Dr. Frank-Peter Schilling
Dr. Frank-Peter Schilling
ZHAW
School of Engineering
Centre for Artificial Intelligence
Technikumstrasse 71
8400 Winterthur
Arbeit an der ZHAW
Tätigkeit
- Stv. Zentrumsleiter, Centre for Artificial Intelligence (CAI)
- Gruppenleiter, Intelligent Vision Systems (IVS), CAI
- Koordinator, Doktoratsprogramm Data Science (mit Univ. Zürich)
- Adjunct Professor, Victoria University of Wellington (NZ)
Arbeits- und Forschungsschwerpunkte
Künstliche Intelligenz, Deep Learning, Computer Vision, MLOps, KI für die Wissenschaft
Lehrtätigkeit
- MLOps für 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)
Lehrtätigkeit in der Weiterbildung
- CAS Advanced Machine Learning and Machine Learning Operations
- CAS Foundations of AI and Machine Learning
Berufserfahrung
- Dozent und Teamleiter
ZHAW
2022 - heute - Senior Wissenschaftlicher Mitarbeiter
ZHAW
2019 - 2022
Aus- und Weiterbildung
Ausbildung
- Dr. rer. nat. (PhD) / Physik
Universität Heidelberg
1998 - 2001 - Dipl.-Phys. (MSc equiv.) / Physik
Universität Heidelberg
1992 - 1998
Weiterbildung
- CAS Hochschuldidaktik
PH Zürich
2024 - Certified Project Management Associate
IPMA
2015
Netzwerk
Mitglied in Netzwerken
- 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
Auszeichnungen
EPS HEPP Prize
European Physical Society
07 / 2013
Social Media
Projekte
- Hohe Rechenleistung und KI an Bord von Satelliten / Projektleiter:in / laufend
- A data-driven solution that optimizes ankle-foot-orthopedic braces for children / Teammitglied / laufend
- Antibiotika-Resistenz Tracker / Stellv. Projektleiter:in / laufend
- SCRAI – A Think-and-Do-Tank for Responsible Development and Societal Alignment of Artificial Intelligence Systems / Stellv. Projektleiter:in / laufend
- GenAI4SKA: Simulierte astronomische Beobachtungen durch generative Deep Learning / Stellv. Projektleiter:in / laufend
- Performance-optimiertes KI Stereo Matching / Projektleiter:in / abgeschlossen
- Certification program for assessing ethics of Autonomous Intelligent Systems (IEEE CertifAIEd Assessor Training) / Projektleiter:in / abgeschlossen
- certAInty – A Certification Scheme for AI systems / Teammitglied / abgeschlossen
- OSR4H – Open Set Recognition for Hematology / Projektleiter:in / abgeschlossen
- AI powered CBCT for improved Combination Cancer Therapy / Projektleiter:in / abgeschlossen
- Square Kilometre Array: Simulierte astronomische Beobachtungen durch generative Deep Learning / Stellv. Projektleiter:in / abgeschlossen
- PhD Program in Data Science / Projektleiter:in / abgeschlossen
- Standardized Data and Modeling for AI-based CoVID-19 Diagnosis Support on CT Scans / Teammitglied / abgeschlossen
- DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes / Projektleiter:in / abgeschlossen
- Foundations of Trustworthy AI – Integrating Reasoning, Learning and Optimization / Teammitglied / abgeschlossen
- RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Co-Projektleiter:in / abgeschlossen
- Visual Food Waste Analysis for Sustainable Kitchens / Teammitglied / abgeschlossen
- QualitAI – Quality control of industrial products via deep learning on images / Teammitglied / abgeschlossen
Publikationen
Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed
- P. Denzel, Y. Billeter, F.-P. Schilling, and E. Gavagnin, "Galactic alchemy : deep learning map-to-map translation in hydrodynamical simulations," Monthly Notices of the Royal Astronomical Society, vol. 546, no. 4, p. stag155, Feb. 2026, doi: 10.1093/mnras/stag155.
- D. Barco et al., "MInDI-3D : iterative deep learning in 3D for sparse-view cone beam computed tomography," IEEE Access, vol. 14, pp. 6438–6449, Jan. 2026, doi: 10.1109/access.2026.3652627.
- M. Amirian, D. Barco, I. Herzig, and F.-P. Schilling, "Artifact reduction in 3D and 4D cone-beam computed tomography images with deep learning - a review," IEEE Access, Jan. 2024, doi: 10.1109/ACCESS.2024.3353195.
- M. Amirian et al., "Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks," Medical Physics, vol. 50, no. 10, pp. 6228–6242, Mar. 2023, doi: 10.1002/mp.16405.
