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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About the keynote talk:

Title: QPLEX Decision Processes

Work by: Ton Dieker, Steven T. Hackman, Zitong Wang, and Yunhao Yan

Abstract: We introduce a QPLEX Decision Process (QDP) as a model for dynamic control of queueing systems with non-stationary arrivals, general service distributions, and service-level chance constraints. QDPs integrate QPLEX, a computational modeling methodology for transient analysis of stochastic systems, into a nonlinear Markov decision framework. Since QPLEX approximations use nonlinear transition probabilities with orders-of-magnitude smaller state spaces, QDPs circumvent the curse of dimensionality associated with general service times. Via forward and backward iterative schemes, we can rapidly compute gradients deterministically on the much smaller state space, eliminating sampling variance. We further address optimization through natural-gradient-inspired methods with block-diagonal Fisher approximations. To illustrate the QDP methodology, we formulate a single-station dynamic pricing problem with non-stationary demand as a QDP. When the reward structure uses waiting and terminal costs, our approach can find near-optimal policies in seconds on a single CPU; when the reward structure uses penalties for deviating from service-level chance constraints, the optimization landscape is substantially more challenging yet our approach can find a high-quality, practical policy in approximately a minute on a single CPU.

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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About the keynote talk:

Title: Information Design in Queueing Systems: From Fluid Limits to Human Limits

Work by: Rouba Ibrahim, Philipp Afeche, Junqi Hu, Vahid Sarhangian, Arturo Estrada, and Mirko Kremer

Abstract: We study the use of delay information in queueing systems from two perspectives: a macro system-level view and a micro behavioral view. Our approach utilizes a diverse methodological toolkit, ranging from heavy-traffic limits to controlled behavioral experiments with human subjects. By bridging these methodologies, we highlight robust insights that emerge across different modeling techniques. Specifically, we show that more information is not always better and that vague or binary signaling can paradoxically enhance performance. Finally, we address the gap between rigorous mathematical modeling and the nuances of human decision-making, offering a way forward to integrate both into a unified, behaviorally-aware framework for operational design.

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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About the keynote talk:

Title: More Assumptions, Worse Decisions? A Distributionally Robust View on Stochastic Models

Work by: Johan van Leeuwaarden

Abstract: Stochastic optimization often rewards detailed modeling: the more we specify the distribution of uncertainty, the more precise our decisions seem. This talk asks whether such assumptions might actually make decisions more fragile, and whether we should instead aim for more robust, distribution-free analysis. Distributionally robust optimization (DRO) takes this view. Rather than fixing a single distribution, it considers all distributions consistent with limited information, such as mean and variance. This leads to a max–min problem: choose a decision that performs well against the worst-case distribution. While the search for the worst case is infinite-dimensional, it often becomes tractable through duality, linking probability and optimization (via primal–dual structure). We illustrate DRO through examples from inventory, queueing, and pricing, and highlight recent progress. New results handle higher-dimensional models (with applications in queues and inventory), and show how in pricing small changes in information can trigger sudden shifts in decisions, driven by changes in the worst-case distribution.