Dipl.-Ing. Jonas Schulz

PhD Researcher

Jonas Schulz received his Diploma degree (Dipl.-Ing.) in electrical and computer engineering from the Technical University Dresden, Germany in 2022. His research focus is anticipation of human intention and how it can be leveraged in communication networks to reduce latency.


Email: jonas.schulz2@tu-dresden.de
Room: BAR I/27

Research Interests

  • Machine Learning
  • Neuroscience
  • Statistics



  • Communication Networks 1
  • Mikrorechentechnik 1



  • Internship in the field of Human Activity Recognition (Louis Fay)



Schade, Achim; Schulz, Jonas; Nguyen, Vu; Scheunert, Christian; Bodensted, Sebastian; Nguyen, Giang T.; Speidel, Stefanie; Fitzek, Frank H. P.

On the Advantages of Hand Gesture Recognition with Data Gloves for Gaming Applications Proceedings Article

In: IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), IEEE, Atlanta, GA, USA, 2023.

Links | BibTeX

Schulz, Jonas; Nguyen, Vu; Seeling, Patrick; Nguyen, Giang T.; Fitzek, Frank H. P.

Anticipatory Hand Glove: Understanding Human Actions for Enhanced Interaction Proceedings Article

In: Proceedings of the ACM international joint conference on Pervasive and Ubiquitous Computing (UbiComp), Association for Computing Machinery, 2023, (accepted for publication).


Schulz, Jonas; Radak, Hristina; Nguyen, Phuong T.; Nguyen, Giang T.; Fitzek, Frank H. P.

On the Limits of Lossy Compression for Human Activity Recognition in Sensor Networks Proceedings Article

In: The 48th IEEE Conference on Local Computer Networks (LCN), 2023, (accepted for publication).


Radak, Hristina; Scheunert, Christian; Schulz, Jonas; Nguyen, Giang T.; Fitzek, Frank H. P.

Performance Comparison of Real-Time Algorithms for IMU-Based Orientation Estimation Proceedings Article

In: European Wireless (EW), Rome, Italy, 2023, (accepted for publication).



Schulz, Jonas; Santos-Rodriguez, Raul; Poyiadzi, Rafael

Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach Proceedings Article

In: Proceedings of the Northern Lights Deep Learning Workshop, 2022.

Links | BibTeX