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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis

The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.

June  2,  2022  16:15

Bayesian Data Analysis for quantitative Magnetic Resonance Fingerprinting

Abstract - Magnetic Resonance Imaging (MRI) is a medical imaging technique which is widely used in clinical practice. Usually, only qualitative images are obtained. The goal in quantitative MRI (qMRI) is a quantitative determination of tissue- related parameters. In 2013, Magnetic Resonance Fingerprinting (MRF) was introduced as a fast method for qMRI which simultaneously estimates the parameters of interest. In this talk, I will present main results of my PhD thesis in which I applied Bayesian methods for the data analysis of MRF. A novel, Bayesian uncertainty analysis for the conventional MRF method is introduced as well as a new MRF approach in which the data are modelled directly in the Fourier domain. Furthermore, results from both MRF approaches will be compared with regard to various aspects.

Biographie - Selma Metzner studied Mathematics at Friedrich-Schiller-University in Jena. She then started her PhD at PTB Berlin and successfully defended her thesis in September 2021. Currently she is working on a DFG project with the title: Bayesian compressed sensing for nanoscale chemical mapping in the mid- infrared regime.

Call for Papers: Workshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro) by SFB876 - Project C3

Project C3 is proud to announce their "Workshop on Machine Learning for Astroparticle Physics and Astronomy" (ml.astro), co-located with INFORMATIK 2022. 

The workshop will be held on September 26th 2022 in Hamburg, Germany and include invited as well as contributed talks. Contributions should be submitted as full papers of 6 to 10 pages until April 30th 2022 and may include, without being limited to, the following topics:

Machine learning applications in astroparticle physics and astronomy; Unfolding / deconvolution / quantification; Neural networks and graph neural networks (GNNs); Generative adversarial networks (GANs); Ensemble Methods; Unsupervised learning; Unsupervised domain adaptation; Active class selection; Imbalanced learning; Learning with domain knowledge; Particle reconstruction, tracking, and classification; Monte Carlo simulations Further information on the timeline and the submission of contributions is provided via the workshop website: https://sfb876.tu-dortmund.de/ml.astro/

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