27.11.2025
Seeing the Bigger Picture – One Detail at a Time
MCML Research Insight - With Rui Xiao, Sanghwan Kim, and Zeynep Akata
Large vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-training) have changed how AI works with mixed inputs of images and text, by learning to connect pictures and words. Given an image with a caption like “a dog playing with a ball”, CLIP learns to link visual patterns (the …
25.11.2025
Daniel Rückert Among the World’s Most Cited Researchers
TUM News
MCML Director Daniel Rückert is listed among the world’s most frequently cited researchers in the Cross-Field category for his work on artificial intelligence in healthcare and medicine. In total, 17 TUM scientists were recognized in the 2025 Highly Cited Researchers rankings by Clarivate, …
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24.11.2025
Research Stay at Stanford University
Kun Yuan – Funded by the MCML AI X-Change Program
During my research stay at Stanford University from July to September 2025, I had the pleasure of being part of the research group led by Assistant Professor Serena Yeung in the Department of Biomedical Data Science. My two-month stay in California gave me the opportunity to investigate how public …
20.11.2025
Digdeep Podcast: How Does Synera Shape Product Development of the Future, Moritz Maier?
News From the Digital World by MCML PI Frauke Kreuter and Christof Horn
In the new episode of #digdeep, Moritz Mayer talks about the shape product development of Synera. Developing physical products requires specialized knowledge and the use of complex tools. Moritz Maier and his startup Synera want to simplify this process by creating a platform that enables product development such as “low code” programming. On this …
20.11.2025
Zigzag Your Way to Faster, Smarter AI Image Generation
MCML Research Insight - With Vincent Tao Hu, Olga Grebenkova, Pingchuan Ma, Johannes Schusterbauer, and Björn Ommer
State-of-the-art diffusion models like DiT and Stable Diffusion have made AI image generation incredibly powerful. But they still struggle with one big issue: scaling to large images or videos quickly and efficiently without exhausting your GPU memory. What if we could process images faster, use less memory, and still retain visual quality—without …
13.11.2025
Anne-Laure Boulesteix Among the World’s Most Cited Researchers
LMU Newsroom
MCML PI Anne‑Laure Boulesteix has been named a Highly Cited Researcher 2025 by Clarivate for her influential work in the Cross-Field category. The recognition highlights publications from 2014–2024 that rank among the top 1% most cited worldwide, reflecting Anne‑Laure Boulesteix’s global impact and the international visibility of her research. She …