Wouter Kouw is a researcher in the Bayesian Intelligent Autonomous Systems lab of the Electrical Engineering department. 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 analyzing, designing and developing probabilistic machine learning systems based on models of information processing in the brain. Specifically, variational Bayesian inference algorithms implemented as message passing on factor graphs. His work has been applied to signal processing and control, with the latest projects moving towards mobile robotics. He is a former Niels Stensen Fellow and currently works part-time as a Research Associate at Sioux Technologies.
Nature has produced elegant forms of information processing that I believe can help us tackle challenges in modern society.
Wouter Kouw holds a research master's degree in Neuroscience from Maastricht University (graduated in 2013), and a doctorate in Computer Science from TU Delft (defended in 2018). He has done academic work internationally: in 2012, he worked at the Computational Vision & Neuroscience lab of the Werner Reichardt Centre for Integrative Neuroscience in Tübingen (Germany), in 2016 at the department of Computer Science of Cornell University (USA) and in 2018 at the Datalogisk Institut of Copenhagen University (Denmark). In 2019, he started as a post-doctoral researcher at TU Eindhoven, where he is now leading a research direction as an assistant professor.
Variational message passing for online polynomial NARMAX identification(2022)
On Epistemics in Expected Free Energy for Linear Gaussian State Space ModelsEntropy (2021)
Bayesian joint state and parameter tracking in autoregressive models(2020)
Current Educational Activities
- Research Associate, Sioux Technologies B.V.