Dr. Frank-Peter Schilling

Work at ZHAW

Position

• Deputy Director, Centre for Artificial Intelligence (CAI)
• Group Leader, Intelligent Vision Systems (IVS), CAI
• Coordinator, PhD Programme in Data Science (with Univ. Zurich)
• Adjunct Professor, Victoria University of Wellington (NZ)

Focus

Artificial Intelligence, Deep Learning, Computer Vision, MLOps, AI for Science

Teaching

• MLOps for MSc (TSM_MachLeData, MSE, 2025-)
• Machine Learning Operations (MLOps, BSc IT+DS, 2024-)
• Computer Vision with Deep Learning (CVDL, BSc IT+DS, 2024-)
• Deep Learning (CAS Machine Intelligence, 2022-)
• Artificial Intelligence I (AI1, BSc IT, 2019-2022)
• Machine Intelligence Lab (MSc, 2019)
• AI Seminar (MSc, 2019)

Professional development teaching

• CAS Machine Intelligence
• CAS Advanced Machine Learning and Machine Learning Operations

Experience

• Senior Lecturer and Group Leader
  ZHAW
  2022 - today
• Senior Researcher
  ZHAW
  2019 - 2022

Education and Continuing education

Education

• PhD / Physics
  University of Heidelberg
  1998 - 2001
• Dipl.-Phys. (MSc equiv.) / Physics
  University of Heidelberg
  1992 - 1998

Continuing Education

• CAS University Didactics
  PH Zurich
  2024
• Certified Project Management Associate
  IPMA
  2015

Network

Membership of networks

• Confederation of Laboratories for AI Research in Europe CAIRNE
• data innovation alliance
• European Lab for Learning and Intelligent Systems ELLIS (Supporter)
• Deutsche Physikalische Gesellschaft DPG
• ZHAW Datalab
• ZHAW Digital Health Lab

Awards

EPS HEPP Prize
European Physical Society
07 / 2013

Social media

• LinkedIn
• Personal Home Page

Projects

• A data-driven solution that optimizes ankle-foot-orthopedic braces for children / Team member / ongoing
• Antimicrobial Resistance Tracker / Deputy project leader / ongoing
• SCRAI – A Think-and-Do-Tank for Responsible Development and Societal Alignment of Artificial Intelligence Systems / Deputy project leader / ongoing
• Square Kilometre Array: Mock-observations via generative deep learning (GenAI4SKA) / Deputy project leader / ongoing
• High performance AI stereo matching / Project leader / completed
• Certification program for assessing ethics of Autonomous Intelligent Systems (IEEE CertifAIEd Assessor Training) / Project leader / completed
• certAInty – A Certification Scheme for AI systems / Team member / completed
• OSR4H – Open Set Recognition for Hematology / Project leader / completed
• AI powered CBCT for improved Combination Cancer Therapy / Project leader / completed
• Square Kilometre Array: Mock-observations via generative deep learning / Deputy project leader / completed
• PhD Program in Data Science / Project leader / completed
• Standardized Data and Modeling for AI-based CoVID-19 Diagnosis Support on CT Scans / Team member / completed
• DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes / Project leader / completed
• TAILOR – Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization / Team member / completed
• RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Co-project leader / completed
• Visual Food Waste Analysis for Sustainable Kitchens / Team member / completed

Publications

Articles in scientific journal, peer-reviewed

• Denzel, P. et al. (2026) ‘Galactic alchemy : deep learning map-to-map translation in hydrodynamical simulations’, Monthly Notices of the Royal Astronomical Society, 546(4), p. stag155. doi: 10.1093/mnras/stag155.
• Barco, D. et al. (2026) ‘MInDI-3D : iterative deep learning in 3D for sparse-view cone beam computed tomography’, IEEE Access, 14, pp. 6438–6449. doi: 10.1109/access.2026.3652627.
• Amirian, M. et al. (2024) ‘Artifact reduction in 3D and 4D cone-beam computed tomography images with deep learning - a review’, IEEE Access. doi: 10.1109/ACCESS.2024.3353195.
• Amirian, M. et al. (2023) ‘Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks’, Medical Physics, 50(10), pp. 6228–6242. doi: 10.1002/mp.16405.
• Schilling, F.-P. et al. (2022) ‘Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data Science’, Archives of Data Science, Series A, 8(2). doi: 10.5445/IR/1000146422.
• Tuggener, L. et al. (2020) ‘Design patterns for resource-constrained automated deep-learning methods’, AI, 1(4), pp. 510–538. doi: 10.3390/ai1040031.

Books, peer-reviewed

• Stadelmann, T. and Schilling, F.-P. (eds) (2022) Advances in deep neural networks for visual pattern recognition. Basel: MDPI. Available at: https://www.mdpi.com/journal/jimaging/special_issues/deep_neural_network.
• Schilling, F.-P. and Stadelmann, T. (eds) (2020) Artificial neural networks in pattern recognition. Basel: MDPI. Available at: https://www.mdpi.com/journal/computers/special_issues/ANNPR2020.

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.
• 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.
• Herzig, I. et al. (2022) ‘Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT’, in Medical Physics. American Association of Physicists in Medicine, pp. e325–e326. doi: 10.1002/mp.15769.
• Simmler, N. et al. (2021) ‘A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications’, in Proceedings of the 8th SDS. IEEE, pp. 26–31. doi: 10.1109/SDS51136.2021.00012.
• Amirian, M. et al. (2021) ‘Two to trust : AutoML for safe modelling and interpretable deep learning for robustness’, in Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. Springer. doi: 10.21256/zhaw-22061.
• Schilling, F.-P. and Stadelmann, T. (eds) (2020) Artificial neural networks in pattern recognition : proceedings of the 9th IAPR TC3 workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020, 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR′20), Winterthur, Switzerland, 2-4 September 2020. Springer. doi: 10.1007/978-3-030-58309-5.

Other publications

• Denzel, P. et al. (2026) Optimization of deep learning models for radio galaxy classification. arXiv. doi: 10.48550/arXiv.2601.04773.
• Barco, D. et al. (2025) MInDI-3D : iterative deep learning in 3D for sparse-view cone beam computed tomography. arXiv. doi: 10.48550/arXiv.2508.09616.
• Frischknecht-Gruber, C. et al. (2025) ‘Assessment tool for trustworthy AI systems : operational workflows for compliance assessment with regulatory requirements’, in AI Days @ HES-SO, Geneva and Lausanne, Switzerland, 27–29 January 2025. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-32422.
• Denzel, P., Schilling, F.-P. and Gavagnin, E. (2023) ‘Map-to-map translation for SKA mock observations and cosmological simulations’, in Hammers & Nails 2023 - Swiss Edition, Ascona, Switzerland, 29 October - 3 November 2023. Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-29047.
• Weng, J. et al. (2023) ‘certAInty : a certification scheme for AI systems (Innosuisse project)’, in Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-27261.
• Denzel, P. B., Schilling, F.-P. and Gavagnin, E. (2023) ‘Deep learning the SKA : the Square Kilometer Array project’, in Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-27219.
• Amirian, M. et al. (2019) ‘Efficient deep CNNs for cross-modal automated computer vision under time and space constraints’, in ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-18357.
• Stadelmann, T. and Schilling, F.-P. (2019) ‘Deep Learning in medizinischer Diagnostik und Qualitätskontrolle’, Netzwoche. doi: 10.21256/zhaw-20163.

Oral conference contributions and abstracts

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.

Other publications

• https://fpschill.github.io/publications/
• Google Scholar
• ORCID

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