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Meet the team

We are excited to be part of a wave of innovation, where machine learning is fundamentally altering numerous disciplines. This technological shift promises to expand our insights into various systems, including the human brain.

Want to join us?

If you are a student at FH Aachen and would like to work with us, you can find information on seminars, bachelor and master theses here.

Members

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Stephan Bialonski. Stephan has been a full professor of Data Science at Aachen University of Applied Sciences since 2018, after a successful tenure as a researcher at the Max Planck Society in Dresden and the Universities of Bonn and Dresden. At Aachen, Stephan established the Lab, designed and instructs courses in Deep Learning and Data Science, and became a founding member of the Institute of Data-Driven Technologies (IDT). Email: last name @ fh-aachen.de

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Niklas Grieger, Ph.D cand. Niklas is working on his Ph.D. studies as a member of the Machine Learning Lab at Aachen University of Applied Sciences in collaboration with Utrecht University (Netherlands). Compelled by the potential of self-supervised representation learning techniques, his research explores novel applications of deep learning, with a current focus on neuroscience time series data. A dedicated and invaluable member of our team, he aims to expand the capabilities of self-supervised models and further the scientific community's understanding of their possibilities. Email: last name @ fh-aachen.de

Associated Doctoral Researchers

Hanneke Scheppink

Hanneke Scheppink, Ph.D. candidate. Hanneke explores user-friendly brain-computer interfaces that decode EEG responses to visual stimulation, including moving stimuli as an alternative to conventional flickering targets. She is based at Ivan Volosyak's BCI lab at Rhine-Waal University of Applied Sciences. Her doctoral research is jointly supervised by Ivan Volosyak, Rainer Herpers, and Stephan Bialonski.

Alumni

  • Aymane Hemmouda, BSc 2026. Interpretable Quality Control for Wearable Sleep EEG.
  • Maximilian Schneider, BSc 2026. Investigation of Deep Learning Models for EEG Artifact Suppression.
  • Tim Wende, MSc 2025. Efficiency Analysis of Pretraining with Synthetic Data for U-Net-Based Automatic Music Transcription.
  • Christian Rene Thelen, BSc 2025. From Hate to Harmony: Classification and Span Detection of Candy Speech in YouTube Comments for GermEval 2025.
  • Patrick Blaneck, MSc 2025. Training and Downstream Evaluation of an EEG Foundation Model for Sleep Diagnostics.
  • Jannik Raskob, MSc 2025. High-frequency sleep staging with Deep Neural Networks.
  • Jan Zänker, MSc 2024. Detecting Mental Disorders Using EEG Foundation Models.
  • Florian Bremm, BSc 2024. Automatic Sexism Detection Using Large Language Models.
  • Tobias Bornheim, MSc 2023. Prompt Engineering vs. QLoRA Fine-Tuning: Speaker Attribution Using Large Language Models.
  • Noah Hartwig, BSc 2023. From Algebra to Number Theory: Evaluating the Mathematical Understanding of Modern Large Language Models in English and German.
  • Eric Thor, BSc 2023. Contrasting Llama-2 and LeoLM Models for Speaker Attribution in German Parliamentary Discourse: Investigating Model Efficacy and Language-Specific Prompt Influence.
  • Lars Osiewacz, MSc 2022. Robust Deep Learning Methods for Sleep Stage Classification with Noise-Contaminated Annotations.
  • Johanna Schindler, BSc 2022. Poor versus Rich: Investigating Biases in German-Language Transformer Models.
  • Andrii Mikhno, BSc 2022. Sleep Stage Classification with Automated Feature Engineering.
  • Lars Kaulen, MSc 2021. Automated Sleep Spindle Detection with Deep Learning.