Service-centric Networking

Boris Lorbeer

Office TEL 19
Building TEL

Scientific Career

After receiving a diploma in Physics from Technische Universität Berlin, Boris Lorbeer worked for a couple of companies as software developer and data scientist. He soon specialized in machine learning, which he applied to e.g. prediction analysis, pattern recognition, anomaly detection, text analysis, ad click optimization or computer vision.

In May 2015, Boris Lorbeer joined the Telekom Innovation Laboratories as a research scientist in the strategic research area of Service-centric Networking led by Prof. Dr. Axel Küpper. His research interests include machine learning, deep learning, discrete geometry and graph theory and the application of those fields to networking.

Research Interests

  • Machine Learning
  • Deep Learning
  • Discrete Geometry
  • Graph Theory


Lorbeer, B. (1995). The Einstein field equations as an infinite dimensional Hamiltonian system.  Diploma Thesis, Technische Universität Berlin



B. Lorbeer and M. Mohsen, "Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders" in Proceedings of the The Fifth IEEE International Conference on Cognitive Machine Intelligence (CogMI 2023), IEEE (accepted for publication), 2023.


B. Lorbeer and M. Botler, "Anomaly Detection with Partitioning Overfitting Autoencoder Ensembles" in Proceedings of the 14th International Conference on Machine Vision (ICMV 2021), SPIE, 2022. pp. 19-27.


B. Lorbeer, T. Deutsch, P. Ruppel and A. Küpper, "Anomaly Detection with HMM Gauge Likelihood Analysis" in BigDataService, IEEE, 2019. pp. 1-8.


B. Lorbeer, A. Kosareva, B. Deva, D. Softic, P. Ruppel and A. Küpper, "Variations on the Clustering Algorithm BIRCH" , Big Data Research, vol. 11, pp. 44-53, 2018. Elsevier.


B. Lorbeer, A. Kosareva, B. Deva, D. Softic, P. Ruppel and A. Küpper, "A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm" in Advances in Big Data, Springer, 2016. pp. 169-178.