Intelligent Vision Systems Group
"We aim at advancing the state of the art in AI, Deep Learning and Machine Learning research, while at the same time developing tailored solutions to real-world challenging problems which help to advance technology and benefit humanity."
Fields of expertise
- Computer Vision
- Machine Learning Systems (MLOps)
- Trustworthy and certifiable AI
We conduct research primarily in the domain of computer vision using 2-,3- or 4-D image or video data as input and performing classification, object detection or other visual tasks, for which we develop state of the art deep neural network architectures. We are particularly interested in recent developments including vision transformers and gauge equivariant neural networks. Domains of applications include, but are not limited to, industrial quality control, medical imaging and diagnosis (computed tomography), as well as earth (satellites) and sky (radio-astronomy) observation data. We are also interested in hybrid approaches to AI as well as geometric deep learning. Our second main area of interest concerns MLOps, which describes best practices for building complete, production-ready and scalable Machine Learning systems. Finally, we are interested in methods to create safe, trustworthy and certifiable AI systems, which comply with current and future legislation.
Services
- Insight: keynotes, trainings
- AI consultancy: workshops, expert support, advise, technology assessment
- Research and development: small to large-scale research projects, third party-funded research, student projects, commercially applicable prototypes
Team
Head of Research Group
Projects
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AI-BRIDGE - A Think-and-Do-Tank for Responsible Development and Societal Alignment of Artificial Intelligence Systems (AI-BRIDGE)
AI-BRIDGE brings responsible AI to the ground by bridging the gap between societal values and development of AI technology and solutions. The AI-BRIDGE Think-and-Do Tank will help organizations to exploit the potential of AI while complying with legal requirements and being compatible with societal…
ongoing, 04/2025 - 12/2029
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Certification program for assessing ethics of Autonomous Intelligent Systems (IEEE CertifAIEd Assessor Training)
ZHAW is offering, in partnership with IEEE SA, the IEEE CertifAIEd (TM) Authorized Assessor Training. IEEE CertifAIEd is a certification program for assessing ethics of Autonomous Intelligent Systems (AIS) to help protect, differentiate, and grow product adoption. The resulting certificate and mark…
ongoing, 05/2024 - 07/2025
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certAInty – A Certification Scheme for AI systems (certAInty)
Certification of AI Systems by an accredited body increases trust, accelerates adoption and enables their use for safety-critical applications. We develop a Certification Scheme comprising specific requirements, criteria, measures, and technical methods for assessing Machine Learning enabled…
completed, 11/2022 - 12/2024
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OSR4H – Open Set Recognition for Hematology
Development of a Proof of Concept for visual Open Set Recognition (OSR) algorithms applied to a Hematology task, the classification of white blood cells.
completed, 08/2022 - 03/2023
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AI powered CBCT for improved Combination Cancer Therapy (AC3T)
The project enables a novel, combined, adaptive cancer therapy combining tumor treating field and radiation therapy due to significantly improved static (3D) and time-resolved (4D) low dose Cone Beam Computer Tomography images based on artificial intelligence image reconstruction algorithms.
completed, 05/2022 - 02/2025
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Square Kilometre Array: Mock-observations via generative deep learning
In this project we use generative deep learning methods (GANs and VAEs) to produce realistic astronomical mock-observations of numerically simulated astrophysical objects, as they will be obseved by the Square Kilometre Array Telescope (SKA). This project contributes to the Swiss-wide activities for…
completed, 09/2021 - 12/2024
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PhD Program in Data Science
Understanding effective and ethical ways of using vast amounts of data is a significant challenge to science and society as a whole. Data Science encompasses such activities and is therefore inherently interdisciplinary and applied. ZHAW is an early mover in the field of Data Science. Since 2013…
ongoing, 01/2021 - 06/2025
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DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes
Project DIR3CT aims at improving the image quality of CBCT images by deep learning (DL) the 3D reconstruction from X-ray images end-to-end. This enables a novel CBCT product to be used during radiation therapy and will allow the use of these images for adaptive treatment.
