Ton Dieker


419 S.W. Mudd
Mail Code 4721

Tel(212) 853-0683

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.

Research Interests

Stochastic networks and resource allocation problems, design and analysis of stochastic simulation algorithms.

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. He serves on the editorial boards on several journals in Operations Research and Applied Probability.

Research Experience

  • Goldstine Postdoctoral fellow, IBM Watson Research Center, 2007–2008
  • Postdoctoral researcher, University College Cork, Ireland, 2006–2007

Professional Experience

  • Associate Professor of Industrial Engineering and Operations Research, Columbia University, 2014–present
  • Associate Professor of Industrial and System Engineering, Georgia Institute of Technology, 2013–2015
  • Assistant Professor of Industrial and System Engineering, Georgia Institute of Technology, 2008–2013


  • Institute for Operations Research and the Management Sciences (INFORMS)
  • Institute for Mathematical Statistics (IMS)
  • Netherlands Society for Statistics and Operations Research (VVS)

Honors & Awards

  • Air Force NYC Civic Leader tour, 2017
  • White House PECASE award, 2016
  • IBM Faculty award, 2013
  • Erlang Prize, 2012

Selected Publications

  • A. B. Dieker, S. Ghosh, and M. Squillante, “Optimal resource capacity management for stochastic networks,” Operations Research, 65, p. 221-241, 2016.
  • A. B. Dieker and S. Vempala, “Stochastic billiards for sampling from the boundary of a convex set,” Mathematics of Operations Research, 40, p. 888–901, 2015.
  • A. B. Dieker, T. Mikosch, “Exact simulation of Brown-Resnick random fields at a finite number of locations,” Extremes, 18, p. 301–314, 2015.
  • A. B. Dieker, B. Yakir, “On asymptotic constants in the theory of Gaussian processes,” Bernoulli, 20, p. 1600–1619, 2014.
  • A. B. Dieker, X. F. Gao, “Positive recurrence of piecewise Ornstein-Uhlenbeck processes and common quadratic Lyapunov functions,” The Annals of Applied Probability, 23, p. 1291–1317, 2013.
  • A. B. Dieker, J. Shin, “From local to global stability in stochastic processing networks through quadratic Lyapunov functions,” Mathematics of Operations Research, 38, p. 638–664, 2013.