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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

We rethink the principles of interactive models of learning, exploring the role of causal modelling in bridging the gap between observational and interventional learning. The ultimate goal is to understand the organizing principles underlying robust intelligent behaviour, and to enable reliable learning-based decision systems for high-stakes real-world applications.

  • Principles of learning-in-the-loop systems
  • Online and 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.

  • Mid-level vision and image reconstruction
  • 3D Geometry from multiple views
  • Object and activity recognition

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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

The aim of the Ellis program Quantum and Physics based machine learning (QPhML) is to use concepts from quantum physics and statistical physics to develop novel machine learning algorithms with the ultimate aim to realize novel future, possibly energy efficient, hardware implementations. Objectives:

  • Exploit quantum effects in machine learning
  • Accelerate and improve energy efficiency of machine learning algorithms through dedicated physical implementations 
  • 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

  • Many contemporary ML algorithms are still comparably badly understood conceptually. As a result, they require manual tuning, can be inflexible and behave erratically.
  • The program connects experts with diverse backgrounds to advance the algorithmic foundations of ML. It will support the development of efficient and reliable learning systems with theoretical guarantees.