Eyke Hüllermeier
Also known as Eyke Hullermeier
Eyke Hüllermeier is a computer scientist whose work centers on machine learning, especially preference learning and uncertainty. He earned a doctorate at Paderborn University in 1997 and held professorships at Marburg, Dortmund and Magdeburg before taking up a professorship at Paderborn in 2014. With Johannes Fürnkranz, he edited Preference Learning, published by Springer in 2010. The International Fuzzy Systems Association gave him the Zadeh Prize in 2019. As of October 2026, he heads the Chair of Artificial Intelligence and Machine Learning at LMU Munich, which he joined in 2021.
Timeline
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1997–2002
A doctorate at Paderborn
He completes a computer science doctorate at Paderborn University in 1997. After a Marie Curie fellowship at IRIT in Toulouse from 1998 to 2000, he habilitates in 2002.
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2002–2014
Chairs at Marburg and Dortmund
He holds professorships at Marburg from 2002 to 2004, at Dortmund in 2004 and at Magdeburg from 2005 to 2006. A second professorship in Marburg follows from 2007 to 2014.
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2010
Co-editing Preference Learning
Johannes Fürnkranz and he co-edit Preference Learning, published by Springer in 2010.
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2014–2021
Professor at Paderborn
A professorship at Paderborn begins in 2014 and runs until 2021.
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2019
The L.A. Zadeh Prize
In 2019, the International Fuzzy Systems Association awards him the L.A. Zadeh Prize.
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October 2019
Aleatoric and epistemic uncertainty
In October 2019, he and Willem Waegeman submit an introduction to aleatoric and epistemic uncertainty in machine learning to arXiv.
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Since 2021
Chair at LMU Munich
He moves to LMU Munich in 2021, after a professorship at Paderborn.
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As of October 2026
Heading the LMU chair
At LMU Munich, he heads the Chair of Artificial Intelligence and Machine Learning, and he is president of the European Association for Data Science.
Sources and credits
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- Eyke Hüllermeier – Wikipedia
- Contact page - Chair of Artificial Intelligence and Machine Learning - LMU München
- Preference Learning
- Eyke Hüllermeier · Harvard Data Science Review
- [1910.09457] Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
- Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods | ML Anthology
- MCML - Research Group Eyke Hüllermeier