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Tanja Hagemann, M.Sc.

Lupe [1]

Tanja Hagemann is a research assistant at Service-centric Networking. In 2016 she achieved her Master's degree in mathematics at Wuppertal University with a focus on stochastic processes and 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 mainly concerned with applications of stochastic concepts in the field of machine learning technologies. Her current research is in cooperation with Deutsche Telekom, where she develops machine learning approaches for anomaly detection in self-organizing networks.

Research Interests

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

Supervised Theses

  • Henneberg, P. (2019). InfoGAN Disentanlement Framework. Bachelor Thesis, Technische Universität Berlin [2]
  • Shekhawat, D. (2019). Sentiment Analysis for Product Reviews Using Machine Learning. Master Thesis, Technische Universität Berlin [3]
  • Lux, Z. (2018). Deep Learning for Anomaly Detection in Time Series. Master Thesis, Technische Universität Berlin [4]
  • Sharkov, D. (2018). Deep Learning for Intrusion Detection. Bachelor Thesis, Technische Universität Berlin [5]
  • Yang, Y. (2018). Anomaly Detection with Time Series Analysis. Master Thesis, Technische Universität Berlin [6]


Lorbeer, B. and Deutsch, T. and Ruppel, P. and Küpper, A. (2019). Anomaly Detection with HMM Gauge Likelihood Analysis [10]. BDS2019 accepted for publication.

Victor, F. and Hagemann, T. (2019). Cryptocurrency Pump and Dump Schemes: Quantification and Detection [11]. 2019 IEEE International Conference on Data Mining Workshops (ICDMW), tbd.


Tanja Hagemann M.Sc.
Service-centric Networking
TEL 19
Ernst-Reuter-Platz 7
10587 Berlin
+49 (30) 8353-58337
e-mail query [13]

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