TU Berlin

Information Systems EngineeringMichael Gebauer

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Michael Gebauer, M.Sc.

Lupe

M.Sc. in Business Administration

Michael has been working as a research assistant at the  Information Systems Engineering chair at TU Berlin since 03/2021. Previously, he worked for three years as a Data Scientist with a focus on Natural Language Processing and Software Engineer in consulting and auditing. He earned his M.Sc. in Business Administration from Friedrich Schiller University Jena in 2017, specializing in statistics, as well as finance and risk management. In his master's thesis, he dealt with the practical application of Bayes Networks. He completed his bachelor´s degree at the Brandenburg University of Applied Sciences


In his research, Michael is involved in the Daskita project, which deals with the processing of complex data sets in order to protect personal data.

Research Interests:

  • Applied Machine Learning in Information Systems
  • Probabilistic Models and Neural Networks
  • Outlier- and Out-of-Distribution Detection

Publications

Max Lübbering and Michael Gebauer and Rajkumar Ramamurthy and Maren Pielka and Christian Bauckhage and Rafet Sifa (2021). Utilizing representation learning for robust text classification under datasetshift. Proceedings of the Conference ``Lernen, Wissen, Daten, Analysen'', CEUR Workshop Proceedings


Max Lübbering and Maren Pielka and Kajaree Das and Michael Gebauer and Rajkumar Ramamurthy and Christian Bauckhage and Rafet Sifa (2021). Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis. ESANN 2021 proceedings. Ciaco - i6doc.com.

Link to publication

Max Lübbering and Michael Gebauer and Rajkumar Ramamurthy and Maren Pielka and Christian Bauckhage and Rafet Sifa (2021). Decoupling Autoencoders for Robust One-vs-Rest Classification. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 1–10.

Link to publication

Max Lübbering and Michael Gebauer and Rajkumar Ramamurthy and Rafet Sifa and Christian Bauckhage (2021). Supervised Autoencoder Variants for End to End Anomaly Detection. Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part II, 566–581.

Link to publication

Max Lübbering and Rajkumar Ramamurthy and Michael Gebauer and Thiago Bell and Rafet Sifa and Christian Bauckhage (2020). From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders. International Conference on Artificial Neural Networks, 27–38.

Link to publication

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