This page lists my own materials as well as external sources that I collected and revisit. It is updated regularly.
Last updated: 19. January 2026.

My work

  • Süsslin, L. E. (2025). Teaching neural networks the rules: A selection of solutions to guarantee compliance of NNs by design [Presentation slides]. GitHub. Slides (PDF)
    Presented at the Seminar in Knowledge Representation and Reasoning: Neurosymbolic Artificial Intelligence, TU Wien (Summer term 2025).
  • Süsslin, L. E., Mäkelä, V., Alt, F., & Hirsch, L. (2025). Designing safer touch displays: Digitally distributing physical touch on a public display [Conference presentation]. MuC 2025 (Mensch und Computer). Slides (PDF) ; Video (YouTube) .
    Related publication: Proceedings of Mensch und Computer 2025 (MuC ’25).

Books (German)

  • Schadt, P. (2022). Digitalisierung. PapyRossa Verlags GmbH & Co. KG.

Academic Articles

  • Xing, P., Lu, S., & Han, Y. (2024). Federated neuro-symbolic learning. arXiv (abs/2308.15324). https://arxiv.org/abs/2308.15324
  • Zhang, H., Shen, T., Wu, F., Yin, M., Yang, H., & Wu, C. (2021). Federated graph learning: A position paper. arXiv (abs/2105.11099). https://arxiv.org/abs/2105.11099
  • Fiaschi, L., & Cococcioni, M. (2024). Informed deep hierarchical classification: A non-standard analysis inspired approach. https://doi.org/10.48550/arXiv.2409.16956
  • Stoian, M. C., Tatomir, A., Lukasiewicz, T., & Giunchiglia, E. (2024). PiShield: A PyTorch package for learning with requirements. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/1037
  • Giunchiglia, E., Tatomir, A., Stoian, M. C., & Lukasiewicz, T. (2024). CCN+: A neuro-symbolic framework for deep learning with requirements. International Journal of Approximate Reasoning, 171, 109124. https://doi.org/10.1016/j.ijar.2024.109124
  • Giunchiglia, E., & Lukasiewicz, T. (2021). Multi-label classification neural networks with hard logical constraints. CoRR, abs/2103.13427. https://arxiv.org/abs/2103.13427
  • Giunchiglia, E., Stoian, M. C., & Lukasiewicz, T. (2022). Deep learning with logical constraints. In L. D. Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) (pp. 5478–5485). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2022/767
  • Niebel, C. (2021). The impact of the General Data Protection Regulation on innovation and the global political economy. Computer Law & Security Review, 40, 105523. https://doi.org/10.1016/j.clsr.2020.105523
  • Deng, Z., & Dai, J. (n.d.). First court judgment on cross-border personal information transfer in China: Local adaptation required for multinational companies’ GDPR privacy policies. Retrieved January 30, 2025, from https://www.lexology.com/library/detail.aspx?g=0ef7b467-b539-44b0-b138-b77ef0ac3767
  • Albrecht, D. (2022). Chinese first Personal Information Protection Law in contrast to the European GDPR. Computer Law Review International, 23(1), 1–5. https://doi.org/10.9785/cri-2022-230102

Other Articles