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Link to AI Research by Daniel Rückert Improves Medical Imaging and Data Privacy

29.07.2025

AI Research by Daniel Rückert Improves Medical Imaging and Data Privacy

TUM News Article

MCML Director Daniel Rückert and his team are developing AI technologies to improve diagnostic imaging and protect patient data. Their research includes federated learning approaches that allow models to learn from clinical data without sharing sensitive information, as well as privacy-enhancing …

Link to Barbara Plank Awarded 2025 Imminent Research Grant for Work on Language Data

29.07.2025

Barbara Plank Awarded 2025 Imminent Research Grant for Work on Language Data

Supporting Innovative Research at the Intersection of Language and AI

We are proud to announce that Barbara Plank, MCML PI and Professor of AI & Computational Linguistics at LMU Munich, has received the 2025 Imminent Research Grant for her work on language data together with Siyao (Logan) Peng and Marie-Catherine de Marneffe. Congratulations to Barbara and the …

Link to Yusuf Sale Receives IJAR Young Researcher Award

29.07.2025

Yusuf Sale Receives IJAR Young Researcher Award

Award for Promising Work in Uncertainty Quantification at ISIPTA 2025

MCML Junior Member Yusuf Sale has been honored with one of the IJAR Young Researcher Awards at ISIPTA 2025. This prestigious award, presented by the International Journal of Approximate Reasoning, recognizes early-career researchers for outstanding contributions in the field of imprecise …

Link to AI for Enhanced Eye Diagnostics - With Researcher Lucie Huang

29.07.2025

AI for Enhanced Eye Diagnostics - With Researcher Lucie Huang

Research Film

Curious how AI is revolutionizing the treatment of eye diseases? Learn more about what AI can do for ophthalmology with Lucie Huang, MCML junior member and PhD student at TUM, who is developing AI for sharper and faster eye scans. This means earlier diagnoses and better treatments for critical eye conditions like diabetes-related damage. Discover …

Link to MCML Stammtisch - Recap

25.07.2025

MCML Stammtisch - Recap

July Edition

Conversations, new connections and lots of pizza and drinks at our MCML Stammtisch, yesterday. Our Stammtisch is a regular informal meetup for all members. It’s a relaxed setting where researchers come together to chat about current projects, exchange ideas, or simply unwind and talk about life beyond the lab.

Link to Industry Pitch Talks Recap

25.07.2025

Industry Pitch Talks Recap

Visit to SAP Labs

On July 22nd, we visited SAP Labs in Garching and had an event of our series “MCML Pitchtalks with Industry”.

  • Yunpu Ma presented his latest work on Agentic AI.
  • Ivica Obadic talked about AI applications in Earth Observation and Energy Conservation.
  • Maximilian Muschalik presented his work on uncertainty quantified research in ML.
SAP presented work …

Link to MCML Researchers With 32 Papers at ACL 2025

25.07.2025

MCML Researchers With 32 Papers at ACL 2025

63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025). Vienna, Austria, 27.07.2025 - 01.08.2025

We are happy to announce that MCML researchers are represented with 32 papers at ACL 2025. Read more to get details on the topics.

Link to SceneDINO: How AI Learns to See and Understand Images in 3D–Without Human Labels

24.07.2025

SceneDINO: How AI Learns to See and Understand Images in 3D–Without Human Labels

MCML Research Insight - With Christoph Reich, Felix Wimbauer, and Daniel Cremers

Imagine looking at a single image and trying to understand the entire 3D scenery–not just what’s visible, but also what’s occluded. Humans do this effortlessly: when we see a photo of a tree, we intuitively grasp its 3D structure and semantic meaning. We learn this ability through interaction and movement in the 3D world, without explicit …

Link to How Reliable Are Machine Learning Methods? With Anne-Laure Boulesteix and Milena Wünsch

23.07.2025

How Reliable Are Machine Learning Methods? With Anne-Laure Boulesteix and Milena Wünsch

Research Film

Often a new machine learning method claims to outperform the last. Whether it’s in bioinformatics, finance, or image recognition, the message is the same: this algorithm is faster, more accurate, more powerful. But can we trust those claims?

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