Dynamic programming

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

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KAUST

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

Ph.D. Student’s Research Listed in The American Journal of Transplantation’s “Top 10 Articles of 2019”

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Research work carried out by Michał Mańkowski, a Ph.D. student in the KAUST Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently selected for inclusion by the editors of The American Journal of Transplantation (AJT) in their “Top 10 Articles of 2019.

Michal A. Mankowski

Postdoctoral Research Fellow, Computer Science
As a member of the KAUST Extensions of Dynamic Programming, Machine Learning, Discrete Optimization research group (TREES), Mankowski is currently developing an algorithmic and theoretical framework for combinatorial optimization. The application side of his research centers on healthcare analytics and operations research applied to the healthcare domain. Research Interests Dynamic programming Combinatorial optimization Complexity of algorithms​ Healthcare Analytics Education Profile Ph.D. (Computer Science), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2020. M.S.