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Wouter is an assistant professor in Operations Research and Financial Engineering. His research- and teaching activities center around optimization, simulation and machine learning - in particular reinforcement learning - with a focus on applications in logistics and finance. He teaches a variety of BSc and MSc courses, primarily in the Industrial Engineering & Management (IEM) program, covering topics such as reinforcement learning, option pricing, Markov decision processes and simulation optimization. Additionally, he has supervised over 100 students for their thesis research, and teaches reinforcement learning at the PhD level both nationally and internationally.

His research has been published in outlets such as Transportation Science and the International Conference on Learning Representations (ICLR). He has acquired and is involved in national research projects such as Logiquay (NWA), fMaas (NWA) and DReSC (Dinalog and Health Holland), as well as European ones such as the  EU COST Action CA19130 Fintech and AI in Finance and the Marie Sklodowska-Curie Action (MSCA) Digital Finance. He supervises a number of PhD candidates in these projects.

For the MSCA Digital Finance project, Wouter is a member of the Executive Board and leads the European training activities. Within the UT, he is chairman of the IEM Program Committee, member of the curriculum team for the IEM satellite program that is presently under development, and chairman of the Reinforcement Learning Network.

Through his activities, Wouter seeks to make an impact on enhancing decision making under uncertainty in an array of corporate and societal problems. By combining machine learning techniques and optimization techniques, novel solution methods can be designed to make a positive impact in terms of performance, risk mitigation, transparency and regulatory compliance. For this, close and ongoing interactions between universities, industry and governmental bodies is essential.

Expertise

  • Social Sciences

    • Logistics
    • Simulation
    • Costs
    • Problem
    • Urban Areas
  • Computer Science

    • Dynamic Programming
    • Reinforcement Learning
    • Models

Organisations

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