Biography
Wouter Kouw develops probabilistic machine learning systems, particularly active inference agents, for autonomous robots. By formulating Bayesian inference as message passing on graphical models, he builds hierarchical reactive agents that learn internal kinematics and adapt to uncertain dynamic environments. These agents are deployed on mobile robots and high-tech systems to automate hazardous tasks, creating safer work environments for people.
“Intelligence is more than just computation at scale - it’s about making sense of the world under uncertainty.”
- Autonomous intelligent systems
- Probabilistic machine learning
- Neuromorphic computing
- Biomimetic robotics
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PhD in Computer Science, 2018
Delft University of Technology
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MSc in Neuroscience, 2013
Maastricht University
Projects
Publications
Expected free energy as an information constraint on the Bethe Lagrangian.
International Workshop on Active Inference, 2026.
Balancing time and precision with active sensor selection and positioning.
International Workshop on Active Inference, 2026.
Expected free energy planning with learned external-camera measurement reliability.
International Workshop on Active Inference, 2026.
Expected free energy-based informative path planning for robotic mars exploration.
International Workshop on Active Inference, 2026.
A factor graph approach to scalable multi-output Gaussian process regression.
International Conference on Probabilistic Graphical Models, 2026.
Spike-based belief propagation in nonlinear dynamical systems.
International Conference on Neural Computation Theory and Applications, 2026.
Software
RxInfer is a powerful Julia package for event-driven variational Bayesian inference, and is used for probabilistic machine learning, signal processing, adaptive control and the design of intelligent agents.