piwik-script

Intern
Lehrstuhl für BWL und Wirtschaftsinformatik - Prof. Dr. A. Winkelmann

Fabian Gwinner

Sprechstunden:

 

! aktuell keine Beutreuung von Studierendenarbeiten (weder Seminararbeiten noch Thesen)

 

Forschungsinteressen:

  • applied Machine Learning, Künstliche Intelligenz, Data Mining,

Forschungsprojekte:

  • DeepScan
  • PipeAI

Kurzlebenslauf:

  • Seit 2019 Research Assistant Julius-Maximilians-Universität Würzburg
  • 2014 bis 2019 Senior Solution Consultant Supply Chain Management (Consilio GmbH)
  • 2014 Master of Science Wirtschaftsinformatik an der Julius-Maximilians-Universität Würzburg
  • 2014 Werksstudent FIS GmbH Grafenrheinfeld
  • 2012 Bachelor of Science Wirtschaftsinformatik an der DHBW Mosbach (T-Systems)

Xing: https://www.xing.com/profile/Fabian_Gwinner/cv
LinkedIn: https://www.linkedin.com/in/fabian-gwinner/

Veröffentlichungen:

  • Hofmann, A., Gwinner, F., Fuchs, K., & Winkelmann, A. (2020). An Industry-Agnostic Approach for the Prediction of Return Shipments. In Americas Conference on Information Systems (AMCIS), Salt Lake City.
  • Fuchs, A., Fuchs, K., Gwinner, F., & Winkelmann, A. (2021). A Meta-Model for Real-Time Fraud Detection in ERP Systems. In Proceedings of the 54th Hawaii International Conference on System Sciences.
  • Tritscher, J., Krause, A., Schlör, D., Gwinner, F., von Mammen, S., & Hotho, A. (2021) A financial game with opportunities for fraud. In Proceedings of IEEE-CoG 2021
  • Hofmann, A., Gwinner, F., Winkelmann, A., and Janiesch, C. (2021) Security Implications of Consortium Blockchains: The Case of Ethereum Networks, 12 (2021) JIPITEC - Journal of Intellectual Property, Information Technology and E-Commerce Law. 347 para 1.  
  • Tritscher, J., Gwinner, F., Schlör, D., Krause, A., & Hotho, A. (2022). Open ERP System Data For Occupational Fraud Detection. arXiv preprint arXiv:2206.04460, https://doi.org/10.48550/arXiv.2206.04460
  • Tritscher, J., Schlör, D., Gwinner, F., Krause, A., Hotho, A. (2023). Towards Explainable Occupational Fraud Detection. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1753. Springer, Cham. https://doi.org/10.1007/978-3-031-23633-4_7
  • Schascheck, M., Gwinner, F., Winkelmann, A. (forthcoming). From Black Box to Glass Box: Evaluating the Faithfulness of Process Predictions with GCNNs,
    ECML PKDD 2023. Communications in Computer and Information Science. Springer, Cham.
  • Gwinner, F., Tomitza, C., Winkelmann, A. (forthcoming). Comparing expert systems and their explainability through similarity. 2024 DSS

Working Papers:

  • Investigating Synthetic data for Product Return prediction.
  • Transcending Homogeneity: Obtaining Subvariants in Process Mining with Machine Learning. BISE

Lehrveranstaltungen:

  • [WiSe21/22] Integrierte Informationsverarbeitung Wuestudy
  • [WiSe20/21] Integrierte Informationsverarbeitung
  • [WiSe19/20] Integrierte Informationsverarbeitung
  • [WiSe18/19] Business Software 1: Systemgestützte Unternehmensführung

Sonstiges:

  • Organizer local DSSML Paper Reading Group: Link