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Jakob Andersen, M.Sc.

Room: D 215

Phone: +49 (40) 42883 -2305

E-mail: andersen(at)informatik.uni-hamburg.de

 

SHORT CV

Jakob Andersen is a research associate at the Applied Software Engineering Group (MAST) at the University of Hamburg. He holds a master’s degree (M.Sc.) in Information Systems from the University of Hamburg and a bachelor’s degree in Business Informatics (best graduate) from the HAW-Hamburg. Currently he is part of the Forum 4.0 project.       

 

RESEARCH INTERESTS

  • Human-in-the-Loop Machine Learning
  • Uncertainty Awareness
  • Natural Language Processing

Projects

  • Forum 4.0

Publications

  • Andersen, J. S., Zukunft, O., Maalej, W. (2021). REM: Efficient Semi-Automated Real-Time Moderation of Online Forums. In Proceedings of the Software Demonstrations of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (to appear).
  • Haering, M., Andersen, J. S., Biemann, C., Loosen, W., Milde, B., Pietz, T., Stoecker, C., Wiedemann, G., Zukunft, O., Maalej, W. (2021). Forum 4.0: An Open-Source User Comment Analysis Framework. In Proceedings of the Software Demonstrations of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 63-70).
  • Andersen, J. S., Schöner, T., & Maalej, W. (2020). Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 5541-5546).
  • Tropmann-Frick, M., & Andersen, J. S. (2019). Towards Visual Data Science-An Exploration. In Proceedings of the International Conference on Human Interaction and Emerging Technologies (pp. 371-377). Springer
  • Andersen, J. S. (2019). A User Centric Visual Analytics Framework for News Discussions. In Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference.
  • Andersen, J. S., & Zukunft, O. (2016). Semi-clustering that scales: an empirical evaluation of GraphX. In 2016 IEEE International Congress on Big Data (BigData Congress) (pp. 333-336). IEEE.
  • Andersen, J. S., & Zukunft, O. (2016). Evaluating the scaling of graph-algorithms for big data using graphx. In 2016 2nd International Conference on Open and Big Data (OBD) (pp. 1-8). IEEE.