Wouter 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.
Interests
- Intelligent agents
- Machine learning
- Bayesian inference
- Neural computation
Education
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PhD in Computer Science, 2018
Delft University of Technology
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MSc in Neuroscience, 2013
Maastricht University