18.09.2025
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 …
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17.09.2025
This summer, I had the incredible opportunity to spend 11 weeks at Harvard University as part of the AI X-change program, visiting the group of Flavio Calmon at the Harvard John A. Paulson School of Engineering and Applied Sciences. The idea for this visit came after my former office mate and …
15.09.2025
What if robots could see in the dark and react faster than any human? Yannick Burkhardt, researcher at TUM and MCML, researches event cameras that capture motion in a revolutionary way. Unlike traditional cameras that take full pictures at fixed intervals, event cameras detect every tiny change in …
08.09.2025
Can AI understand the world in 3D the way we do? Riccardo Marin, researcher at TUM and MCML, works at the intersection of computer vision and 3D geometry to teach machines how to perceive shapes, patterns, and spatial structures. His work ranges from detecting production flaws in manufacturing to analyzing archaeological artifacts – showing how …
01.09.2025
Can AI help us understand why some people develop mental disorders while others remain resilient? Clara Vetter, PhD candidate at LMU and MCML, uses machine learning to uncover hidden patterns in brain scans, genetic data, and even smartphone-based information. Her goal: identifying biological markers that could improve diagnosis and treatment in …
25.08.2025
Can AI from satellite imagery help us design more liveable cities, improve well-being, and ensure sustainable food production? Ivica Obadić, PhD student at TUM and MCML, develops transparent AI models that not only predict change but also give actionable insights for urban planners.
18.08.2025
Azade Farshad researches digital twins of patients at TUM and MCML to improve personalized treatment, surgical planning, and training. Using graph-based analysis and multimodal patient data, she builds models that create realistic surgical simulations — helping surgeons preview procedures, spot potential complications, and optimize strategies.
12.08.2025
Tracking actions, not just objects: The Spatiotemporal Action Grounding Challenge and Workshop at ICCV 2025 focusses on detecting and localizing actions both in space and time within complex, real-world videos. Unlike standard action recognition, this task requires identifying when and where an action occurs, pushing models to handle long, diverse, …
07.08.2025
Text-to-image (T2I) models like Stable Diffusion have become masters at turning prompts like “a happy man and a red car” into vivid, detailed images. But what if you want the man to look just a little older, or the car to appear slightly more luxurious without changing anything else? Until now, that level of subtle, subject-specific control was …
06.08.2025
Sven Nyholm, Chair of the Ethics of AI at LMU Munich and PI at MCML, explores one of the most urgent questions in AI: how responsibility, agency, and credit shift when intelligent systems make decisions for us.
04.08.2025
Meet Dominik Bär, MCML junior member and PhD student at LMU exploring how AI can enhance the integrity of social media platforms. Dominik’s work goes beyond just detecting harmful content like hate speech and misinformation. He’s developing AI that understands why content is posted and generates thoughtful, human-like responses - so-called …
31.07.2025
Machine‑learning models can be undermined before training even starts. By silently altering a small share of training labels - marking “spam” as “not‑spam,” for instance - an attacker can cut accuracy by double‑digit percentages. The paper “Exact Certification of (Graph) Neural Networks Against Label Poisoning” by MCML Junior Member Lukas Gosch, …
30.07.2025
From dreaming of seeing the Earth from space to leading efforts to understand our planet using AI and satellite data to tackle urgent global challenges. Xiaoxiang Zhu, Chair Professor for Data Science in Earth Observation at TUM and PI at MCML, develops machine learning systems that analyze petabytes of satellite imagery. Her work focuses on …