Vincent Latzko

Dipl.-Ing. Vincent Latzko

PhD Researcher

Vincent Latzko received his Diploma degree in Electrical Engineering and Information Technology from Technische Universität Darmstadt, with a focus on the fundamental theory of electromagnetic fields ( and the numerical solution of Maxwell’s equations using a multitude of mathematical and algorithmic methods (such as DG-FEM, FDTD or BEM/Integral Equation solvers). He worked extensively in fundamental research at one of Europe’s largest particle accelerator facilities, the GSI Helmholtzcentre for Heavy Ion Research (

In his diploma thesis “Robust Time of Flight Denoising using Regression Tree Fields” with Prof. PhD Carsten Rother at the computer vision lab dresden he looked at a modern machine learning approach to restore distorted depth maps using non-parametric Gaussian Conditional Random Fields.
He afterwards focused on analytical approaches for regression problems as well as on data driven, deep learning models.


Phone: +49 351 463-35337

Research Interests

  • Machine Learning
  • Compression
  • Software Defined Networks
  • Network Slicing
  • Network Function Virtualisation


WS 18/19

  • Problem Based Learning
  • Oberseminar
  • Mentoring / Grundlagen ET

SS 18

  • Traffic Theory
  • Electrical and Magnetical Fields

WS 17/18

  • Kommunikationsnetze 3/Problem Based Learning
  • Mentoring / Grundlagen ET


  • Theme Improving Next-Generation Video Codec Segmentation using Deep Learning Techniques (Cheng Chiang Huang)
  • Learning Optimal Congestion Control Algorithms (Christian Vielhaus)
  • Optimising and Learning Coding Parameters (Christian Vielhaus)
  • Deep Reinforcement Learning for Traffic Control (Johannes Busch)
  • Telepresence Demonstrator – Remote Controlled Car via Augmented Reality (Renbing Zhang, joint supervision with Alexander Kropp)
  • Exploring resilience in-car using multipath SDN (Florian Kemser)
  • Learning Coding Schemes (Alexander Sarmanow)



Mehrabi, Mahshid ; You, Dongho ; Latzko, Vincent ; Salah, Hani ; Reisslein, Martin ; Fitzek, Frank H P

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey Journal Article

IEEE Access, 2019.