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Research at the ELLIS Institute Tübingen spans a broad spectrum of foundational AI topics, including AI mechanisms, AI safety, probabilistic intelligence, cooperative machine intelligence, societal impacts, AutoML, computational and applied mathematics, deep models and optimization, robust machine learning, and resource-efficient AI. Explore the research of our Principal Investigators below.

Research Groups

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AI & Mechanisms

Rediet Abebe

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AI Safety and Alignment

Maksym Andriushchenko

The new AI Safety and Alignment Group focuses on developing technical solutions to reduce risks from general-purpose AI models.

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Algorithms and Society

Celestine Mendler-Dünner

Building theoretical and practical tools to support responsible and reliable machine learning in social context.

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AutoML

Frank Hutter

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Computational Applied Mathematics & AI Lab

T. Konstantin Rusch

The Computational Applied Mathematics & AI Lab (CAMAIL) is a research group at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems headed by T. Konstantin Rusch.

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Cooperative Machine Intelligence for People-Aligned Safe Systems

Sahar Abdelnabi

Developing safe, aligned, and steerable AI agents with emphasis on security, human aspects, and cooperative multi-agent systems.

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Deep Models and Optimization

Antonio Orvieto

Investigating the interplay between optimizer and architecture in Deep Learning, and new networks for long-range reasoning.

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Empirische Inferenz

Bernhard Schölkopf

The problems studied in the department can be subsumed under the heading of empirical inference. This term refers to inference performed on the basis of empirical data.

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Robust Machine Learning

Wieland Brendel

We use theoretical and empirical approaches to build machine vision systems that see and understand the world like humans.

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Safety- and Efficiency-Aligned Learning

Jonas Geiping

Investigating the feasibility of technical solutions to safety, security in machine learning.

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Science and Probabilistic Intelligence

Maximilian Dax

The group for Science and Probabilistic Intelligence (SPIN) combines foundational research on probabilistic AI with applied research in science.

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Wild, Efficient, and Innovative AI

Shiwei Liu