Zeynep Akata is Professor of Computer Science at the Technical University of Munich/Germany and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. When it comes to image recognition, an AI must use large amounts of data to learn what distinguishes one class from another (for example, a dog from a cat). A common thread in Akata’s research is her innovative combination of images and language to train the AI in this classification. In addition to images, linguistic attributes are used to explain the characteristics of a dog or a cat. This allows the AI to learn from just a few examples in the training data, a process known as low-shot learning. As far back as 2013, Akata developed attribute label embedding (ALE), a zero-shot learning model with which AI can recognise new image classifications based on descriptions alone, without any training data. For example, if an AI is trained using only cat and dog images as well as linguistic descriptions, it can still recognise an image of a zebra if the characteristics of a zebra have previously been described to it. Akata’s current research on explainable AI aims to make AI decisions more comprehensible and thus increase user trust in the technology. For example, when it comes to image classification, the AI explains why an image element was attributed to a particular class (such as the zebra) and shows the relevant characteristics in the image that led to this classification (hooves, stripes, etc.). In her research on generative AI, Akata also developed new methods to automatically create realistic images based on text descriptions.
Zeynep Akata studied technical computer science and media computer science at Trakya University/Turkey and at the RWTH Aachen/Germany. Following her PhD in computer science at the INRIA Rhône-Alpes/France and positions at the Max Planck Institute for Informatics in Saarbrücken/Germany, the University of California in Berkeley/USA, and the University of Amsterdam/The Netherlands, she held a W3 professorship for computer science at the University of Tübingen/Germany from 2019 to 2023. At the same time, she was conducting research as Senior Group Leader at the Max Planck Institute for Informatics in Saarbrücken/Germany and as Senior Researcher at the Max Planck Institute for Intelligent Systems in Tübingen/Germany. Since 2024, she has been Liesel Beckmann Distinguished Professor of Computer Science at the Technical University of Munich and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. Akata has already received numerous awards for her research activities, including the Lise Meitner Award for Excellent Women in Computer Science from the Max Planck Society in 2014, the Junior Scientist Award from the Werner-von-Siemens-Ring Foundation in 2019, and the Alfried Krupp Award from the Alfried Krupp von Bohlen und Halbach Foundation in 2023. In addition, Akata received an ERC Starting Grant from the European Commission in 2019. Capital magazine selected her as one of Germany’s “Top 40 under 40” in 2024.
The Leopoldina and the Commerzbank Foundation annually honour outstanding young scientists with the award “ZukunftsWissen – the Early Career Award from the German National Academy of Sciences Leopoldina and the Commerzbank Foundation”. Endowed with €50,000, the award honours scientists for outstanding achievements in a research field related to the main topic of the respective Leopoldina Annual Assembly and who, at the same time, address forward-looking challenges in their research. The relevance to the topic is broadly defined and may be drawn from any of the disciplines represented at the Leopoldina. Interdisciplinary research is particularly welcome. The prize winner gives a public lecture at the Annual Assembly.
The “ZukunftsWissen” Award will be presented to Zeynep Akata during the Leopoldina Annual Assembly in Halle (Saale) on Thursday, 25 September 2025. This year’s event focuses on the topic of artificial intelligence