Combinatorial machine learning

Decision Trees Versus Systems of Decision Rules

New book by KAUST Ph.D. Students Kerven Durdymyradov, Azimkhon Ostonov, and Professor Mikhail Moshkov published by Springer

1 min read · Thu, Jan 16 2025

decision trees combinatorial optimization Combinatorial machine learning machine learning machine learning algorithm

New book by Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov, and Professor Mikhail Moshkov explores decision trees and rule systems using rough set theory.

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

1 min read · Wed, Jan 22 2020

Dynamic programming Combinatorial machine learning

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

Dynamic programming Combinatorial machine learning Complexity of algorithms discrete optimization

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.