completed, 02/2020 - 05/2022
Publications
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Bonaldi, A.; Hartley, P.; Braun, R.; Purser, S.; Acharya, A.; Ahn, K.; Resco, M. Aparicio; Bait, O.; Bianco, M.; Chakraborty, A.; Chapman, E.; Chatterjee, S.; Chege, K.; Chen, H.; Chen, X.; Chen, Z.; Conaboy, L.; Cruz, M.; Darriba, L.; De Santis, M.; Denzel, P.; Diao, K.; Feron, J.; Finlay, C.; Gehlot, B.; Ghosh, S.; Giri, S. K.; Grumitt, R.; Hong, S. E.; Ito, T.; Jiang, M.; Jordan, C.; Kim, S.; Kim, M.; Kim, J.; Krishna, S. P.; Kulkarni, A.; López-Caniego, M.; Labadie-García, I.; Lee, H.; Lee, D.; Lee, N.; Line, J.; Liu, Y.; Mao, Y.; Mazumder, A.; Mertens, F. G.; Munshi, S.; Nasirudin, A.; Ni, S.; Nistane, V.; Norregaard, C.; Null, D.; Offringa, A.; Oh, M.; Oh, S. -H.; Parkinson, D.; Pritchard, J.; Ruiz-Granda, M.; López, V. Salvador; Shan, H.; Sharma, R.; Trott, C.; Yoshiura, S.; Zhang, L.; Zhang, X.; Zheng, Q.; Zhu, Z.; Zuo, S.; Akahori, T.; Alberto, P.; Allys, E.; An, T.; Anstey, D.; Baek, J.; Basavraj; Brackenhoff, S.; Browne, P.; Ceccotti, E.; Chen, H.; Chen, T.; Choudhuri, S.; Choudhury, M.; Coles, J.; Cook, J.; Cornu, D.; Cunnington, S.; Das, S.; Acedo, E. De Lera; Delou is, J. -M.; Deng, F.; Ding, J.; Elahi, K. M. A.; Fernandez, P.; Fernández, C.; Alcázar, A. Fernández; Galluzzi, V.; Gao, L. -Y.; Garain, U.; Garrido, J.; Gendron-Marsolais, M. -L.; Gessey-Jones, T.; Ghorbel, H.; Gong, Y.; Guo, S.; Hasegawa, K.; Hayashi, T.; Herranz, D.; Holanda, V.; Holloway, A. J.; Hothi, I.; Höfer, C.; Jelić, V.; Jiang, Y.; Jiang, X.; Kang, H.; Kim, J. -Y.; Koopmans, L. V.; Lacroix, R.; Lee, E.; Leeney, S.; Levrier, F.; Li, Y.; Liu, Y.; Ma, Q.; Meriot, R.; Mesinger, A.; Mevius, M.; Minoda, T.; Miville-Deschenes, M. -A.; Moldon, J.; Mondal, R.; Murmu, C.; Murray, S.; SR, Nirmala; Niu, Q .; Nunhokee, C.; O'Hara, O.; Pal, S. K.; Pal, S.; Park, J.; Parra, M.; tra, N. N. Pa; Pindor, B.; Remazeilles, M.; Rey, P.; Rubino-Martin, J. A.; Saha, S.; Selvaraj, A.; Semelin, B.; Shah, R.; Shao, Y.; Shaw, A. K.; Shi, F.; Shimabukuro, H.; Singh, G.; Sohn, B. W.; Stagni, M.; Starck, J. -L.; Sui, C.; Swinbank, J. D.; Sánchez, J.; Sánchez-Expósito, S.; Takahashi, K.; Takeuchi, T.; Tripathi, A.; Verdes-Montenegro, L.; Vielva, P.; Vitello, F. R.; Wang, G. -J.; Wang, Q.; Wang, X.; Wang, Y.; Wang, Y. -X.; Wiegert, T.; Wild, A.; Williams, W. L.; Wolz, L.; Wu, X.; Wu, P.; Xia, J. -Q.; Xu, Y.; Yan, R.; Yan, Y. -P.,
2025.
Square Kilometre Array Science Data Challenge 3a : foreground removal for an EoR experiment.
arXiv.
Available from: https://doi.org/10.48550/arxiv.2503.11740
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Frischknecht-Gruber, Carmen; Denzel, Philipp; Forster, Oliver; Billeter, Yann; Iranfar, Arman; Repetto, Marco; Reif, Monika Ulrike; Schilling, Frank-Peter; Weng, Joanna; Chavarriaga, Ricardo,
2025.
In:
AI Days @ HES-SO, Geneva and Lausanne, Switzerland, 27–29 January 2025.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-32422
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Frischknecht-Gruber, Carmen; Denzel, Philipp; Reif, Monika; Billeter, Yann; Brunner, Stefan; Forster, Oliver; Schilling, Frank-Peter; Weng, Joanna; Chavarriaga, Ricardo; et al.,
2025.
AI assessment in practice : implementing a certification scheme for AI trustworthiness[paper].
In:
Görge, Rebekka; Haedecke, Elena; Poretschkin, Maximilian; Schmitz, Anna, eds.,
Symposium on Scaling AI Assessments (SAIA 2024).
Symposium on Scaling AI Assessments (SAIA 2024), Cologne, Germany, 30 September - 1 October 2024.
Schloss Dagstuhl – Leibniz-Zentrum für Informatik.
pp. 15:1-15:18.
Open Access Series in Informatics (OASIcs) ; 126.
Available from: https://doi.org/10.4230/OASIcs.SAIA.2024.15
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Billeter, Yann; Denzel, Philipp; Chavarriaga, Ricardo; Forster, Oliver; Schilling, Frank-Peter; Brunner, Stefan; Frischknecht-Gruber, Carmen; Reif, Monika Ulrike; Weng, Joanna,
2024.
MLOps as enabler of trustworthy AI[paper].
In:
2024 11th IEEE Swiss Conference on Data Science (SDS).
11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.
IEEE.
pp. 37-40.
Available from: https://doi.org/10.1109/SDS60720.2024.00013
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Denzel, Philipp; Brunner, Stefan; Billeter, Yann; Forster, Oliver; Frischknecht-Gruber, Carmen; Reif, Monika Ulrike; Schilling, Frank-Peter; Weng, Joanna; Chavarriaga, Ricardo; Amini, Amin; Repetto, Marco; Iranfar, Arman,
2024.
Towards the certification of AI-based systems[paper].
In:
2024 11th IEEE Swiss Conference on Data Science (SDS).
11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.
IEEE.
pp. 84-91.
Available from: https://doi.org/10.1109/SDS60720.2024.00020