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Research Blog

Welcome to our research blog, where we proudly showcase the talents and achievements of our researchers, with a special focus on our junior members. Here, you’ll gain insight into their innovative work and the fresh ideas they bring to the ever-evolving fields of AI and machine learning.

Link to Research Stay at Princeton University

01.12.2025

Research Stay at Princeton University

Abdurahman Maarouf – Funded by the MCML AI X-Change Program

From May to July, I spent three exciting months as a visiting researcher at the Computer Science Department of Princeton University, hosted by Prof. Manoel Horta Ribeiro. The visit grew out of a keynote Manoel gave at LMU. After his talk, we discussed potential joint projects at the intersection of …

Link to Seeing the Bigger Picture – One Detail at a Time

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 …

Link to Research Stay at Stanford University

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 scientific articles can be leveraged to build …

Link to Zigzag Your Way to Faster, Smarter AI Image Generation

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 …

Link to Explaining AI Decisions: Shapley Values Enable Smart Exosuits

13.11.2025

Explaining AI Decisions: Shapley Values Enable Smart Exosuits

MCML Research Insight - With Julia Herbinger, Giuseppe Casalicchio, Yusuf Sale, Bernd Bischl and Eyke Hüllermeier

Picture a typical day in a warehouse: one worker lifts, bends, and carries out the same task over and over again. While the routine may seem simple, the physical toll steadily builds—affecting joints and muscles. To combat the long-term health risks associated with such repetitive movements, businesses are increasingly turning to exoskeletons and …

Link to SIC: Making AI Image Classification Understandable

16.10.2025

SIC: Making AI Image Classification Understandable

MCML Research Insight - With Tom Nuno Wolf, Emre Kavak, Fabian Bongratz, and Christian Wachinger

Deep learning models are emerging more and more in everyday life, going as far as assisting clinicians in their diagnosis. However, their black box nature prevents understanding errors and decision-making, which arguably are as important as high accuracy in decision-critical tasks. Previous research typically focused on either designing models to …

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