Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov ...

This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

Dynamic programming is a methodological framework for solving optimisation problems that evolve over time by breaking them into simpler subproblems. Central to this approach is the principle of ...

Decision making in stochastic and dynamic environments plays an essential role in many areas, including finance, robotics, game theory, revenue management and social networks. This course aims to gain ...

The Annals of Applied Probability, Vol. 28, No. 1 (February 2018), pp. 1-34 (34 pages) In this paper, we aim to develop the stochastic control theory of branching diffusion processes where both the ...

Dan Zhang is Associate Professor of Operations Management at Leeds School of Business, University of Colorado Boulder. Dr. Zhang teaches in the area of operations management and data analytics in ...