Summary

PhD candidate at the Complexity Science Hub & TU Wien. Machine learning for complex adaptive systems, with a focus on supply chain dynamics.

Education

• ML methods for dynamic graph-structured data
  Complexity Science Hub & TU Wien, Vienna
• Physics (Theoretical Physics track)

  2023
  Universiteit van Amsterdam

  GPA: 9.0/10
• Physics & Mathematics

  2020
  Radboud Universiteit, Nijmegen

  GPA: cum laude

Work Experience

• Quantitative Analyst (part-time)

  2021 - 2022
  Gimli

  Game-theoretic models for Flashbots MEV blind auction bid optimisation.
• Intern Analyst (part-time)

  2020 - 2021
  MindHash

  Machine learning for object extraction and tracking in LiDAR point cloud data.
• 2020 - 2021
  Blommers Coffee Roastery
• Undergraduate Researcher

  2020 - 2020
  University of Washington, Seattle

  Computational research on constrained mutations in genome space.
• Teaching Assistant / Tutor

  2015 - 2020
  Radboud Universiteit

Skills

Publications

• Generalized Degrees for Scalable Discrete Time Dynamic Graph Generation

  2025
  Learning on Graphs Conference

  K. van Driel, L.N. Ialongo, P.A. Astudillo-Estévez, S. Thurner. Oral presentation (extended abstract).

  View Publication
• Prediction Mismatch Responses Arise as Corrections of a Predictive Spiking Code

  2023
  bioRxiv preprint

  K. van Driel, L. Rudelt, V. Priesemann, F.A. Mikulasch.

  View Publication

Presentations

• Complexity science in the green transition

  2025
  European Forum Alpbach

  Alpbach, Austria
• Generalized Degrees for Scalable Discrete Time Dynamic Graph Generation

  2025
  Learning on Graphs Conference

  Oral presentation
• Summer School on Economic Networks

  2024
  University of Oxford

  Oxford, UK
• Volunteer

  2024
  ICLR
• CSH Winter School on Complex Systems

  2024
  Complexity Science Hub

  Vienna, Austria
• Summer School on Economic Complexity

  2024
  Maastricht University

  Maastricht, Netherlands

Languages

• Dutch

  Native
• English

  Fluent
• German

  B2