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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]


EvolMusic: Towards Musical Adversarial Examples for Black-Box Attacks on Speech-To-Text
Citation key haggecco21
Author Motta, M. and Hagemann, T. and Fischer, S. and Assion, F.
Title of Book 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion), accepted for publication
Year 2021
Bibtex Type of Publication SNET Data
Download Bibtex entry [11]

Lorbeer, B. and Deutsch, T. and Ruppel, P. and Küpper, A. (2019). Anomaly Detection with HMM Gauge Likelihood Analysis [13]. 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
Contact [15]

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