Wouter Kouw

Wouter Kouw

Assistant Professor

TU Eindhoven

Biography

Wouter Kouw is a researcher in the Bayesian Intelligent Autonomous Systems lab 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 analyzing, designing and developing probabilistic machine learning systems based on models of information processing in the brain. Specifically, the development of variational Bayesian inference algorithms implemented as message passing on graphs. His work has been applied to nonlinear system identification, adaptive control systems and mobile robotics.

Interests
  • Machine Learning
  • Bayesian Inference
  • Intelligent Systems
  • Neural Computation
Education

  • PhD in Computer Science, 2018

    Delft University of Technology

  • MSc in Neuroscience, 2013

    Maastricht University

Featured Publication

Wouter Kouw
December 2023 IEEE Control Systems Letters
Information-seeking polynomial NARX model-predictive control through expected free energy minimization
Information-seeking polynomial NARX model-predictive control through expected free energy minimization

An adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the parameters of a nonlinear autoregressive exogenous model.

Publications

Tim Nisslbeck, Wouter Kouw.
Insights into sample efficient quadrupedal robot locomotion.
Submitted to Frontiers in Robotics & Artificial Intelligence, 2024.
Code Project
Chengfeng Jia, Jie Ma, Bert de Vries, Wouter Kouw.
Bayesian inference of collision avoidance intent during ship encounters.
IEEE Transactions on Automation Science and Engineering, 2024.
PDF Cite Code DOI
Sepideh Adamiat, Wouter Kouw, Bart van Erp, Bert de Vries.
Message passing-based Bayesian control of a Cart-Pole system.
Submitted to European Signal Processing Conference, 2024.
Project
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Projects

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All Active Inference Walking robots
FEPQuad
Design of an intelligent autonomous system for quadrupedal robot locomotion using Active Inference (AIF). AIF is a neuroscience-based framework of perception and action, and could bring us closer to how animals learn to walk.
FEPQuad
BayesBrain
Computation in biological brain tissue consumes several orders of magnitude less power than silicon-based systems. Motivated by this fact, this project aims to develop the world’s first hybrid neuro-in-silico Artificial Intelligence (AI) computer.
BayesBrain
BayesFilters
Design and development of Bayesian filters and smoothers for signal processing applications such as spectroscopy, radar object tracking, wireless communication and system identification.
BayesFilters

Talks

Natural AI for control and mobile robotics
Natural AI is Artificial Intelligence based on physics principles. The Free Energy Principle allows for designing agents that learn in real-time, are low-power and are explainable. They are powered by Bayesian mechanics, updating beliefs over states of the world and actions as data streams in. At BIASlab and LazyDynamics, we have developed a toolbox for scalable automatic Bayesian inference called RxInfer.
15 Apr 2024 13:30 — 15:00 University of Amsterdam, Netherlands
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Teaching

TU/e 5XIF0
Neuro Computation: a BSc course covering the computational properties of neurobiological systems and how to design spiking neural networks using mixed-signal “neuromorphic” electronic circuits.
Last updated on 28 Jun 2023
TU/e 5XIF0
TU/e 5XSL0
Fundamentals of Machine Learning: a BSc course that covers the basics of machine learning, including linear classification and regression, support vector machines, random forests, and neural networks.
Last updated on 4 Jan 2022
TU/e 5XSL0
TU/e 5SSD0
Bayesian Machine Learning & Information Processing: a MSc course that covers the fundamentals of the Bayesian (i.e., probabilistic) approach to machine learning and information processing systems.
Last updated on 4 Jan 2022
TU/e 5SSD0

Contact