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


SS 20

  • Communication Networks 2
  • ICT for Smart Grids

WS 19/20

  • Oberseminar

WS 18/19

  • Communication Networks 3
  • Problem Based Learning
  • Oberseminar
  • Mentoring / Grundlagen ET

SS 18

  • Traffic Theory
  • Electrical and Magnetical Fields

WS 17/18

  • Communication Networks 3 / Problem Based Learning
  • Mentoring / Grundlagen ET


  • Predictive Placement of Virtualized Network Functions for Urban Vehicular Routing (Preliminary Title of Master Thesis Giovanni Tancredi Iavarone, of Prof. Antonella Molinaro)
  • Learning Coding Schemes (Diploma Thesis Alexander Sarmanow)
  • Learning Optimal Congestion Control Algorithms (Diploma Thesis Christian Vielhaus)
  • Improving Next-Generation Video Codec Segmentation using Deep Learning Techniques (Master Thesis Cheng Chiang Huang)
  • Deep Reinforcement Learning for Traffic Control (Diploma Thesis Johannes Busch)
  • Optimising and Learning Coding Parameters (Study Thesis Christian Vielhaus)
  • Telepresence Demonstrator – Remote Controlled Car via Augmented Reality (Study Thesis Renbing Zhang, joint supervision with Alexander Kropp)
  • Exploring Resilience in-car using Multipath SDN (Study Thesis Florian Kemser)


  • IEEE ComSoc
  • Wiley



Mehrabi, Mahshid ; Shen, Shiwei ; Latzko, Vincent ; Wang, Yuanfei ; Fitzek, Frank H P

Energy-Aware Cooperative Offloading Framework for Inter-dependent and Delay-sensitive Tasks Inproceedings

2020 IEEE Global Communications Conference: Selected Areas in Communications: Cloud & Fog/Edge Computing, Networking and Storage (Globecom2020 SAC CCNS), Taipei, Taiwan, 2020.


Mehrabi, Mahshid ; Taghdiri, Pooria ; Latzko, Vincent ; Salah, Hani ; Fitzek, Frank H P

Accurate Energy-Efficient Localization Algorithm for IoT Sensors Inproceedings

2020 IEEE International Conference on Communications (ICC): SAC Internet of Things Track (IEEE ICC'20 – SAC-06 IoT Track), Dublin, Ireland, 2020.


Latzko, Vincent ; Vielhaus, Christian Leonard ; Fitzek, Frank H P

Use Case Driven Evolution of Network Coding Parameters Enabling Tactile Internet Applications Inproceedings

2020 IEEE International Conference on Communications (ICC): Communication Theory Symposium (IEEE ICC'20 – CT Symposium), Dublin, Ireland, 2020.


Bonetto, Riccardo ; Latzko, Vincent

Machine Learning Book Chapter

Fitzek, Frank H P; Granelli, Fabrizio ; Seeling, Patrick (Ed.): Computing in Communication Networks – From Theory to Practice, 1 , Chapter 8, Elsevier, 1, 2020, (



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.