How to reach out for a thesis?

We don’t list precisely narrowed down thesis topics on the webpage, as we prefer to make an individualized plan with you!
In order to figure out whether there is a mutual fit, we recommend the following steps:

• Themes: view our publications page to get an idea for the kind of research we do. If none of these works are interesting to you, or conversely, if you have something in mind that is far out of this broad area, it will be unlikely that we have an exciting topic for you.
• Courses: if you plan to do a Master’s thesis with us, you will ideally have attended our courses, or at the minimum have acquired similar knowledge from a different source. We are excited to discuss thesis topics with you, but understand that it is hard to work on lifelong machine learning and sustainable AI systems if the foundations are yet to be obtained. For Bachelor’s theses, you will ideally have some knowledge, but of course the thesis constitutes an entry point to research and your motivation is more critical.
• Reach out: If you are as excited as we are about lifelong ML & sustainable AI and have acquired some prerequisites, reach out to us in person or by mail with a short summary of i) your degree program & academic background, ii) your motivation and interest in (one of) our topics, iii) your envisioned timeline for the thesis. You do not need a fully fleshed out proposal, we are flexible to deliberate the precise topic with you, but if you already have a concrete idea then feel free to send a short exposé.

Ongoing and completed OWL-ML theses

• Raven Hölting: Selecting Core Tokens for Large Language Model Context Compression (MSc thesis, working title, ongoing)
• Christian Penner: Unlearning Confounders in Continual ML (MSc thesis, working title, ongoing)
• Michel Ruge: Rational Neuron Regularization in Continual Learning (MSc thesis, working title, ongoing)
• Oliver Böse: Tracing the Evolution of Fairness in AI-Face Detection Using Continual Learning (BSc thesis, February 2026)
• Lara Munderloh: Continual Semantic Segmentation for Urban Surfaces (MSc thesis, December 2025)
• Ronja Schloen: Detecting Anomalies in Critical Infrastructure with Generative Models (MSc thesis, December 2025)
• Robin Menzenbach: Self-Expanding Variational Autoencoders (MSc thesis, March 2024, at TUDa)
• Nick Lemke: Distribution-Aware Replay for Continual MRI Segmentation (MSc thesis, co-supervised with Anirban Mukhopadhyay from GRIS TUDa)
• Patrick Vimr: Continual Causal Knowledge Distillation (MSc thesis, December 2023, at TUDa)
• Aleksandar Tatalovic: Continual Reinforcement Learning by Merging Models (BSc thesis, October 2023, at TUDa)
• Tobias Gockel: Knowledge Distillation for Continual Learning in Sum Product Networks (MSc thesis, September 2023, at TUDa)
• Uranik Berisha: Progressive Probabilistic Circuits (MSc thesis, May 2023, at TUDa)
• Laura Boyette: Deep Continual Learning with Intentional Forgetting (MSc thesis, May 2023, at TUDa)
• Zhanke Liu: Self-supervised Learning for Financial Time-Series Prediction via Transformers (MSc thesis, April 2023, at TUDa)
• Yves Neyraud: Active Latent Space Packing for Variational Open World Learning (MSc thesis, February 2022, at TUDa)

Student internships

Please note that we do not have an internship culture with regular open positions for undergraduate students similar to the US, but we are fortunate enough to have structured programs for students, e.g. in collaboration with Mahidol University in Thailand, or through DAAD & Erasmus, which we are happy to support given sufficient mutual interest.

• Zwe Nyan Zaw: working on continual learning centered question-answering (Summer Intern, Mahidol University, 2026)
• Pongsakorn Arayasak: working on continual learning centered question-answering (Summer Intern, Mahidol University, 2026)
• Tanadhon Dumnernpan: working on continual confounding (Summer Intern, Mahidol University, 2025)