Prof. Dr. Heinke Hihn
Professor of B.Sc. Software Development
Profile
Dr Hihn Heinke works in the field of cybersecurity with a focus on data-driven methods, intelligent systems, and secure information processing. His background includes a PhD in machine learning and experience in both academic research and industry, including applied work on semantic search and large-scale data systems. His research addresses efficient algorithms, robustness, and reliability of machine-learning-based systems in security-relevant contexts. His work emphasizes analytical approaches to protecting information systems and ensuring trustworthy, scalable digital infrastructures.
Research Areas
Machine Learning Algorithms
Semantic Search and Information Retrieval
Robust and Reliable AI Systems
Data-Driven Security Methods
Trustworthy Intelligent Systems
Publikationen und Vorträge
Hihn, H., Dittrich, D. A. V., Jeske, C., Sobral, C. C., Pais, H., & Lochmann, T. (2025). Ontology-aligned embeddings for data-driven labour market analytics. In Proceedings of the SIG Knowledge Management Workshop (FG WM) at KI 2025, Potsdam, Germany.
Hihn, H., & Braun, D. A. (2024). Online continual learning through unsupervised mutual information maximization. Neurocomputing, 578, 127422.
Hihn, H., & Braun, D. A. (2023). Hierarchically structured task-agnostic continual learning. Machine Learning, 112(2), 655–686.
Thiam, P., Hihn, H., Braun, D. A., Kestler, H. A., & Schwenker, F. (2021). Multi-modal pain intensity assessment based on physiological signals: A deep learning perspective. Frontiers in Physiology, 12, 720464.
Hihn, H., & Braun, D. A. (2021). Mixture-of-variational-experts for continual learning. In ICLR 2022 Workshop on Agent Learning in Open-Endedness.