Daniel Rückert
Also known as Daniel Ruckert
Daniel Rückert works on artificial intelligence for medical imaging, including image registration, reconstruction, segmentation and clinical prediction. He completed computer-science studies at TU Berlin in 1993 and a doctorate at Imperial College London in 1997, followed by a postdoctoral position at King’s College London. He became an assistant professor at Imperial in 1999 and held its Visual Information Processing chair from 2005. In 2020 he took an Alexander von Humboldt professorship for AI in medicine at TUM. His coauthored research includes neural-network reconstruction of MRI, attention mechanisms for medical images, and privacy-preserving machine learning.
- Works
- 50
- Citations
- 1,714
- h-index
- 10
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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1993–1999
Training and Imperial appointment
Rückert studied computer science at TU Berlin in 1993, and completed his doctorate at Imperial in 1997. After postdoctoral work at King’s College London, he became an assistant professor at Imperial in 1999.
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2005–2020
Visual information processing
He held Imperial’s chair for Visual Information Processing from 2005. He served as dean from 2016 to 2020. These roles sit beside his work on medical-image analysis and interpretation.
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2018–2020
Neural methods for medical images
He coauthored a 2018 deep cascade of convolutional networks for dynamic MRI reconstruction, a 2019 attention-gated network paper, and a 2020 paper on secure, privacy-preserving and federated learning in imaging. Several other researchers coauthored each paper with him.
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2020
AI in medicine at TUM
He took an Alexander von Humboldt professorship at TUM in 2020 and also held an Imperial professorship. His research concerns algorithms for medical images and extraction of clinical information for computer-assisted diagnosis and prognosis.
Sources and credits
Photo: ellis.eu; Daniel Rückert profile photograph; creator not stated. License: Not stated by source. Source: ellis.eu. Cropped to a square and resized. Ask us to remove or replace this photo.