Dr. Frank-Peter Schilling

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)

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 Machine Intelligence

Berufserfahrung

• Dozent
  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

• LinkedIn
• Persönliche Home Page

Projekte

• 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
• TAILOR – 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

• Denzel, P., Billeter, Y., Schilling, F.-P., & Gavagnin, E. (2026). Galactic alchemy : deep learning map-to-map translation in hydrodynamical simulations. Monthly Notices of the Royal Astronomical Society. https://doi.org/10.1093/mnras/stag155
• Barco, D., Stadelmann, M., Oswald, M., Herzig, I., Lichtensteiger, L., Paysan, P., Peterlik, I., Walczak, M., Menze, B., & Schilling, F.-P. (2026). MInDI-3D : iterative deep learning in 3D for sparse-view cone beam computed tomography. IEEE Access, 14, 6438–6449. https://doi.org/10.1109/access.2026.3652627
• Amirian, M., Barco, D., Herzig, I., & Schilling, F.-P. (2024). Artifact reduction in 3D and 4D cone-beam computed tomography images with deep learning - a review. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3353195
• Amirian, M., Montoya-Zegarra, J. A., Herzig, I., Eggenberger Hotz, P., Lichtensteiger, L., Morf, M., Züst, A., Paysan, P., Peterlik, I., Scheib, S., Füchslin, R. M., Stadelmann, T., & Schilling, F.-P. (2023). Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks. Medical Physics, 50(10), 6228–6242. https://doi.org/10.1002/mp.16405
• Schilling, F.-P., Flumini, D., Füchslin, R. M., Gavagnin, E., Geller, A., Quarteroni, S., & Stadelmann, T. (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). https://doi.org/10.5445/IR/1000146422
• Tuggener, L., Amirian, M., Benites de Azevedo e Souza, F., von Däniken, P., Gupta, P., Schilling, F.-P., & Stadelmann, T. (2020). Design patterns for resource-constrained automated deep-learning methods. Ai, 1(4), 510–538. https://doi.org/10.3390/ai1040031

Bücher, peer-reviewed

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

Schriftliche Konferenzbeiträge, peer-reviewed

• Frischknecht-Gruber, C., Denzel, P., Reif, M., Billeter, Y., Brunner, S., Forster, O., Schilling, F.-P., Weng, J., & Chavarriaga, R. (2025). AI assessment in practice : implementing a certification scheme for AI trustworthiness [Conference paper]. In R. Görge, E. Haedecke, M. Poretschkin, & A. Schmitz (Eds.), Symposium on Scaling AI Assessments (SAIA 2024) (pp. 15:1–15:18). Schloss Dagstuhl – Leibniz-Zentrum für Informatik. https://doi.org/10.4230/OASIcs.SAIA.2024.15
• Billeter, Y., Denzel, P., Chavarriaga, R., Forster, O., Schilling, F.-P., Brunner, S., Frischknecht-Gruber, C., Reif, M. U., & Weng, J. (2024). MLOps as enabler of trustworthy AI [Conference paper]. 2024 11th IEEE Swiss Conference on Data Science (SDS), 37–40. https://doi.org/10.1109/SDS60720.2024.00013
• Denzel, P., Brunner, S., Billeter, Y., Forster, O., Frischknecht-Gruber, C., Reif, M. U., Schilling, F.-P., Weng, J., Chavarriaga, R., Amini, A., Repetto, M., & Iranfar, A. (2024). Towards the certification of AI-based systems [Conference paper]. 2024 11th IEEE Swiss Conference on Data Science (SDS), 84–91. https://doi.org/10.1109/SDS60720.2024.00020
• Weng, J., Denzel, P., Reif, M. U., Schilling, F.-P., Billeter, Y., Frischknecht-Gruber, C., Brunner, S., Chavarriaga, R., Repetto, M., & Iranfar, A. (2024, June). Certification scheme for artificial intelligence based systems. 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. https://doi.org/10.21256/zhaw-30549
• Herzig, I., Paysan, P., Scheib, S., Züst, A., Schilling, F.-P., Montoya, J., Amirian, M., Stadelmann, T., Eggenberger Hotz, P., Füchslin, R. M., & Lichtensteiger, L. (2022). Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT [Conference poster]. Medical Physics, 49(6), e325–e326. https://doi.org/10.1002/mp.15769
• Simmler, N., Sager, P., Andermatt, P., Chavarriaga, R., Schilling, F.-P., Rosenthal, M., & Stadelmann, T. (2021). A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications [Conference paper]. Proceedings of the 8th SDS, 26–31. https://doi.org/10.1109/SDS51136.2021.00012
• Amirian, M., Tuggener, L., Chavarriaga, R., Satyawan, Y. P., Schilling, F.-P., Schwenker, F., & Stadelmann, T. (2021, March). Two to trust : AutoML for safe modelling and interpretable deep learning for robustness. Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. https://doi.org/10.21256/zhaw-22061
• Artificial neural networks in pattern recognition : proceedings of the 9th IAPR TC3 workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020. (2020). In F.-P. Schilling & T. Stadelmann (Eds.), 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR′20), Winterthur, Switzerland, 2-4 September 2020. Springer. https://doi.org/10.1007/978-3-030-58309-5

