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Research Blog

Welcome to our research blog, where we highlight the work and achievements of our researchers, with a special focus on our Junior Members. Here, you’ll gain insight into their innovative work and the fresh ideas they bring to the dynamic fields of ML and AI.

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 …

02.06.2026

Benjamin Lange: The Real Risk of AI Agents Is Manipulation Through Kindness

Research Film

As AI moves from answering questions to managing our lives, how do we make sure we stay in control?

28.05.2026

Zeynep Akata: To Trust AI, We Need to Understand What Goes on Behind the Scenes

Research Film

How can we build AI systems that are transparent enough to be trusted with decisions that affect our health and daily lives?

11.05.2026

Research Stay at Imperial College London

Jun Li – Funded by the MCML AI X-Change Program

After the MCML delegation trip to London, I was very happy to stay for one week at Imperial College London and visit Professor Wenjia Bai’s group as part of the MCML AI X-Change Program. It was …

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 …