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ELLIS Health

ellis Program

  • Connect and promote the ELLIS vision within the broad areas of human health
  • Demonstrate the impact of AI/ML on biomedicine and health
  • Feed back key challenges of health applications into AI/ML methods development
  • Foster training and education of the next generation of interdisciplinary scientists at the interface of health and AI/ML

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ELLIS Robot Learning: Closing the Reality Gap!

ellis Program

This program focusses on central questions for closing the real-world gap for intelligent systems: 
How should the robot move? How to act? How to interact? How can sensorimotor behavior be improved by machine learning approaches?

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Geometric Deep Learning

ellis Program

  • Machine learning on non-Euclidean domains
  • 4G: geometric, graph, group, gauge convolutions
  • Applications: computer vision, graphics, social networks, chemistry, biology, physics, medicine

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Human-centric Machine Learning

ellis Program

  • help to ensure widespread benefits to, and acceptance from, the public by guaranteeing:
  • transparency, clear accountability, interpretability and fairness of the algorithmic decisions
  • amenable to legal and technical certification, accountability and verifiability.

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Interactive Learning and Interventional Representations

ellis Program

  • Principles of learning-in-the-loop systems
  • Reinforcement learning
  • Causal inference
  • Interacting learning systems (multi-agent learning, games, networks)

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Machine Learning and Computer Vision

ellis Program

Computer Vision has been revolutionalised by Machine Learning. Our goal is to connect classical Vision algorithms and modern machine learning more explicitly.

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Machine Learning for Earth and Climate Sciences

ellis Program

Goal: Model and understand the Earth system with Machine Learning and Process Understanding

  • Spatio-temporal anomaly and extreme events detection, anticipation and attribution
  • Data-driven dynamic modelling and forecasting
  • Hybrid modeling: linking physics and machine learning models
  • Causal inference, Learning and explaining feature representations
  • Earth and Climate model emulation, generative modelling and data-model fusion
  • Benchmark synthetic and real datasets

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Natural Intelligence

ellis Program

The standard paradigm of machine learning is task-centric.
Natural intelligence is agent-centric: a single brain shaped through evolution learns to perform all tasks.
Proposed topics:

  • Lifelong learning
  • Deep semantics and cross-domain learning
  • Shaping inductive bias via neural network structure
  • Adaptive resource deployment
  • Social reasoning

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Quantum and physics based machine learning

ellis Program


  • to exploit quantum effects in machine learning
  • to accelerate and improve energy efficiency of machine learning algorithms through dedicated physical implementations 
  • to use machine learning methods to advance understanding of quantum information processing

Program website:

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Robust Machine Learning

ellis Program

  • Principles and methods for Robust ML
  • Quantification and verification of Robust ML
  • Applications in health, environmental sciences, design, autonomous vehicles, industrial control.

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Theory, Algorithms and Computations of Modern Learning Systems

ellis Program

  • Modern ML software needs manual tuning and is badly understood conceptually
  • We will bring together experts with diverse backgrounds to advance the algorithmic foundations of ML, to build efficient and reliable learning systems with theoretical guarantees.