Weitere Publikationen

• Denzel, P., Weiss, M., Gavagnin, E., & Schilling, F.-P. (2026). Optimization of deep learning models for radio galaxy classification. arXiv. https://doi.org/10.48550/arXiv.2601.04773
• Barco, D., Stadelmann, M., Oswald, M., Herzig, I., Lichtensteiger, L., Paysan, P., Peterlik, I., Walczak, M., Menze, B., & Schilling, F.-P. (2025). MInDI-3D : iterative deep learning in 3D for sparse-view cone beam computed tomography. arXiv. https://doi.org/10.48550/arXiv.2508.09616
• Frischknecht-Gruber, C., Denzel, P., Forster, O., Billeter, Y., Iranfar, A., Repetto, M., Reif, M. U., Schilling, F.-P., Weng, J., & Chavarriaga, R. (2025, January 28). Assessment tool for trustworthy AI systems : operational workflows for compliance assessment with regulatory requirements. AI Days @ HES-SO, Geneva and Lausanne, Switzerland, 27–29 January 2025. https://doi.org/10.21256/zhaw-32422
• Denzel, P., Schilling, F.-P., & Gavagnin, E. (2023, October 30). Map-to-map translation for SKA mock observations and cosmological simulations. Hammers & Nails 2023 - Swiss Edition, Ascona, Switzerland, 29 October - 3 November 2023. https://doi.org/10.21256/zhaw-29047
• Denzel, P. B., Schilling, F.-P., & Gavagnin, E. (2023, January 11). Deep learning the SKA : the Square Kilometer Array project. Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. https://doi.org/10.21256/zhaw-27219
• Weng, J., Reif, M., Chavarriaga, R., & Schilling, F.-P. (2023, January 11). certAInty : a certification scheme for AI systems (Innosuisse project). Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. https://doi.org/10.21256/zhaw-27261
• Amirian, M., Rombach, K., Tuggener, L., Schilling, F.-P., & Stadelmann, T. (2019). Efficient deep CNNs for cross-modal automated computer vision under time and space constraints. ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019. https://doi.org/10.21256/zhaw-18357
• Stadelmann, T., & Schilling, F.-P. (2019). Deep Learning in medizinischer Diagnostik und Qualitätskontrolle. Netzwoche. https://doi.org/10.21256/zhaw-20163

Mündliche Konferenzbeiträge und Abstracts

Denzel, P., Brunner, S., Luley, P.-P., Frischknecht-Gruber, C., Reif, M. U., Schilling, F.-P., Amini, A., Repetto, M., Iranfar, A., Weng, J., & Chavarriaga, R. (2023, November 2). A framework for assessing and certifying explainability of health-oriented AI systems. Explainable AI in Medicine Workshop, Lugano, Switzerland, 2-3 November 2023.

Übrige Publikationen

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

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