Research Blog
29.06.2026
Research Stay at Stanford University
Felix Dülmer – Funded by the MCML AI X-Change Program
From March to May 2026, I had the opportunity to spend three months as a visiting researcher at Stanford University, hosted by Prof. Jeremy Dahl and the Ultrasound Imaging and Instrumentation Lab. …
26.06.2026
Research Stay at National University of Singapore
Bailiang Jian – Funded by the MCML AI X-Change Program
During my academic visit to Singapore. I am happy to work with the MVI-Lab lead by Prof. Hongwei Bran Li. The group works on a wide range of topics in medical image analysis, including CT-PET imaging …
10.06.2026
How Should Researchers Report Their Use of LLMs?
MCML Research Insight – With Stefan Feuerriegel, Barbara Plank, Kerstin Forster, Dominique Geissler, Abdurahman Maarouf, Sebastian Maier
Large Language Models (LLMs) are increasingly becoming part of scientific research. They can generate text, analyze data, simulate participants, and support researchers in entirely new ways. But there …
08.05.2026
Right Answer, Wrong Reasoning - Is AI Thinking or Cheating?
MCML Research Insight – With Xinpeng Wang and Barbara Plank
Imagine a student solving a math problem. The steps look perfectly logical, neatly written, and convincing. But in reality, the student already knew the answer and simply worked backwards to justify …
27.04.2026
Research Stay at Imperial College London
Lennart Bastian – Funded by the MCML AI X-Change Program
During my research stay at Imperial College London, I had the pleasure of working with the CIRCLE group led by Tolga Birdal in the Department of Computing. Among the various research topics at CIRCLE …
23.04.2026
When Vision AI Hallucinates Details
MCML Research Insight – With Rui Xiao, Sanghwan Kim and Zeynep Akata
A photo shows a dog on a sofa. You ask an AI assistant: Is there also a cat sitting next to it? The assistant confidently says yes, even though there is no cat at all. In many everyday situations, …
16.04.2026
Do Language Models Reason Like Humans?
MCML Research Insight – With Philipp Mondorf and Barbara Plank
Imagine reading the sentence: “If it rains, the streets will be wet.” Most people would consider it perfectly reasonable. Now consider a different statement: “If the moon is made of cheese, the …
02.04.2026
How AI Avatars Shape Perceived Fairness
MCML Research Insight – With Ka Hei Carrie Lau and Enkelejda Kasneci
Imagine applying for a job and being interviewed not by a human recruiter, but by an AI avatar on your screen. The conversation feels surprisingly natural. The interviewer smiles, asks questions, and …
19.03.2026
Teaching Models to Say ‘I’m Not Sure’
MCML Research Insight – With Hannah Laus, Felix Krahmer and Holger Rauhut
Machine learning models are good at giving answers, predicting patterns, and classifying objects but they are much worse at saying when they are unsure. But what if the model could admit when it was …
05.03.2026
Foundations of Diffusion: One Map for Images and Text
MCML Research Insight – With Vincent Pauline, Tobias Höppe, Andrea Dittadi, and Stefan Bauer
From hyper-realistic video generation to protein design, Diffusion Models are the engine behind the current wave of Generative AI. But if you try to understand how diffusion models actually work, you …
19.02.2026
COSMOS – Teaching Vision-Language Models to Look Beyond the Obvious
MCML Research Insight - With Sanghwan Kim, Rui Xiao, Mariana-Iuliana Georgescu, Stephan Alaniz, and Zeynep Akata
Today’s AI systems are remarkably good at recognizing what stands out most. Yet understanding how details relate to each other remains surprisingly difficult. Imagine asking an AI assistant to …
05.02.2026
Needle in a Haystack: Finding Exact Moments in Long Videos
MCML Research Insight - With With Tanveer Hannan and Thomas Seidl
Long videos are everywhere, with footage of movies, YouTube videos, body-cam recordings, and AR/VR often running for tens of minutes or even hours. Now imagine asking a simple question like “Where are …
29.01.2026
How Machines Can Discover Hidden Rules Without Supervision
MCML Research Insight - With Tobias Schmidt, and Steffen Schneider
How can machines learn the hidden rules that govern how systems change—how objects move, how weather patterns unfold, or how biological signals evolve—without ever being told what those rules are?
22.01.2026
From Global to Regional Explanations: Understanding Models More Locally
MCML Research Insight - With Giuseppe Casalicchio, Thomas Nagler, and Bernd Bischl
Machine learning models can be powerful, but understanding why they behave the way they do is often much harder. Early global interpretability tools were designed to show how each feature affects the …
22.01.2026
Research Stay at University of St. Gallen
Andrea Maldonado – Funded by the MCML AI X-Change Program
Between Freudenberg – “happiness mountain” – and Rosenberg – “roses mountain”, I had the pleasure to visit the Institute of Computer Science (ICS-HSG) at the University of St. Gallen (HSG) in …
15.01.2026
Blind Matching – Aligning Images and Text Without Training or Labels
MCML Research Insight - With Dominik Schnaus, Nikita Araslanov, and Daniel Cremers
Vision-language models have shown that images and text can live in a shared space: a picture of a “cat” often lands close to the word “cat” in the embedding space. But such …
08.01.2026
High-Res Images, Less Wait: A Simple Flow for Image Generation
MCML Research Insight - With Johannes Schusterbauer, Pingchuan Ma, Vincent Tao Hu, and Björn Ommer
Image generation models today can create almost anything, like a futuristic city glowing at sunset, a classical painting of your cat, or a realistic spaceship made of glass. But when you ask them to …
18.12.2025
"See, Don’t Assume": Revealing and Reducing Gender Bias in AI
MCML Research Insight - With Leander Girrbach, Yiran Huang, Stephan Alaniz and Zeynep Akata
Using AI and LLMs at work feels almost unavoidable today: they make things easier, but they can also go wrong in important ways. One of the trickiest problems? Gender bias. For example, if you ask …
11.12.2025
From Sitting Dog to Standing: A New Way to Morph 3D Shapes
MCML Research Insight - With Lu Sang and Daniel Cremers
Ever wondered how a 3D shape can smoothly change — like a robot arm bending or a dog rising from sitting to standing — without complex simulations or hand-crafted data? Researchers from MCML and the …
2024-11-22 - Last modified: 2026-06-29