Professor Dr.

Andreas Weber († 15.03.2020)

Head of Multimedia, Simulation and Virtual Reality Group
 
Endenicher Allee 19A, Room
D-53115 Bonn
Germany
3.014
Phone: +49 (0) 228 73-4426
Fax: +49 (0) 228 73-4212
Email: weber@REMOVETHISPART.cs.uni-bonn.de

Bereavement

We are very deeply saddened to announce that our dear friend and esteemed colleague Prof. Dr. Andreas Weber has suddenly passed away on the evening of Sunday, 15 March 2020. Born and raised in Baden-Württemberg, he first studied mathematics at the Universities of Tübingen and Boulder, Colorado. After completing his doctorate in computer science at the University of Tübingen, he worked as a postdoc at the Institute for Computer Science at Cornell University, the University of Tübingen and the Fraunhofer Institute for Computer Graphics Research in Darmstadt. In 2001, Andreas accepted an appointment at our university as head of the research group "Multimedia, Simulation and Virtual Reality". Scientifically, he was considered an authority in the fields of physically-based modeling and simulation as well as computer algebra. Andreas had an unprecedented commitment to teaching and self-administration. Among other things, he was chairman of the examination board for computer science, chairman of the faculty group and most recently also representative of computer science in the faculty council. He was incredibly well-read, possessed an immense wealth of knowledge across different disciplines and was highly committed to the international networking of his field of study, always having an open door for visitors from all over the world. We will always remember his good-natured humor and his boundless compassion for others. We are all proud to have known him, proud of what he achieved in his life and proud of the legacy he left in our field. We will miss his presence very much.

Courses

Ongoing Projects

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Physically-based analysis and synthesis of (human) motions have a number of applications. They can help to enhance the efficiency of medical rehabilitation, to improve the understanding of motions in the realm of sports or to generate realistic animations for movies and computer games.
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The goal of the project is the anticipation of full body motions. Combining purely data-driven with physics-based modeling approaches, anticipation of full body motions on the basis of very sparse sensor signals of different nature, e.g., inertial measurement units, EMG sensors, or ground-contact sensors, will be realized. Extending the information on the physics-based layer by model-based anticipation components (including information from balance control, physical constraints) is another important objective to allow robust extrapolations from the range of motions similar to ones recorded in an existing knowledge base to new motion ranges, especially those relatedto disabilities. The anticipated whole-body motions can be used to determine the optimal robot placement for collaborative tasks and for direct entrainment and modulation of ongoing motor behavior. Symbolic labels and trajectories from affordances obtained from other projects of the research unit will be incorporated as additional a priori knowledge on motions, which will reduce computations times, will stabilize short term predictions and even open the door for the method to long-term anticipations.
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On this page, we want to introduce you to our research in the field of sonification, partially carried out in cooperation with the Institute of Sport-science and Sports at the University of Bonn and the University of Hannover.
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SYMBIONT is an interdisciplinary project ranging from mathematics via computer science to systems biology and systems medicine. The project has a clear focus on fundamental research on mathematical methods, and prototypes in software, which is in turn benchmarked against models from computational biology databases. Computational models in systems biology are built from molecular interaction networks and rate parameters resulting in large systems of differential equations. These networks are foundational for systems medicine. The currently prevailing numerical approaches shall be complemented with our novel algorithmic symbolic methods, which will address fundamental problems in this area. One important problem is that statistical estimation of model parameters is computationally expensive and many parameters are not identifiable from experimental data. In addition, there is typically a considerable uncertainty about the exact form of the mathematical model itself. The parametric uncertainty (with wide potential variations of parameters by several orders of magnitudes) leads to severe limitations of numerical approaches even for rather small and low dimensional models. Furthermore, extant model inference and analysis methods suffer from the curse of dimensionality that sets an upper limit of about ten variables to the tractable models. For those reasons, the formal deduction of principle properties of large and very large models has a very high relevance. The main goal of SYMBIONT is to combine symbolic methods with model reduction methods for the analysis of biological networks. We propose new methods for symbolic analysis, which overcome the above mentioned obstacles and therefore can be applied to large networks. In order to cope more effectively with the parameter uncertainty problem, we impose an entirely new paradigm replacing thinking about single instances with thinking about orders of magnitude. Our computational methods are diverse and involve various branches of mathematics such as tropical geometry, real algebraic geometry, theories of singular perturbations, invariant manifolds and symmetries of differential systems. The foundations and validity of our methods will be carefully secured by mathematical investigation. Corresponding computer algebra problems are NP-hard, but experiments point at their feasibility for biological networks. We have already shown that complexity parameters such as tree-width or number of distinct metastable regimes grow only slowly with size for models available in existing biological databases. We will exploit this observation to solve challenging problems in network analysis including determination of parameter regions for the existence and stability of attractors, model reduction, and characterization of qualitative dynamics of nonlinear networks. The methods developed in this project will be benchmarked against existing biological models and also against more challenging models, closer to the needs of systems and precision medicine that will be generated using biological pathways databases.

Completed Projects