ton-dieker

Ton Dieker

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

Ton Dieker is an expert in random processes and computer simulation algorithms, and he develops tools so that data for one system can be used to make predictions about modified systems for which no data is available.

Such tools are useful to regulators in predicting how well banks respond to financial shocks, to scientists in predicting future climate under various carbon-emissions rates, and to engineers in predicting how factory layouts will boost performance.

Of particular interest to Dieker are tractable approximations of performance metrics in stochastic networks that can be used to quickly explore initial system designs, to reduce computational burdens associated with simulation, or even to eliminate the need for simulation altogether. Such approximations have the potential to improve operational efficiencies in hospitals, among other applications.

Research Areas

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

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