Biography
Wouter Kouw is a researcher in the Bayesian Intelligent Autonomous Systems lab at the Electrical Engineering department of TU Eindhoven. He has a dual background in neuroscience and computer science, and is most interested in neuro-inspired forms of artificial intelligence. His research focuses on designing, developing and analyzing probabilistic machine learning systems that learn from interactions with their environment, most notably active inference agents. Internally, these agents perform Bayesian inference by way of message passing in graphical models. They are deployed to mobile robots to assist humans with dangerous, dirty or dull work.
- Intelligent autonomous systems
- Probabilistic machine learning
- Variational Bayesian inference
- Neural information processing
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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
Gaussian Variational Inference with Non-Gaussian Factors for State Estimation.
IEEE Robotics & Automation Letters, 2025.
Bayesian autoregression to optimize temporal Matérn-kernel Gaussian process hyperparameters.
International Conference on Probabilistic Numerics, 2025.
Message passing-based inference in an autoregressive active inference agent.
International Workshop on Active Inference, 2025.
Spike-timing dependent plasticity for Bernoulli message passing.
International Workshop on Active Inference, 2025.
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.