Hi, I am a PhD candidate in Machine Learning at ETH Zurich and the Max Planck Institute for Intelligent Systems. I am advised by Andreas Krause and Bernhard Schölkopf.
My interests in machine learning span across various fields. I am particularly interested reinforcement learning, post-training of LLMs, and LLM evaluation. I am part of the team that developed Apertus, a fully open family of language models at 8B and 70B scales.
Beyond this, I am also interested in causal inference and causal reasoning.
I hold a Master's degree from ETH Zurich. For my Master's thesis, I worked with Charlotte Bunne and David Alvarez-Melis at Harvard on optimal transport for modeling single-cell dynamics. Before my PhD, I was a Data Scientist at QuantCo and interned at IBM.
Reach out if you want to chat!
Email: frederike.luebeck@inf.ethz.ch
Publications
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Reinforcement Learning via Self-Distillation
Jonas Hübotter, Frederike Lübeck, Lejs Behric, Anton Baumann, Marco Bagatella, Daniel Marta, Ido Hakimi, Idan Shenfeld, Thomas Kleine Buening, Carlos Guestrin, Andreas Krause
ICML, 2026
Best Paper Award at ICLR 2026 Workshop on Test-Time Updates
Model
Master Thesis Projects
I am happy to supervise Master students. You can find current available projects on the LAS Group Student Projects page. If you're interested, please reach out with your research interests, along with your up-to-date CV and academic transcript.