Senior data expert. Georg specializes in working with large-scale spatio-temporal graph data, focusing on an end-to-end view of data pipelines and holistic data architecture. As an experienced engineer, he has delivered impactful solutions in fraud detection, mobility analytics, and predictive maintenance in cable networks. As an architect he is working on transitioning the data platform of Magenta to the cloud.
During his doctoral studies, Georg researched inferring supply networks and analyzed the societal impact of government interventions during the COVID-19 pandemic using mobility data. He shares his enthusiasm for data as a lecturer on data science and big data, inspiring up-and-coming data talents. Additionally, Georg is an active speaker and organizer at meetups, contributing to the growth of the data science community.
Outside of his professional life, Georg enjoys rowing and hiking in the mountains, reflecting his love for nature and physical activity.
Dr. techn. Informatics, 2023
TU Wien
MSc in Business Informatics, 2018
TU Wien
BSc in Business Informatics, 2015
TU Wien
Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.
Remarkably little is known about the structure, formation, and dynamics of supply- and production networks that form one foundation of society. Neither the resilience of these networks is known, nor do we have ways to systematically monitor their ongoing change. Systemic risk contributions of individual companies were hitherto not quantifiable since data on supply networks on the firm-level do not exist with the exception of a very few countries. Here we use telecommunication meta data to reconstruct nationwide firm-level supply networks in almost real-time. We find the probability of observing a supply-link, given the existence of a strong communication-link between two companies, to be about 90%. The so reconstructed supply networks allow us to reliably quantify the systemic risk of individual companies and thus obtain an estimate for a country’s economic resilience. We identify about 65 companies, from a broad range of company sizes and from 22 different industry sectors, that could potentially cause massive damages. The method can be used for objectively monitoring change in production processes which might become essential during the green transition.
http://predictr.eu/
.https://viennadatasciencegroup.at/
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