacts and intense exchange with domain experts are essential for the identification of relevant questions and the evaluation of the developed methods.

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Last modified:12 May 2017 4.28 p.m.