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Zeynep Akata is a distinguished professor in the field of Interpretable and Reliable Machine Learning at the Technical University of Munich. Her research focuses on explainable machine learning, aiming to develop transparent algorithms that can make comprehensible decisions. Her approach integrates methods from machine vision and natural language processing to create artificial intelligence systems that can effectively learn with minimal feedback and interact reliably with humans. Prior to her current role, she obtained her PhD from INRIA Rhone Alpes in 2014 and worked as a post-doctoral researcher at the Max Planck Institute for Informatics and the University of California, Berkeley. She served as an assistant professor at the University of Amsterdam from 2017 to 2019. In 2024, she will transition to a professorship at the University of Tübingen and currently directs the Institute of Explainable Machine Learning at Helmholtz Munich.
Technical University of Munich • Munich, Germany
Leading research and teaching in Interpretable and Reliable Machine Learning.
University of Amsterdam • Amsterdam, Netherlands
Conducted research in machine learning and supervised graduate students.
Max Planck Institute for Informatics • Saarbrücken, Germany
Worked on advanced machine learning models and applications.
University of California, Berkeley • Berkeley, USA
Research in the intersection of machine learning and AI.