Intelligent Vision Systems
"We advance the state of the art in applied AI and Deep Learning research."
Fields of expertise
- Generative, foundation and predictive AI models for computer vision
- Machine learning systems (MLOps)
- Trustworthy, verifiable and certifiable AI
- AI for science
We conduct research primarily in the domain of AI-based computer vision for multidimensional image, video or point cloud data, for which we develop state of the art deep neural network architectures.
We are particularly interested in generative models including diffusion- as well as flow- or energy-based models, as well as in geometric deep learning. Domains of applications include, but are not limited to, industrial quality control, medical imaging and diagnosis (e.g. based on computed tomography or MRI), as well as earth (using satellites or drones) and sky (e.g. in radio-astronomy) observation data. The latter serves as an example of our aim to apply AI methods to advance science and scientific discoveries.
Our second main area of interest concerns Machine Learning Operations (MLOps), which describes best practices for building complete, production-ready and scalable Machine Learning or AI systems. As these systems should also be robust, reliable, trustworthy, safe and compliant, we investigate both the integration of these principles by design, e.g. by following MLOps best practices and standards, as well as their post-hoc assessment using technical methods.
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
Further Information
Get in touch to explore collaborating with us in research & innovation projects, or if you are interested to pursue a MSc degree under our supervision. We post any job offers (including for PhD students) on the ZHAW jobs portal if and when they become available.
Team
Head of Research Group
Projects
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High computational power and AI aboard satellites (SATAI)
A structured inventory of potential use cases is developed for onboard satellite AI compute, organised across application domains and AI capability types, with key system assumptions and qualitative assessments of feasibility, novelty, and impact. Selected use cases are then refined into detailed,…
current, 07/2026 - 12/2026
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A data-driven solution that optimizes ankle-foot-orthopedic braces for children (OrthoSense)
OrthoSense by Leg&airy revolutionizes ankle-foot orthotics by turning subjective brace fitting into data-driven care. Embedded sensors capture pressure data, enabling AI driven biomechanical algorithms to tailor each brace’s shape and fit to the individual patient while enabling patients tracking.
current, 01/2026 - 12/2027
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High performance AI stereo matching
For the further development of an intraoral scanner (IOS), various AI stereo matching algorithms are being examined for their suitability for use in the product. The aim is to identify a suitable model for training and implementation, to convert it to TensorRT and to optimize the real-time…
expired, 08/2025 - 10/2025
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Antimicrobial Resistance Tracker (AMR)
The goal of the project is to strengthen surveillance of antimicrobial resistance in animals and food products, with a focus on international travelers. The project will explore (and quantify the uncertainty of) using AI-based text and image mining techniques to extract data on the prevalence of…
current, 08/2025 - 07/2028
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SCRAI – A Think-and-Do-Tank for Responsible Development and Societal Alignment of Artificial Intelligence Systems
SCRAI brings responsible AI to the ground by bridging the gap between societal values and development of AI technology and solutions. The SCRAI Think-and-Do Tank will help organizations to exploit the potential of AI while complying with legal requirements and being compatible with societal…
current, 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…
expired, 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…
expired, 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.
expired, 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.
expired, 05/2022 - 02/2025
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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…
expired, 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.
expired, 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/10.21256/zhaw-32837
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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/10.21256/zhaw-32423
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Bonaldi, Anna; Hartley, Philippa; Braun, Robert D.; Purser, S. J. D.; Acharya, Anshuman; Ahn, Kyungjin; Resco, Miguel Aparicio; Bait, Omkar; Bianco, Michele; Chakraborty, Abhijit; Chapman, Emma; Chatterjee, S.; Chege, K.; Chen, Hao; Chen, Xiao; Chen, Zhong; Conaboy, Luke; Cruz, M.; Darriba, Laura; Santis, M. De; Denzel, Philipp; Diao, Kai; Feron, Jennifer; Finlay, Chris; Gehlot, B. K.; Ghosh, Shaon; Giri, Sambit K.; Grumitt, R D P; Hong, Sungwook E.; Ito, Takaaki; Jiang, Min; Jordan, C.; Kim, S.; Kim, Mi Ran; Kim, J.; Krishna, Shreyam Parth; Kulkarni, Akshay; López-Caniego, M.; Labadie-García, I.; Lee, H. W.; Lee, David; Lee, Nicole; Line, J.; Liu, Yu‐Rong; Mao, Yi; Mazumder, Aishrila; Mertens, Florent; Munshi, S.; Nasirudin, Ainulnabilah; Ni, Shaoqing; Nistane, Viraj; Norregaard, Carina; Null, Donald M.; Offringa, A. R.; Seo, H.; Oh, Se–Heon; Parkinson, David; Pritchard, Jonathan R.; Ruiz-Granda, M.; López, Valle; Shan, Huanyuan; Sharma, Rohit; Trott, Cathryn M.; Yoshiura, Shintaro; Zhang, Li; Zhang, X.; Zheng, Q.; Zhu, Z.; Zuo, Shuai; Akahori, Takuya; Alberto, P.; Allys, Erwan; An, Tao; Anstey, D.; Baek, Ji‐Hye; Basavraj; Brackenhoff, S. A.; Browne, P.; Ceccotti, E.; Chen, Hao; Chen, Tianyue; Choudhuri, S; Choudhury, Mahbuba; Coles, Jonathan; Cook, Joseph M.; Cornu, David; Cunnington, Steven; Das, Sonali; Acedo, Eloy de Lera; Delouis, J.‐M.; Dèng, Fēi; Ding, Junjun; Elahi, Khandakar Md Asif; Fernandez, P; Fernandéz, Christian; Alcázar, A.; Galluzzi, V.; Gao, L. Q.; Garain, Utpal; Garrido, Julián,
2025.
Square Kilometre Array Science Data Challenge 3a : foreground removal for an EoR experiment.
Monthly Notices of the Royal Astronomical Society.
543(2), pp. 1092-1119.
Available from: https://doi.org/10.21256/zhaw-34188
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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/10.21256/zhaw-30443