Research Blog
26.06.2025
What Words Reveal: Analyzing Language in the Trump–Harris 2024 Debate
MCML Research Insight - With Philipp Wicke
In presidential debates, every word counts - not only what candidates say but how they say it. Words are carefully selected to resonate with voters’ values, fears, and hopes. A new insightful study by MCML Junior Member Philipp Wicke and Marianna M. Bolognesi dives deep into the September 10, 2024 …
25.06.2025
When Clinical Expertise Meets AI Innovation – With Michael Ingrisch
Research Film
Artificial intelligence has enormous potential in radiology — but realizing it requires more than good algorithms. Michael Ingrisch, Clinical Data Science Professor at LMU and MCML PI, shares how his team took a practical approach: identifying a key diagnostic challenge in PET CT imaging and inviting the broader AI community to help solve it.
23.06.2025
Autonomous Driving: From Infinite Possibilities to Safe Decisions— With Matthias Althoff
Research Film
How can we guarantee that autonomous vehicles always make the right decision in unpredictable traffic?
Matthias Althoff, Professor at the chair of Cyber-Physical Systems at the Technical University of Munich and MCML PI, explores the complex challenge of ensuring safety in AI-powered driving systems.
20.06.2025
Zooming in on Moments: ReVisionLLM for Long-Form Video Understanding
MCML Research Insight - With Tanveer Hannan and Thomas Seidl
Imagine watching a two-hour video and trying to find the exact moment someone scores a goal - or says something important. Humans can do this with ease by skimming and zooming in. But for AI, finding specific moments in long videos is incredibly hard. Most current AI systems struggle to handle more than a few minutes of video at a time. They often …
12.06.2025
Why Causal Reasoning Is Crucial for Reliable AI Decisions
MCML Research Insight - With Christoph Kern, Unai Fischer-Abaigar, Jonas Schweisthal, Dennis Frauen, Stefan Feuerriegel and Frauke Kreuter
As algorithms increasingly make decisions that impact our lives, from managing city traffic to recommending hospital treatments, one question becomes urgent: Can we trust them?
11.06.2025
Better Data, Smarter AI: Why Quality Matters – With Frauke Kreuter
Research Film
How does data quality shape the future of AI? Frauke Kreuter, Professor at the Chair of Statistics and Data Science at LMU Munich and MCML PI, shares her insights on the fundamental role of data quality in the development and deployment of AI.
30.04.2025
Who Spreads Hate?
MCML Research Insight – With Dominique Geissler, Abdurahman Maarouf, and Stefan Feuerriegel
Hate speech on social media isn’t just offensive - it’s dangerous. It spreads quickly, harms mental health, and can even contribute to real-world violence. While many studies have focused on identifying hate speech or profiling those who create it, a key piece of the puzzle remained missing: Who reshares hate speech? The team at MCML - Dominique …
29.04.2025
How Certain Is AI? An Introduction to Bayesian Deep Learning
Researcher in Focus: Emanuel Sommer
MCML Junior Member Emanuel Sommer is a PhD-student at the Munich Uncertainty Quantification AI Lab at LMU Munich supervised by David Rügamer. His research focuses on Scalable and Reliable (Bayesian) Deep Learning.
10.04.2025
Text2Loc: A Smarter Way to Navigate With Words
MCML Research Insight - With Yan Xia, Zifeng Ding and Daniel Cremers
Imagine standing in an unfamiliar part of a city, no GPS in sight. All you can say is, “I’m west of a green building, near a black garage.” That might be vague to a machine, but Text2Loc understands you perfectly. With this powerful new system, AI can find your exact location in a 3D map - just from how you describe the world around you.
03.04.2025
CUPS: Teaching AI to Understand Scenes Without Human Labels
MCML Research Insight - With Christoph Reich, Nikita Araslanov, and Daniel Cremers
What matters now
Understanding the location and semantics of objects in a scene is a significant task, enabling robots to navigate through complex environments or facilitating autonomous driving. Recent AI models for understanding scenes from images require significant guidance from humans in the form of pixel-level annotations to achieve accurate …
27.03.2025
Beyond the Black Box: Choosing the Right Feature Importance Method
MCML Research Insight - With Fiona Katharina Ewald, Ludwig Bothmann, Giuseppe Casalicchio and Bernd Bischl
Machine learning models make powerful predictions, but can we really trust them if we don’t understand how they work? Global feature importance methods help us discover which factors really matter - but choosing the wrong method can lead to misleading conclusions. To see why this is important, consider a real-world example from medicine.
13.03.2025
ReNO: A Smarter Way to Enhance AI-Generated Images
MCML Research Insight - With Luca Eyring, Shyamgopal Karthik, Karsten Roth and Zeynep Akata
Despite their impressive capabilities, Text-to-Image (T2I) models frequently misinterpret detailed prompts, leading to errors in object positioning, attribute accuracy, and color fidelity. Traditional improvements rely on extensive dataset training, which is not only computationally expensive but also may not generalize well to unseen prompts. To …
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04.03.2025
Research at EWCS at the Broad Institute of MIT and Harvard
Cecilia Casolo - Funded by the MCML AI X-Change Program
During my research stay at Broad Institute of MIT and Harvard in autumn 2024, I had the pleasure of being part of the research group led by Caroline Uhler, Director of the Eric and Wendy Schmidt Center (EWCS) at the Broad Institute, and Andrew (1956) and Erna Viterbi Professor of Engineering in EECS and IDSS at MIT. My three-month stay in Boston …
23.02.2025
ChatGPT in Radiology: Making Medical Reports Patient-Friendly?
MCML Research Insight - With Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber and Philipp Wesp and MCML PI Michael Ingrisch
Medical reports, especially in radiology, are commonly difficult for patients to understand. Filled with complex terminology and specialized jargon, these reports are primarily written for medical professionals, often leaving patients struggling to make sense of their diagnoses. But what if AI could help? Recognizing this potential early on, the …
11.02.2025
Can AI Help Solve Complex Physics Equations? Meet APEBench
MCML Research Insight - With Felix Köhler, Rüdiger Westermann and Nils Thuerey
Our Junior Member Felix Köhler, together with our PIs Rüdiger Westermann and Nils Thuerey, and collaborator Simon Niedermayr, have introduced APEBench, an innovative benchmark suite. APEBench sets a new standard for evaluating autoregressive neural emulators, which are designed to solve partial differential equations (PDEs)—the fundamental …