Home  | News

26.06.2025

Tiny logo
Teaser image to Zeynep Akata Receives 2025 ZukunftsWissen Prize

Zeynep Akata Receives 2025 ZukunftsWissen Prize

Award Highlights Contributions to the Field of Interpretable Machine Learning

We are happy to share that our PI Zeynep Akata has been awarded the 2025 ZukunftsWissen Prize by the German National Academy of Sciences Leopoldina and the Commerzbank Foundation.

The award honors her work in explainable and trustworthy AI, including early contributions to zero-shot learning and recent advancements in multimodal generative models. Akata’s research focuses on making AI decisions transparent and interpretable, helping build systems that explain their outputs in human-understandable ways.

The prize, endowed with €50,000, will be officially presented during the Leopoldina Annual Assembly on September 25, 2025, which this year focuses on artificial intelligence.

Congratulations from us!

#award #research #akata

Related

Link to How Should Researchers Report Their Use of LLMs?

10.06.2026

How Should Researchers Report Their Use of LLMs?

Is AI making science impossible to replicate? Stefan Feuerriegel and the MCML team introduce the GUIDE-LLM framework in Nature.

Read more
Link to Benjamin Lange: The Real Risk of AI Agents is Manipulation Through Kindness

02.06.2026

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

MCML Junior Research Group Leader Benjamin Lange examines how trust in AI agents can itself become a source of risk.

Read more
Tiny logo
Link to MCML at CVPR 2026

02.06.2026

MCML at CVPR 2026

MCML researchers are represented with 28 papers at CVPR 2026 (26 Main, and 2 Workshops).

Read more
Tiny logo
Link to MCML at ICRA 2026

29.05.2026

MCML at ICRA 2026

MCML researchers are represented with 4 papers at ICRA 2026 (3 Main, and 1 Workshop).

Read more
Link to Zeynep Akata: To Trust AI, We Need to Understand What Goes On Behind the Scenes

28.05.2026

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

MCML PI Zeynep Akata explains that to trust AI, we must understand its inner workings, address foundation model bias, and make explainability central.

Read more
Back to Top