World's Best Scientists 2026 revealed!
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Computer Science
Germany
2025

D-Index & Metrics

Computer Science

D-Index
69
Citations
24315
World Ranking
1941
National Ranking
75

Research.com Recognitions

  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Eyke Hüllermeier is affiliated with Ludwig-Maximilians-Universität München in Germany. Their research primarily lies within the broad field of Computer Science, with a significant focus on subfields such as Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Computer Vision and Pattern Recognition, and Computational Theory and Mathematics.

The scientist's key research topics cover various areas in machine learning and artificial intelligence. These include:

  • Machine Learning and Data Classification
  • Machine Learning and Algorithms
  • Explainable Artificial Intelligence (XAI)
  • Bayesian Modeling and Causal Inference
  • Advanced Bandit Algorithms Research
  • Text and Document Classification Technologies
  • Data Stream Mining Techniques

Eyke Hüllermeier has contributed to numerous research papers published in notable venues. Some recent papers include:

  • "ChatGPT for good? On opportunities and challenges of large language models for education" (2023) published in Learning and Individual Differences
  • "How to measure uncertainty in uncertainty sampling for active learning" (2021) published in Machine Learning
  • "AutoML for Multi-Label Classification: Overview and Empirical Evaluation" (2021) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems" (2020) published in IEEE Transactions on Cognitive and Developmental Systems
  • "A Survey of Methods for Automated Algorithm Configuration" (2022) published in Journal of Artificial Intelligence Research

The scientist frequently collaborates with several co-authors, including Marcel Wever, Viktor Bengs, Alexander Tornede, Barbara Hammer, and Maximilian Muschalik.

Eyke Hüllermeier has published extensively in several academic venues, with the highest number of publications appearing in:

  • arXiv (Cornell University)
  • Machine Learning
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Open access LMU (Ludwid Maxmilian's Universitat Munchen)
  • International Journal of Approximate Reasoning

In addition to journal and conference publications, Eyke Hüllermeier has authored books published by Springer Science+Business Media and the Centre National de la Recherche Scientifique. Notable works include Discovery Science (2021) and Advances in Intelligent Data Analysis XX (2022) from Springer, as well as Actes de la conférence CAID 2020 (2021) published by CNRS.

Best Publications

  • ChatGPT for good? On opportunities and challenges of large language models for education

    Unknown

  • Aleatoric and epistemic uncertainty in machine learning : an introduction to concepts and methods

    Eyke Hüllermeier;Willem Waegeman

  • Multilabel classification via calibrated label ranking

    Johannes Fürnkranz;Eyke Hüllermeier;Eneldo Loza Mencía;Klaus Brinker

  • Preference Learning and Ranking by Pairwise Comparison

    Johannes Fürnkranz;Eyke Hüllermeier

  • Label ranking by learning pairwise preferences

    Eyke Hüllermeier;Johannes Fürnkranz;Weiwei Cheng;Klaus Brinker

  • FURIA: an algorithm for unordered fuzzy rule induction

    Jens Christian Hühn;Eyke Hüllermeier

  • An Approach to Modelling and Simulation of Uncertain Dynamical Systems

    Eyke Hüllermeier

  • On label dependence and loss minimization in multi-label classification

    Krzysztof Dembczyński;Willem Waegeman;Weiwei Cheng;Eyke Hüllermeier

  • Open challenges for data stream mining research

    Georg Krempl;Indre Žliobaite;Dariusz Brzeziński;Eyke Hüllermeier

  • Grouping, Overlap, and Generalized Bientropic Functions for Fuzzy Modeling of Pairwise Comparisons

    H. Bustince;M. Pagola;R. Mesiar;E. Hullermeier

  • Online clustering of parallel data streams

    Jürgen Beringer;Eyke Hüllermeier

  • Combining instance-based learning and logistic regression for multilabel classification

    Weiwei Cheng;Eyke Hüllermeier

  • Preference Learning

    Unknown

  • Pairwise preference learning and ranking

    Johannes Fürnkranz;Eyke Hüllermeier

  • Fuzzy methods in machine learning and data mining: Status and prospects

    Eyke Hüllermeier

  • A systematic approach to the assessment of fuzzy association rules

    Didier Dubois;Eyke Hüllermeier;Henri Prade

  • Preferences in AI: An overview

    Carmel Domshlak;Eyke Hüllermeier;Souhila Kaci;Henri Prade

  • ML-Plan: Automated machine learning via hierarchical planning

    Felix Mohr;Marcel Dominik Wever;Eyke Hüllermeier

  • Learning from ambiguously labeled examples

    Eyke Hüllermeier;Jürgen Beringer

  • Decision tree and instance-based learning for label ranking

    Weiwei Cheng;Jens Hühn;Eyke Hüllermeier

  • Dependent binary relevance models for multi-label classification

    Elena Montañes;Robin Senge;Jose Barranquero;José Ramón Quevedo

  • A Unified Model for Multilabel Classification and Ranking

    Klaus Brinker;Johannes Fürnkranz;Eyke Hüllermeier

Frequent Co-Authors

Johannes Fürnkranz
Johannes Fürnkranz Johannes Kepler University of Linz
Didier Dubois
Didier Dubois Paul Sabatier University
Henri Prade
Henri Prade Paul Sabatier University
Frank Hoffmann
Frank Hoffmann TU Dortmund University
Rudolf Kruse
Rudolf Kruse Otto-von-Guericke University Magdeburg
Inés Couso
Inés Couso University of Oviedo
Toon Calders
Toon Calders University of Antwerp
Floriana Esposito
Floriana Esposito University of Bari Aldo Moro
Ralf Mikut
Ralf Mikut Karlsruhe Institute of Technology
Bernard De Baets
Bernard De Baets Ghent University

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