Ton Dieker develops mathematical and computational tools to model and analyze systems that evolve randomly over time. Applications include service operations, financial risk, and network performance.
A centerpiece of his research is QPLEX, a computational methodology for modeling and analyzing nonstationary stochastic systems introduced in the open-access book "QPLEX: A Computational Modeling and Analysis Methodology for Stochastic Systems" (Springer, 2025, with Steven T. Hackman) and supported by a Python package at qplex.org. His current work extends QPLEX toward dynamic control and optimization of stochastic systems. A parallel research thread explores how modern AI tools can lower the barrier to building computational stochastic models.
Dieker received an MSc in Operations Research from the Vrije Universiteit Amsterdam in 2002 and a PhD degree in Mathematics from the University of Amsterdam in 2006.
