Prof. Dr. Zeynep Akata
Academic Career and Research Areas
Prof. Akata's (*1986) field of research is explainable machine learning. Her goal is to build transparent computer algorithms that can make comprehensible decisions. her approach combines different methods of machine vision, machine learning and natural language processing. Her scientific vision is to create a self-explanatory artificial intelligence (AI) that can learn through minimal feedback and interact reliably with humans.
Prof. Akata is a Liesel Beckmann Distinguished professor of Computer Science at TUM and the director of the Institute for Explainable Machine Learning at Helmholtz Munich. After completing her PhD at the INRIA Rhone Alpes (2014), she worked as a post-doctoral researcher at the Max Planck Institute for Informatics (2014-17) and at University of California Berkeley (2016-17) and as an assistant professor at the University of Amsterdam (2017-19). Before moving to Munich in 2024, she was a professor (W3) at the University of Tübingen.
Awards
- Alfried Krupp Young Researcher Award (2023)
- European Computer Vision Association Young Researcher Award (2022)
- German Pattern Recognition Award (2021)
- Werner-von-Siemens-Ring Young Researcher Award (2019)
- ERC Starting Grant (2019)
Key Publications (all publications)
Generative Adversarial Text to Image Synthesis International Conference on Machine Learning (ICML), 2016 Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee.
AbstractZero-Shot Learning: A Comprehensive Evaluation of the Good, the Bad and the Ugly IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019 Yongqin Xian, Christoph Lampert, Bernt Schiele, Zeynep Akata.
AbstractFeature Generating Networks for Zero-Shot Learning IEEE Computer Vision and Pattern Recognition (CVPR) 2018 Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata.
AbstractIn-Context Impersonation Reveals Large Language Models' Strengths and Biases Neural Information Processing Systems (NeurIPS), 2023 Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, Zeynep Akata.
AbstractFantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model International Conference on Learning Representations (ICLR), 2024 Karsten Roth, Lukas Thede, A. Sopthia Koepke, Oriol Vinyals, Olivier Hénaff, Zeynep Akata.
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