Keynote speakers

Ton Dieker is a Professor of Industrial Engineering and Operations Research at Columbia University and a member of the university’s Data Science Institute. He earned his M.Sc. from VU Amsterdam in 2002 and his Ph.D. from the University of Amsterdam in 2006. Before joining Columbia, he held a faculty position at Georgia Tech, where he was the Fouts Family Associate Professor. His research focuses on applied probability and its intersections with data science and operations research. He has received several honors, including the Goldstine Fellowship from IBM Research, the Erlang Prize from the INFORMS Applied Probability Society, and the Presidential Early Career Award for Scientists and Engineers (PECASE). In addition, he has contributed to the academic community through editorial roles with journals in Operations Research and Applied Probability. He co-authored "QPLEX: A Computational Modeling and Analysis Methodology for Stochastic Systems" with Steven T. Hackman, published by Springer Nature in 2025. This book introduces a computational framework for modeling and analyzing nonstationary stochastic systems.

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I am a professor at the School of Management of University College London, where I currently head the Operations & Technology group. I serve on the editorial boards of Management Science and Queueing Systems as an associate editor, and I serve as an Area co-Editor of the Operations and Supply Chains area at Operations Research. My research interests lie in service operations management.  I am especially interested in the operational management of queueing systems, from both mathematical and behavioral perspectives. 

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Johan S.H. van Leeuwaarden (PhD in math, 2005, Eindhoven) is a professor of Stochastic Operations Research at the Tilburg School of Economics and Management (TiSEM), Department of Econometrics and Operations Research, where he works on probability, stochastic networks, queueing theory, stochastic optimization, and approaches for decision-making under uncertainty. His research focuses on modelling and optimizing complex systems influenced by randomness, with applications in service systems, networks, random graphs and large-scale operations. Current research themes also include scaling limits, stochastic-process limits, distribution-free pricing and distributionally robust optimization. 

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