Barbara Hammer

Barbara Hammer

bhammer@techfak.uni-bielefeld.de
Institution: Bielefeld University
Department: Machine Learning Group
Position: Head of the Hammer Lab
https://hammer-lab.techfak.uni-bielefeld.de/

Sustainable AI, Hybrid Systems, Computational Learning Theory, Applications for Critical Infrastructure

Incremental learning and learning with drift, learning from limited data set, learning with label noise or few labels, reliability of learning, efficient deep learning, fairness of ML; explainable ML, readability of learning, learning with structured data, prototype-based models, graph neural networks, recurrent and recursive models; biomedical applications

  • Advances in artificial neural networks, machine learning and computational intelligence (2026)

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    Published at: Neurocomputing (Volume: 700)
    DOI: 10.1016/j.neucom.2026.134511

  • Analysis of Search Heuristics in the Multi-Armed Bandit Setting (2026)

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    Published at: Gecco 2026 Companion Proceedings of the 2026 Genetic and Evolutionary Computation Conference
    DOI: 10.1145/3795101.3805359

  • The effect of data poisoning on counterfactual explanations (2026)

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    Published at: Information Fusion (Volume: 132)
    DOI: 10.1016/j.inffus.2026.104237

  • Pruning Federated Models Through Loss Landscape Analysis and Client Agreement Scoring (2026)

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    Published at: IEEE Internet of Things Journal (Volume: 13)
    DOI: 10.1109/JIOT.2026.3686028

  • Evaluating automatic label noise detection in 3D segmentation with realistic label noise (2026)

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    Published at: Neurocomputing (Volume: 684)
    DOI: 10.1016/j.neucom.2026.133587

  • Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–bénard convection (2026)

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    Published at: Neurocomputing (Volume: 679)
    DOI: 10.1016/j.neucom.2026.133201

  • A practical guide to streaming continual learning (2026)

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    Published at: Neurocomputing (Volume: 674)
    DOI: 10.1016/j.neucom.2026.132951

  • Learning in federated and dynamic environments: A tutorial on challenges, trends, and practical strategies (2026)

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    Published at: Neurocomputing (Volume: 672)
    DOI: 10.1016/j.neucom.2026.132671

  • Interpretable event diagnosis in water distribution networks (2026)

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    Published at: Intelligent Systems with Applications (Volume: 29)
    DOI: 10.1016/j.iswa.2025.200621

  • Uncertainty-Aware Remaining Lifespan Prediction from Images (2026)

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    Published at: Lecture Notes in Computer Science (Volume: 16397 LNCS)
    DOI: 10.1007/978-3-032-14495-9_34