Zeynep Akata

Zeynep Akata is the Liesel Beckmann Distinguished Professor of Computer Science at the Technical University of Munich (TUM) and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. Previously she was a W3 Professor at the University of Tübingen (2019-2023) and held faculty and post-doctoral positions at the University of Amsterdam, UC Berkeley and the Max Planck Institute for Informatics. Her research focuses on multimodal learning and explainable artificial intelligence.

Education:

• PhD, University of Grenoble / INRIA Rhône-Alpes, 2014
• MSc, RWTH Aachen University, 2010
• BSc, Trakya University, Turkey, 2008

Research Interests:

Professor Akata’s group develops algorithms that learn from vision, language and other modalities simultaneously, with a strong emphasis on zero-shot, few-shot and continual learning. A central theme is making decisions interpretable, leading to work on explainable AI, concept bottleneck models, multimodal reasoning and human-aligned representation learning. Recent projects investigate large-scale multimodal language models, dataset distillation, model merging and continual knowledge editing.

Publication Trends:

Her 2024-2025 publications reveal a shift toward foundational large-scale models (diffusion, LLMs, vision-language transformers) while retaining the core themes of interpretability and generalization under limited supervision. Topics span dataset distillation, model merging, continual learning, fairness auditing of generative models and novel evaluation protocols for zero-shot learning systems.

Scientific Awards:

• Lise-Meitner Award for Excellent Women in Computer Science (2014)
• Young Scientist Honour, Werner-von-Siemens-Ring Foundation (2019)
• ERC Starting Grant, European Commission (2019)
• DAGM German Pattern Recognition Award (2021)
• ECVA Young Researcher Award (2022)
• Alfried Krupp Award (2023)

Advising & Funding:

Prof. Akata currently supervises or co-supervises 25+ PhD students across TUM and the University of Tübingen via ELLIS and IMPRS-IS doctoral programs. She holds major grants including an ERC Starting Grant and DARPA Explainable AI funding, and is a frequent program chair and area chair for premier conferences (CVPR 2024, ECCV 2026, NeurIPS, ICML, etc.).

Labs & Teams:

She leads the Institute for Explainable Machine Learning at Helmholtz Munich and heads the Multimodal Learning and Explainable AI group at TUM. The institute collaborates closely with the ELLIS Institute Tübingen and Cyber Valley ecosystem, and maintains close ties with the Max Planck Institute for Intelligent Systems and Informatics.