Details

You can find his google scholar profile here.

Dr. Florian Sobieczky is a senior researcher in the Data Science Lab at the Software Competence Center Hagenberg focussing on Machine Learning for Fault Detection. After obtaining his diploma in physics, Florian Sobieczky earned his doctorate in Mathematics at the University of Göttingen with a thesis about percolation theory. During his dissertation, he worked as an assistant at Berlin Technical University in teaching and research in the field of mathematical physics. As a post-doc Florian Sobieczky worked at the Mathematics Department (Institut C) of Graz’ University of Technology, at the 'Mathematisches Insitut' of the University of Jena, and as a guest-professor at the University of Colorado at Boulder (CU). From 2012 until 2015 he was a lecturer in Mathematics at the University of Denver (DU). In 2016 he joined the Software Competence Center Hagenberg.

Florian Sobieczkys research is in probability theory, particularly discrete stochastic processes. He has published in internationally renowned journals in the fields of percolation theory, random walks on graphs, and queueing theory, and organized two workshops at international conferences with proceedings published with Birkhäuser and the American Mathematical Society. The projects at SCCH to which he is typically contributing are related to the statistics of production processes. Fault detection, anomaly diagnosis and predictive maintenance are key tasks for which his skills find applicability.

As a senior scientist at SCCH, Florian Sobieczky is devoted to the reseach associated with fault detection and prescriptive maintenance in industrial production processes. The conventional application of predictive machine learning models to production data is usually enhanced if "third party" knowledge (e.g. in the form of stochastic or physical models) is included into the analysis. The typical goal is to make high performing prediction algorithms work hand in hand with interpretable classical models, thus rendering the increase of predictive performance interpretable in the sense of the underlying physical or stochastic model. In the context of Explainable Artificial Intelligence (XAI) Florian Sobieczky is involved in the project "inAIco" (Interpretable Artificial Intelligence Corrections), a BRIDGE program of the Austrian Industrial Science Foundation (FFG), which has been granted in April 2020. It is a three-year project with the aim of delivering explanations of the predictions of high performing machine learning methods for which AI acts as a "corrector" on top of the predictions of the classical model.

Selected Publications:

• F. Sobieczky, M. Shahriari Shourabi, B. Freudenthaler. A data transformation for the estimation of decay types of multivariate distributions. Procedia Manufacturing,Ivolume 42, pages 524-527, 2020.
• F. Sobieczky. Unimodularity of network representations of time-series for unbiased parameter estimation. In R. Moreno-Díaz, F. Pichler, A. Quesada-Arencibia (edt.), Computer Aided Systems Theory, LNCS, vol. 12013, p. 167-175, Springer, 2020.
• F. Sobieczky. Bounds for the annealed return probabilityon large finite percolation graphs.  Electron. J. Probab. 17, no. 79, 1–17, 2012.

Organized Workshops

• 2009: Alp-Workshop: 2016: “Random Walks, Boundaries and Spectra”
• 2016: "Unimodularity in Randomly Generated Graphs", AMS Sectional Meeting
• 2020: ISM (in Hagenberg): - Special Track "Explainable AI in Industry"