Stefan Brunner

Projects

• Verification and Validation Framework for Safe Railway AI Systems Across Their Lifecycle / Team member / ongoing
• certAInty – A Certification Scheme for AI systems / Team member / completed
• Machine Protection and Autonomous Predictive Interlock System / Team member / completed

Publications

Written conference contributions, peer-reviewed

• Frischknecht-Gruber, C. et al. (2025) 'AI assessment in practice : implementing a certification scheme for AI trustworthiness', in Görge, R. et al. (eds) Symposium on Scaling AI Assessments (SAIA 2024). Schloss Dagstuhl – Leibniz-Zentrum für Informatik, pp. 15:1–15:18. doi: 10.4230/OASIcs.SAIA.2024.15.
• Billeter, Y. et al. (2024) 'MLOps as enabler of trustworthy AI', in 2024 11th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 37–40. doi: 10.1109/SDS60720.2024.00013.
• Denzel, P. et al. (2024) 'Towards the certification of AI-based systems', in 2024 11th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 84–91. doi: 10.1109/SDS60720.2024.00020.
• Brunner, S., Reif, M. U. and Senn, C. (2024) 'Deep Ensemble Novelty Detection : novelty detection and fault identification in multivariate data', in Yang, XS. et al. (eds) Proceedings of Ninth International Congress on Information and Communication Technology. Singapore: Springer, pp. 443–461. doi: 10.1007/978-981-97-3302-6_36.
• Weng, J. et al. (2024) 'Certification scheme for artificial intelligence based systems', in 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-30549.
• Brunner, S., Reif, M. U. and Rejzek, M. (2024) 'Improving resilience and robustness in artificial intelligence systems through adversarial training and verification', in Kołowrocki, K. and Dąbrowska, E. (eds) Advances in Reliability, Safety and Security, Part 4. Gdynia: Polish Safety and Reliability Association, pp. 39–48. doi: 10.21256/zhaw-30985.
• Brunner, S. et al. (2023) 'A comprehensive framework for ensuring the trustworthiness of AI systems', in Brito, M. P. et al. (eds) Proceeding of the 33rd European Safety and Reliability Conference. Singapore: Research Publishing, pp. 2772–2779. doi: 10.3850/978-981-18-8071-1_P230-cd.
• Brunner, S. et al. (2022) 'Deep Gaussian mixture model : a novelty detection method for time series', in Leva, M. C. et al. (eds) Proceedings of the 32nd European Safety and Reliability Conference. Singapore: Research Publishing, pp. 1291–1298. doi: 10.3850/978-981-18-5183-4_R22-12-325-cd.

Oral conference contributions and abstracts

• Brunner, S., Reif, M. U. and Rejzek, M. (2025) 'Increasing trust in medical AI systems through systematic verification : a framework for developing and verification of responsible AI', in AI Days @ HES-SO, Geneva and Lausanne, Switzerland, 27–29 January 2025.
• Denzel, P. et al. (2023) 'A framework for assessing and certifying explainability of health-oriented AI systems', in Explainable AI in Medicine Workshop, Lugano, Switzerland, 2-3 November 2023.

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