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Personal profile

Research profile

Intelligent behaviour fascinates me: how animals can efficiently process a flood of sensory signals and learn to take actions to survive. Nature has produced elegant solutions for information processing that I believe can help us tackle challenges in modern society. I am passionate about taking what we know from how brains process information, to making intelligent machines.

My work in the Bayesian Intelligent Autonomous Systems lab focuses on computational efficiency. Internally, an agent must trade off the costs of performing a computation with prediction accuracy. Higher efficiency will allow us to deploy agents for more complicated tasks, such as long-term motor planning, safe autonomous driving and optimal search using drone swarms.

Most of my previous projects revolve around sampling bias in machine learning. Mainly, I designed robust estimators for statistical models, which have been applied to image, signal and natural language processing problems.

Education/Academic qualification

Doctor, Delft University of Technology

1 Sep 201331 Aug 2017

Master, Maastricht University

1 Sep 201131 Aug 2013

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Research Output

  • 24 Citations
  • 6 Conference contribution
  • 3 Article

A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Kouw, W. M., Ørting, S. N., Petersen, J., Pedersen, K. S. & de Bruijne, M., 2019, International Conference on Information Processing in Medical Imaging. Bao, S., Gee, J. C., Yushkevich, P. A. & Chung, A. C. S. (eds.). Cham: Springer, p. 360-371 12 p. (Lecture Notes in Computer Science; vol. 11492 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
  • 24 Downloads (Pure)

    A review of domain adaptation without target labels

    Kouw, W. M. & Loog, M., 7 Oct 2019, In : IEEE Transactions on Pattern Analysis and Machine Intelligence.

    Research output: Contribution to journalArticleAcademicpeer-review

    Open Access
    File
  • 81 Downloads (Pure)

    Back to the future: sequential alignment of text representations

    Bjerva, J., Kouw, W. M. & Augenstein, I., 11 Nov 2019, AAAI Conference on Artificial Intelligence. 34 ed. Association for the Advancement of Artificial Intelligence, 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access
    File
  • Learning an MR acquisition-invariant representation using Siamese neural networks

    Kouw, W. M., Loog, M., Bartels, L. W. & Mendrik, A. M., Apr 2019, ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. Piscataway: Institute of Electrical and Electronics Engineers, p. 364-367 4 p. 8759281

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access
    File
  • 2 Downloads (Pure)

    Robust importance-weighted cross-validation under sample selection bias

    Kouw, W. M., Krijthe, J. H. & Loog, M., 5 Dec 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019. Piscataway: Institute of Electrical and Electronics Engineers, 6 p. 8918731

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access
    File
  • 13 Downloads (Pure)

    Prizes

    Niels Stensen Fellow

    Wouter Kouw (Recipient), Oct 2017

    Prize: OtherFellowships & membershipsScientific