Research Blog
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 …
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 …
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 …
09.10.2025
Rethinking AI in Public Institutions - Balancing Prediction and Capacity
Researcher in Focus: Unai Fischer Abaigar
Unai Fischer Abaigar is a researcher at MCML whose work focuses on improving decision-making in public institutions by developing AI systems that are both fair and effective in practice.
29.09.2025
Machine Learning for Climate Action - With Researcher Kerstin Forster
Research Film
How can machine learning fight climate change? Kerstin Forster, researcher at LMU and MCML, explores how AI can help reduce greenhouse gas emissions, improve renewable energy systems, and enhance early warning for extreme weather.
26.09.2025
Making Machine Learning More Accessible With AutoML
Researcher in Focus: Matthias Feurer
Matthias Feurer is a Thomas Bayes Fellow and interim professor, funded by the MCML and a member of the Chair of Statistical Learning and Data Science at LMU. He aims to simplify the usage of machine learning by researching methods and developing tools that allow the usage of machine learning by domain scientists and also make machine learning more …
25.09.2025
Compress Then Explain: Faster, Steadier AI Explanations - With One Tiny Step
MCML Research Insight - With Giuseppe Casalicchio and Bernd Bischl
Imagine re-running feature importance plots and getting slightly different “top features.” Annoying, right? That uncertainty often comes from a quiet assumption: model explanation algorithms typically sample points from data at random. A new ICLR 2025 Spotlight paper by MCML Junior Member Giuseppe Casalicchio, MCML Director Bernd Bischl, first …
22.09.2025
Predicting Health With AI - With Researcher Simon Schallmoser
Research Film
How can AI predict medical conditions and personalize treatments? Simon Schallmoser, researcher at LMU and MCML, uses machine learning to forecast health risks and optimize care for patients based on their individual profiles.
18.09.2025
GCE-Pose – Predicting Whole Objects From Partial Views
MCML Research Insight - With Weihang Li, Junwen Huang, Hyunjun Jung, Nassir Navab, and Benjamin Busam
Imagine trying to identify the full shape of a familiar object, e.g. a mug, when only its handle is visible. That’s the challenge a computer faces when estimating the pose of an object (its orientation and size) from partial data. GCE‑Pose, a new approach from MCML Junior Members Weihang Li, Junwen Huang, Hyunjun Jung, Benjamin Busam, MCML PI …