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Resilient algorithmics: How much relevant information can be extracted from my data?

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Abstract:
The digital revolution confronts us with a large volume of heterogeneous data that are generated at high speed and with significant uncertainty. Algorithms map these data spaces to solution spaces. Beside running time and memory consumption, an algorithm might be characterized by its sensitivity to the signal in the input and its robustness to input fluctuations. The achievable precision of an algorithm, i.e., the attainable resolution in output space, is determined by its capability to extract predictive information. I will advocate an information theoretic framework for algorithm analysis where an algorithm is characterized as computational evolution of a posterior distribution on the output space. Algorithms are ranked according to their information contents of the output distribution.
The method allows us to investigate complex data analysis pipelines as they occur in computational neuroscience and neurology as well as in molecular biology. I will demonstrate this design concept for algorithm validation with a statistical analysis of diffusion tensor imaging data and functional MRI data.

Type of Seminar:
Control Seminar Series
Speaker:
Prof. Joachim Buhmann
ETH Zurich
Date/Time:
Mar 27, 2017   4.30pm
Location:

ETZ E 8
Contact Person:

No downloadable files available.
Biographical Sketch:
Joachim M. Buhmann (1959) is full professor at the Department of Computer Science since 2003 and he represents the research area “Information Science and Engineering”. He studied physics at the Technical University of Munich and received a doctoral degree for his research work on artificial neural networks. After research appointments at the University of Southern California (1988-1991) and at the Lawrence Livermore National Laboratory (1991-1992) he served as a professor for applied computer science at the University of Bonn (1992-2003).
His research interests range from statistical learning theory to applications of machine learning and artificial intelligence. Research projects are focused on topics in neuroscience, biology and medical sciences, as well as signal processing and computer vision.
Joachim M. Buhmann served as president of the German Society for Pattern Recognition (DAGM e.V.) from 2009-2015. Since 2014 he acts as Vice-Rector for Study Programs at ETH Zurich. In 2017, he was elected as a member of the Swiss Academy of Technical Sciences SATW and as a research council member of the Swiss National Science Foundation.