Joni Dambre

Group leader

Machine learning theory and applications:

  • Reservoir computing
  • Physical reservoir computing
  • Compliant robotics
  • Deep learning
  • Brain-computer interfacing

Jonas Degrave

Phd Student
  • Oncilla Robot.
  • Rich Motor Skills.
  • Reservoir Computing.

Former Members

Michiel Hermans

  • Training recurrent neural networks.
  • Neural architectures.
  • Photonic networks.
  • Generalising training algorithms to physical systems.

... and drinking coffee.

Pieter Buteneers


The applicability of Machine Learning in real-time epileptic seizure detection and the Home-MATE project.

Juan Pablo Carbajal


I am a physicist interested in the topics:

  • Physical models of computation.
  • Physical modelling.
  • Nonlinear systems (normal modes, resonances).
  • Physics based machine learning.

Get my CV here.

To know more, visit my personal webpage.

Tim Waegeman

  • Robotics
  • Adaptive motor control
  • Underactuated control
  • Reservoir computing
  • Reinforcement learning

Philémon Brakel

Phd Student
  • Models for sequential data.
  • Non-convex optimization.
  • Graphical models.

Sander Dieleman

Phd Student
  • Feature learning, unsupervised learning
  • Deep learning
  • Music information retrieval
  • Recommender systems, collaborative filtering
  • Neural networks

Pieter-Jan Kindermans

  • Machine Learning.
  • Unsupervised Learning.
  • Brain-Computer Interfaces.
  • Bayesian Non-parametric methods.
  • Transfer Learning.

Aäron van den Oord

Phd Student
  • Machine Learning.
  • Generative Models.
  • Large-Scale / Big Data.
  • Deep Learning.