Open Topics in the Area of Artificial Intelligence/Machine Learning (AI/ML)

This page lists all open topics in the area of AI/ML for communication networks. Interested students can contact the supervisor(s) to ask questions or discuss the details of a topic. It is also possible to propose your own topic in the area. An overview of AI/ML applications in networking can be found in [1].

Supervisors of our AI/ML work group: Christian Vielhaus, Johannes Busch, Vincent Latzko, Jonas Schulz

Uncertainty Quantification in Deep Learning for Transition-Aware Human Activity Recognition

Supervisor: Jonas Schulz

Human Activity Recognition (HAR) plays a crucial role in pervasive and ubiquitous computing, tactile internet as well as healthcare and entertainment. By leveraging the knowledge of human activities, network resources can be pre-allocated and services provided appropriately.
Inertial sensors are often used to acquire information about human movement patterns when these sensors are attached to the human body. Existing approaches make use of advancements in deep learning to detect and recognize human activities. Still, they often do not consider the deployment of these classifiers in the wild which comes with multiple challenges. One is, that humans are not restricted to performing activities (such as walking, running or sitting) pre-defined in a dataset. To recognise cases, in which a human is performing arbitrary activities not defined in a dataset is still an open challenge. One way to tackle this particular problem is to monitor the uncertainty of deep learning-based classifiers and to exploit this additional information in combination with thresholding to detect so-called out-of-distribution (OoD) samples. To get a truthful estimate of the classifier’s uncertainty, deep ensembles can be employed.
Motivated by this challenge, the framework of this work can be formulated as uncertainty quantification in deep learning for transition-aware human activity recognition based on ensembling methods. The thesis will involve implementing state-off-the-art deep ensembles and analysing how incorporating knowledge about the model uncertainty can be used to detect OoD samples on benchmark datasets.

  • Starting time: Immediate
  • Student and Diploma/Master thesis (with task extension)
  • Required skills: basic knowledge of machine/deep learning and signal processing
  • Desired skills: Python, Deep Learning Frameworks such as Tensorflow, Pytorch etc ..

Motivation Links:

Motion Capturing & Data Analysis in Physiotherapy

Supervisor: Giang Nguyen

The vision of the spin-off project Veiio in Dresden is to develop an intelligent suit with fully integrated sensors and actuators that uses motion capture to provide real-time vibrotactile feedback to the wearer.

We offer a diploma thesis project in Motion Capture (MoCap) Data Analysis based on Inertial measurement unit (IMU) sensors. In this project, you should develop a prototypical system that recognizes selected movement exercises and that evaluates the movement execution in real-time. Included tasks are also: acquiring the data with provided hardware, testing, and comparing different approaches for motion classification and evaluation, providing a simple interface for examining the algorithms.

You should bring:

  • Profound knowledge in Python programming
  • Experience with approaches from machine learning (e.g., scikit-learn, PyTorch)

We provide:

  • Application-oriented project that is intended to be used
  • Continuous exchange with interdisciplinary team

That sounds exciting?
Then please contact us: or Giang Nguyen


[1] A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities
[2] Network congestion