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Tanja Hagemann, M.Sc.

Lupe [1]

Tanja Hagemann is an external research assistant and PhD candidate at Service-centric Networking. In 2016 she achieved her Master's degree in mathematics at Wuppertal University with a focus on stochastic processes and multi-dimensional, complex analysis.

Besides her studies she gained versatile experience in data science and applied statistics during projects in a market research institute, at DLR Neustrelitz and at Wuppertal Institute for Climate, Environment and Energy. 

At SNET she is responsible for the research project ALMA [2] which aims to improve the maintenance of cloud-based infrastructures through machine learning and deep learning. Her further research is in cooperation with T-Labs in the area of Future Networks & AI [3], where she is involved in the research and development of machine learning solutions for Deutsche Telekom.

Research Interests

  • Data Science
  • Machine Learning
  • Deep Learning
  • Anomaly Detection
  • Statistical Learning Theory
  • Applied Statistics
  • Probability Theory
  • Stochastic Analysis

Supervised Theses

  • Danker, J. (2019). Analysis of LTE cell outages simulated with ns3. Bachelor Thesis, Technische Universität Berlin [4]
  • Henneberg, P. (2019). InfoGAN Disentanlement Framework. Bachelor Thesis, Technische Universität Berlin [5]
  • Rezeul, P. (2019). Natural Language Processing for System Log Analysis. Master Thesis, Technische Universität Berlin [6]
  • Shekhawat, D. (2019). Sentiment Analysis for Product Reviews Using Machine Learning. Master Thesis, Technische Universität Berlin [7]
  • Lux, Z. (2018). Deep Learning for Anomaly Detection in Time Series. Master Thesis, Technische Universität Berlin [8]
  • Sharkov, D. (2018). Deep Learning for Intrusion Detection. Bachelor Thesis, Technische Universität Berlin [9]
  • Yang, Y. (2018). Anomaly Detection with Time Series Analysis. Master Thesis, Technische Universität Berlin [10]


Hagemann, T. and Katsarou, K. (2020). Reconstruction-based anomaly detection for the cloud: A comparison on the Yahoo!Webscope S5 dataset [14]. 4th International Conference on Cloud and Big Data Computing(ICCBDC 2020). ACM, 68-75.

Link zur Publikation [15]

Hagemann, T. and Katsarou, K. (2020). A Systematic Review on Anomaly Detection for Cloud Computing Environments [16]. 3rd Artificial Intelligence and Cloud Computing Conference (AICCC 2020). ACM, 83-96.

Link zur Publikation [17]

Motta, M. and Hagemann, T. and Fischer, S. and Assion, F. (2021). EvolMusic: Towards Musical Adversarial Examples for Black-Box Attacks on Speech-To-Text [18]. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion), accepted for publication

Victor, F. and Hagemann, T. (2019). Cryptocurrency Pump and Dump Schemes: Quantification and Detection [19]. 2019 IEEE International Conference on Data Mining Workshops (ICDMW), 244–251.

Lorbeer, B. and Deutsch, T. and Ruppel, P. and Küpper, A. (2019). Anomaly Detection with HMM Gauge Likelihood Analysis [20]. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 1–8.


Tanja Hagemann M.Sc.
Service-centric Networking
TEL 19
Ernst-Reuter-Platz 7
10587 Berlin
Phone: +49 30 8353-58337
Fax: +49 30 8353 58409
E-Mail-Anfrage [22]

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