ton-dieker

Ton Dieker

Professor of Industrial Engineering and Operations Research; Chair, Department of Industrial Engineering and Operations Research

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.

Research Areas

  • Data Analytics
  • Decision Analysis
  • Resource Allocation
  • Risk Management
  • Service Systems
  • Stochastic Modeling and Simulation
  • Business Analytics
  • Artificial Intelligence

Additional Information

  • Professional Affiliations
    • Institute for Operations Research and the Management Sciences (INFORMS)
    • Institute for Mathematical Statistics (IMS)
    • Netherlands Society for Statistics and Operations Research (VVS)
  • Honors & Awards
    • PECASE award, White House, 2016
    • Erlang Prize, INFORMS Applied Probability Society, 2012
    • Goldstine Fellowship, IBM Research, 2007
  • Education
    • PhD, Mathematics, University of Amsterdam
    • MS, Econometrics and Operations Research, Vrije Universiteit Amsterdam