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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.

January  27,  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.

January  19,  2022  11:00

Algorithmic recourse: from theory to practice

Abstract - In this talk I will introduce the concept of algorithmic recourse, which aims to help individuals affected by an unfavorable algorithmic decision to recover from it. First, I will show that while the concept of algorithmic recourse is strongly related to counterfactual explanations, existing methods for the later do not directly provide practical solutions for algorithmic recourse, as they do not account for the causal mechanisms governing the world. Then, I will show theoretical results that prove the need of complete causal knowledge to guarantee recourse and show how algorithmic recourse can be useful to provide novel fairness definitions that short the focus from the algorithm to the data distribution. Such novel definition of fairness allows us to distinguish between situations where unfairness can be better addressed by societal intervention, as opposed to changes on the classifiers. Finally, I will show practical solutions for (fairness in) algorithmic recourse, in realistic scenarios where the causal knowledge is only limited.

Biographie - I am a full Professor on Machine Learning at the Department of Computer Science of Saarland University in Saarbrücken (Germany), and Adjunct Faculty at MPI for Software Systems in Saarbrücken (Germany). I am a fellow of the European Laboratory for Learning and Intelligent Systems ( ELLIS), where I am part of the Robust Machine Learning Program and of the Saarbrücken Artificial Intelligence & Machine learning (Sam) Unit. Prior to this, I was an independent group leader at the MPI for Intelligent Systems in Tübingen (Germany) until the end of the year. I have held a German Humboldt Post-Doctoral Fellowship, and a “Minerva fast track” fellowship from the Max Planck Society. I obtained my PhD in 2014 and MSc degree in 2012 from the University Carlos III in Madrid (Spain), and worked as postdoctoral researcher at the MPI for Software Systems (Germany) and at the University of Cambridge (UK).


Dissertation award of TU Dortmund University for Dr. Andrea Bommert (Project A3)

We are pleased to announce that Dr. Andrea Bommert has received the TU Dortmund University Dissertation Award. She completed her dissertation entitled "Integration of Feature Selection Stability in Model Fitting", with distinction (summa cum laude) earlier this year on January 20, 2021. The dissertation award would have been presented to her on Dec. 16, 2021, during this year's annual academic celebration, but the annual celebration had to be cancelled due to the Corona pandemic.

In her work, Andrea Bommert developed measures for assessing variable selection stability as well as strategies for fitting good models using variable selection stability and successfully applied them. She is a research associate at the Department of Statistics and a member of the Collaborative Research Center 876 (Project A3).

We congratulate her on this year's dissertation award of the TU Dortmund University!

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