Vita

• seit 2023: Wissenschaftlicher Mitarbeiter (Otto-von-Guericke-Universität Magdeburg)
• 2021-2023: Wissenschaftlicher Mitarbeiter (Otto-von-Guericke-Universität Magdeburg) und Softwareentwickler (METOP GmbH)
• 2019-2021: M.Sc. Biosystemtechnik (Otto-von-Guericke-Universität Magdeburg)
• 2015-2019: B.Sc. Biosystemtechnik (Otto-von-Guericke-Universität Magdeburg)

2027

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• Daniel Walke, Shweta Pandey, Gunter Saake, Alexander Jarasch, David Broneske, and Robert Heyer. Evaluation of Subgraph Querying from Databases for Mini-Batch Training of Graph Neural Networks. In Robert Wrembel, Gabriele Kotsis, A. Min Tjoa, and Ismail Khalil, editors, Database and Expert Systems Applications, pages 69–84, Cham, 2027. Springer Nature Switzerland.
   

2026

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• Daniel Walke, Daniel Steinbach, Alexander Schönhuth, Gunter Saake, David Broneske, and Robert Heyer. GraphAware: Interpretable machine learning on graphs. Discover Artificial Intelligence, 6(345), April 2026.
   

2025

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• Daniel Walke, Daniel Steinbach, Thorsten Kaiser, Alexander Schönhuth, Gunter Saake, David Broneske, and Robert Heyer. SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction. The Journal of Applied Laboratory Medicine, 2025. (PDF)
   
• Daniel Walke, Daniel Steinbach, Sebastian Gibb, Thorsten Kaiser, Paul Ahrens, Gunter Saake, David Broneske, and Robert Heyer. Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks. PLOS One, July 2025. (PDF)
   
• Rahul Mondal, Evelina Ignatova, Jonas Heinzmann, Minh Dung Do, Abhivanth Murali, Daniel Walke, Patrick Cato, Robert A. Becker, Thomas Bleistein, Gunter Saake, David Broneske, and Robert Heyer. SimKit: Similarity Graphs, Eigendecomposition and Spectral Clustering in Neo4j. In 2025 IEEE International Conference on High Performance Computing and Communications (HPCC), pages 685–691, 2025.
   

2024

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• Rahul Mondal, Evelina Ignatova, Daniel Walke, David Broneske, Gunter Saake, and Robert Heyer. Clustering graph data: the roadmap to spectral techniques. Discover Artificial Intelligence, January 2024. (PDF)
   

2023

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• Rahul Mondal, Minh Dung Do, Nasim Uddin Ahmed, Daniel Walke, Daniel Micheel, David Broneske, Gunter Saake, and Robert Heyer. Decision tree learning in Neo4j on homogeneous and unconnected graph nodes from biological and clinical datasets. BMC Medical Informatics and Decision Making, 2023. (PDF)
   
• Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth, David Broneske, Gunter Saake, and Robert Heyer. The importance of graph databases and graph learning for clinical applications. Database: The Journal of Biological Databases and Curation, 2023. Accepted review about graph databases and graph machine learning. (PDF)
   

2021

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• Daniel Walke, Kay Schallert, Prasanna Ramesh, Dirk Benndorf, Emanuel Lange, Udo Reichl, and Robert Heyer. MPA_Pathway_Tool: User-Friendly, Automatic Assignment of Microbial Community Data on Metabolic Pathways. International Journal of Molecular Sciences, 22(20, ARTICLE-NUMBER = 10992), 2021.
   

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