Barbara Hammer

Professor at University of Bielefeld

Bielefeld University

Germany

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Professor Barbara Hammer is a faculty member at Bielefeld University in Germany. Her research encompasses various areas including user trust in explainable AI, machine learning applied to water distribution networks, and the intersection of statistics and data science. Recent publications reflect her work on digital twins for manufacturing processes, synthetic genotype generation, and advanced forecasting techniques in water demand. Professor Hammer is recognized for her contributions to the fields of machine learning and AI systems.

Positions (1)

Barbara Hammer

University Name

University of Bielefeld

AI systems

A PhD position is available at the University of Bielefeld in the context of the SFB TRR 318 project 'Constructing Explainability', funded by the DFG. The research focuses on explanations of joint effects of components within AI systems, addressing current practices such as transfer learning, finetuning, and retrieval-augmented generation (RAG). The position is supervised by Professor Barbara Hammer and is part of a collaborative research environment. Applicants should have a strong background in computer science, mathematics, or related fields, with experience in machine learning and artificial intelligence. The position is fully funded, though specific financial details are not provided in the announcement. Interested candidates should consult the official job posting for further details and application procedures.

Barbara Hammer

Research Interests

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About

Positions (1)

AI systems

Articles (20)

Challenges and Opportunities for Statistics in the Era of Data Science

Aligning generalization between humans and machines

Generating synthetic genotypes using diffusion models

A Benchmark for Physics-informed Machine Learning of Chlorine Concentration States in Water Distribution Networks

Battle of Water Demand Forecasting

Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation

Development of a digital twin for data-driven modeling of punch-bending processes using a graphical modeling notation

EPyT-Flow: A Toolkit for Generating Water Distribution Network Data

A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles

Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis

AutoML technologies for the identification of sparse classification and outlier detection models

Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

Metric Learning with Self-Adjusting Memory for Explaining Feature Drift

Model-based explanations of concept drift

Modularity in Nervous Systems-a Key to Efficient Adaptivity for Deep Reinforcement Learning

Agnostic Explanation of Model Change based on Feature Importance

BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models

Contrasting Explanations for Understanding and Regularizing Model Adaptations

Collaborators (7)

Alexander Schulz

Phoebe Koundouri

Michal Pesta

Michael Biehl

Marianna Bolognesi

Alexander Schönhuth

André Artelt

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