Computational Health Center

Institute of Explainable Machine Learning

The Institute of Explainable Machine Learning (EML) is dedicated to advancing the understanding of AI models, thereby facilitating the development of more reliable and transparent systems.
We are always looking for motivated students and researchers to join our group. If you are interested, check our open positions.

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The Institute of Explainable Machine Learning (EML) is dedicated to advancing the understanding of AI models, thereby facilitating the development of more reliable and transparent systems.
We are always looking for motivated students and researchers to join our group. If you are interested, check our open positions.

Visit our Website

Follow us on Bluesky

Follow us on X

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About our Research

Our research directions include:

  • multi-modality
  • explainability
  • zero-shot learning

Check out our latest publications for more details.

Our Team

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Zeynep Akata

Director Institute of Explainable Machine Learning

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Kirill Bykov

Postdoctoral Researcher

Maria Alejandra Bravo

Postdoctoral Researcher

Quentin Bouniot

Postdoctoral Researcher

Jessica Bader

PhD Researcher

Leander Girrbach

PhD Researcher

EML News

Zeynep Akata receives 2025 ZukunftsWissen Award

AI, Awards & Grants, Computational Health, EML,

Zeynep Akata Receives 2025 ZukunftsWissen Award

Professor Zeynep Akata has been awarded the 2025 ZukunftsWissen Prize by the German National Academy of Sciences Leopoldina and the Commerzbank Foundation in recognition of her outstanding scientific contributions. The €50,000 award was presented on…

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AI, Computational Health, EML, Health AI,

“Extending the Limits of Our Curiosity With the Help of Technological Tools.”

Prof. Zeynep Akata is selected as one of the leading "Top 40 Under 40". Read her compelling interview!

Institute in Numbers

2
Postdoctoral Researchers
11
PhD Researchers
3
Collaborating Researchers
9
Collaborating Faculty

EML Publications

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Artificial Neural Network

International Conference on Machine Learning, ICML 2025

Understanding the Limits of Lifelong Knowledge Editing in LLMs

Lukas Thede, Karsten Roth, Matthias Bethge, Zeynep Akata, Tom Hartvigsen

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Machine Learning Algorithms

IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2025

Context-Aware Multimodal Pretraining

Karsten Roth, Zeynep Akata, Dima Damen, Ivana Balazevic*, Olivier J. Henaff*

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Data transmission channel.

IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2025

COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training

Sanghwan Kim, Rui Xiao, Mariana-Iuliana Georgescu, Stephan Alaniz, Zeynep Akata

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Transfer und Netzwerke

IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2025

FLAIR: VLM with Fine-grained Language-informed Image Representations

Rui Xiao, Sanghwan Kim, Mariana-Iuliana Georgescu, Zeynep Akata, Stephan Alaniz

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KI Bioengineering

IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2025

How to Merge Your Multimodal Models Over Time?

Sebastian Dziadzio*, Vishaal Udandarao*, Karsten Roth*, Ameya Prabhu, Zeynep Akata, Samuel Albanie, Matthias Bethge

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network social online, background 3d illustration rendering, machine deep learning, data cloud storage digital, science neuron, plexus cell brain, futuristic connecting, technology system

International Conference on Learning Representations, ICLR 2025

Disentangled Representation Learning with the Gromov-Monge Gap

Théo Uscidda*, Luca Eyring*, Karsten Roth, Fabian J Theis, Zeynep Akata*, Marco Cuturi*

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Neurological net system

International Conference on Learning Representations, ICLR 2025

Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences

Shuchen Wu, Mirko Thalmann, Peter Dayan, Zeynep Akata, Eric Schulz

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Data science

International Conference on Learning Representations, ICLR 2025

Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)

Leander Girrbach, Stephan Alaniz, Yiran Huang, Trevor Darrell, Zeynep Akata

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Raum Strahlung

International Conference on Learning Representations, ICLR 2025

Decoupling Angles and Strength in Low-rank Adaptation

Massimo Bini, Leander Girrbach, Zeynep Akata

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Contact Coordinator

Viktoria Schweiberger

Foreign Language Assistant