Zeynep Akata

An editor has performed a search and found that sufficient sources exist to establish the subject's notability. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Zeynep Akata" – news · newspapers · books · scholar · JSTOR (May 2022) (Learn how and when to remove this message)

This biography of a living person needs additional citations for verification, as its only attribution is to self-published sources; articles should not be based solely on such sources. Please help by adding reliable, independent sources. Immediately remove contentious material about living people that is unsourced or poorly sourced. (April 2022) (Learn how and when to remove this message)

Zeynep Akata is a Liesel Beckmann Distinguished professor of computer science at the Technical University of Munich^[1] where she leads the Interpretable and Reliable Machine Learning chair. Akata is also the director of the Helmholtz Institute for Explainable Machine Learning.

Akata received her undergraduate degree in Trakya University^[2] in Turkey, her MSc from RWTH Aachen and Ph.D. in computer science at the INRIA Grenoble-Rhônes-Alpes. She was a post-doctoral research fellow at the Max Planck Institute for Informatics with Bernt Schiele and at University of California, Berkeley with Trevor Darrell. Akata was an assistant professor at the University of Amsterdam from 2017 to 2019 and she was a full professor at the Cluster of Excellence Machine Learning at the University of Tübingen between 2019 and 2023. While in Tübingen, Akata was also a senior research scientist at the Max Planck Institute for Intelligent Systems, Tübingen.^[3]

Akata's research interests focus on Explainable Multimodal Machine Learning which is in the intersection between Machine Learning, Computer Vision and Natural Language Processing. Below are some of the subfields that she has been actively working on:

- Zero-Shot Learning and Few Shot Learning

- Weakly Supervised Learning

- Generative modeling with multimodal large language models and large language models: data augmentation via GANs, VAEs, SD

- Audio-visual learning, event detection

- Explainability: Transparency, Robustness, Bias mitigation in multimodal large language models

- Explainability: Attribute based learning, disentangled representation learning, attention, uncertainty quantification

- Fine-grained classification: prototypical learning, automatic part discovery

- Integrating language guidance in various computer vision tasks

- Green model adaptation: Adapting existing models to new tasks without increasing their environmental footprint, model compression

- Knowledge Distillation, Model Editing.^[4][5]

• 2023 Alfried Krupp Prize ^[6]
• 2022 ECVA Young Researcher Award ^[7]
• 2021 German Pattern Recognition Award^[8]
• 2019 ERC Starting Grant^[9][10]
• 2019 Young Scientist Honour from the Werner-von-Siemens-Ring foundation^[11]
• 2014 Lise-Meitner Award for Excellent Women in Computer Science from the Max Planck Society^[12]