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.
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.