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Dipl.-Ing. Steffen Limmer



  • Distributed and sparse signal processing
  • Model-based design of neural networks
  • Kernel Methods and Machine Learning
  • Nonlinear estimation theory


Current Projects:

  • DFG-SPP 1798 (CoSIP): Joint Design of Compressed Sensing and Network Coding for Wireless Meshed Networks

Supervised Theses:

  • Dan Mou, "Detection of Jamming based on Network Simulations with NS-3" (2015)


  • supported by an AWS in Education Research Grant award
  • received a DAAD travel grant to attend ITW 2014

Find me also on:


S. Limmer and S. Stanczak (2018). A Neural Architecture for Bayesian Compressive Sensing over the Simplex via Laplace Techniques. IEEE Transactions on Signal Processing

M. Raceala-Motoc and S. Limmer and I. Bjelakovic and S. Stanczak (2018). Distributed Machine Learning in the Context of Function Computation over Wireless Networks. 52nd Asilomar Conference on Signals, Systems and Computers 2018, Preprint.

S. Limmer and S. Stanczak (2016). Towards optimal nonlinearities for sparse recovery using higher-order statistics. IEEE International Workshop on Machine Learning For Signal Processing (MLSP)

J. Mohammadi and S. Limmer and S. Stanczak (2016). A Decentralized Eigenvalue Computation Method for Spectrum Sensing Based on Average Consensus. Frequenz

S. Limmer and J. Mohammadi and S. Stanczak (2015). A Simple Algorithm for Approximation by Nomographic Functions. Proc. 53rd Annual Allerton Conference on Communication, Control, and Computing

S. Limmer and S. Stanczak (2014). On ℓp-norm computation over multiple-access channels. Information Theory Workshop (ITW), 2014 IEEE

S. Limmer and S. Stanczak and M- Goldenbaum and R.L.G. Cavalcante (2013). Exploiting Interference for Efficient Distributed Learning in Cluster-based Wireless Sensor Networks. Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP) - Network Theory Symposium

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Dipl.-Ing. Univ. Steffen Limmer
Research Associate
Raum 412
Einsteinufer 25
10587 Berlin
+49(0)30 314-28465