Research Blog
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 …
06.02.2025
Improving Business Processes
Researcher in Focus: Gabriel Marques Tavares
MCML Junior Member Gabriel Marques Tavares has a PostDoc position at the chair of Database Systems and Data Mining at LMU Munich. His research focus is in the field of Process Mining, which investigates the execution of business processes within organizations, aiming at improving their performance, saving resources and identifying bottlenecks.
15.01.2025
TruthQuest – A New Benchmark for AI Reasoning
MCML Research Insight - With Philipp Mondorf and Barbara Plank
In their recent work, “Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large Language Models” our Junior Member Philipp Mondorf and our PI Barbara Plank tackle a fascinating question: How well do AI systems handle complex reasoning tasks?
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07.01.2025
Research Collaboration Between TUM/MCML and Stanford University
Maolin Gao - Funded by the MCML AI X-Change Program and BaCaTeC
During the summer of 2024, I had the privilege of representing the Computer Vision Group at TUM, led by Prof. Daniel Cremers, in a collaborative research project with the Geometric Computing Group at Stanford University, headed by Prof. Leonidas Guibas. During my three-month stay at Stanford, researchers from both institutes delved deeply into the …
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19.12.2024
Epistemic Foundations and Limitations of Statistics and Science
Blogpost on the Replication Crisis
The Open Science Initiative in Statistics - which is part of the Open Science Center at LMU Munich - and MCML recently hosted a workshop about epistemic foundations and limitations of statistics and science. The event brought together researchers from diverse fields to discuss one of science’s most pressing challenges: The replication crisis. While …