- F.-P. Schilling et al., "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, vol. 8, no. 2, May 2022, doi: 10.5445/IR/1000146422.
- L. Tuggener et al., "Design patterns for resource-constrained automated deep-learning methods," AI, vol. 1, no. 4, pp. 510–538, Nov. 2020, doi: 10.3390/ai1040031.
Bücher, peer-reviewed
- T. Stadelmann and F.-P. Schilling, Eds., Advances in deep neural networks for visual pattern recognition. Basel: MDPI, 2022.
- F.-P. Schilling and T. Stadelmann, Eds., Artificial neural networks in pattern recognition. Basel: MDPI, 2020.
Schriftliche Konferenzbeiträge, peer-reviewed
- P. Denzel, M. A. Stadelmann, Y. L. Carillo, G. Bovet, F.-P. Schilling, and J. Bogojeska, "Intrinsic and post-hoc explainability for anomaly detection in network intrusion detection systems," in 2026 IEEE Swiss Conference on Data Science and AI (SDS), Jun. 2026, pp. 115–122, doi: 10.1109/sds70563.2026.00023.
- C. Frischknecht-Gruber et al., "AI assessment in practice : implementing a certification scheme for AI trustworthiness," in Symposium on Scaling AI Assessments (SAIA 2024), Jan. 2025, pp. 15:1–15:18, doi: 10.4230/OASIcs.SAIA.2024.15.
- P. Denzel et al., "Towards the certification of AI-based systems," in 2024 11th IEEE Swiss Conference on Data Science (SDS), Sep. 2024, pp. 84–91, doi: 10.1109/SDS60720.2024.00020.
- Y. Billeter et al., "MLOps as enabler of trustworthy AI," in 2024 11th IEEE Swiss Conference on Data Science (SDS), Sep. 2024, pp. 37–40, doi: 10.1109/SDS60720.2024.00013.
- J. Weng et al., "Certification scheme for artificial intelligence based systems," Jun. 2024, doi: 10.21256/zhaw-30549.
- I. Herzig et al., "Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT," in Medical Physics, Jun. 2022, vol. 49, no. 6, pp. e325–e326, doi: 10.1002/mp.15769.
- N. Simmler et al., "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, Jun. 2021, pp. 26–31, doi: 10.1109/SDS51136.2021.00012.
- M. Amirian et al., "Two to trust : AutoML for safe modelling and interpretable deep learning for robustness," Mar. 2021, doi: 10.21256/zhaw-22061.
- F.-P. Schilling and T. Stadelmann, Eds., Artificial neural networks in pattern recognition : proceedings of the 9th IAPR TC3 workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020. Springer, 2020.
Weitere Publikationen
- P. Denzel, M. Weiss, E. Gavagnin, and F.-P. Schilling, "Optimization of deep learning models for radio galaxy classification," arXiv, Jan. 2026. doi: 10.48550/arXiv.2601.04773.
- D. Barco et al., "MInDI-3D : iterative deep learning in 3D for sparse-view cone beam computed tomography," arXiv, Aug. 2025. doi: 10.48550/arXiv.2508.09616.
- C. Frischknecht-Gruber et al., "Assessment tool for trustworthy AI systems : operational workflows for compliance assessment with regulatory requirements," Jan. 2025, doi: 10.21256/zhaw-32422.
- P. Denzel, F.-P. Schilling, and E. Gavagnin, "Map-to-map translation for SKA mock observations and cosmological simulations," Oct. 2023, doi: 10.21256/zhaw-29047.
- J. Weng, M. Reif, R. Chavarriaga, and F.-P. Schilling, "certAInty : a certification scheme for AI systems (Innosuisse project)," Jan. 2023, doi: 10.21256/zhaw-27261.
- P. B. Denzel, F.-P. Schilling, and E. Gavagnin, "Deep learning the SKA : the Square Kilometer Array project," Jan. 2023, doi: 10.21256/zhaw-27219.
- M. Amirian, K. Rombach, L. Tuggener, F.-P. Schilling, and T. Stadelmann, "Efficient deep CNNs for cross-modal automated computer vision under time and space constraints," 2019, doi: 10.21256/zhaw-18357.
- T. Stadelmann and F.-P. Schilling, "Deep Learning in medizinischer Diagnostik und Qualitätskontrolle," Netzwoche, May 2019, doi: 10.21256/zhaw-20163.
Mündliche Konferenzbeiträge und Abstracts
P. Denzel et al., "A framework for assessing and certifying explainability of health-oriented AI systems," Nov. 2023.