Events

Apr 11 - Apr 5, 2026

Data Analytics to Outcome Impact for Transplantation and Clinical Practice

Apr 9, 10:00 - 16:00

B3 R5209

Workshops

AI for healthcare Mathematical modeling smart health bioinformatics Data Analytics

Leveraging national data analytics and computational tools to advance transplantation and clinical practice.

May 10 - May 4, 2025

Relations between Decision Trees and Decision Rule Systems

Kerven Durdymyradov, Postdoctoral Research Fellow, Computer Science

May 4, 11:00 - 13:00

B5 L5 R5220

decision trees machine learning

This thesis investigates the complex transformation of decision rule systems into decision trees, establishing bounds on tree depth, developing polynomial-time path-finding algorithms and an optimal dynamic programming algorithm for minimum depth, and analyzing time-space complexity trade-offs for trees over infinite binary information systems.
On Complexity of Decision Trees for Decision Tables from Closed Classes

Azimkhon Ostonov, Postdoctoral Research Fellow, Computer Science

May 4, 09:00 - 11:00

B5 L5 R5220

decision trees machine learning

This thesis considers classes of decision tables closed under operations of removal of attributes (columns) and changing of decisions and study for the tables from these classes relationships among the minimum complexity of deterministic and nondeterministic decision trees and the complexity of the set of attributes attached to columns of the table, while also analyzing the algorithms for constructing decision trees for these decision tables.

May 3 - Apr 27, 2025

Towards Trustworthy News Recommendation Systems

Manal A. Alshehri, Ph.D. Student, Computer Science

May 1, 17:00 - 19:00

B3 L5 R5209

machine learning artificial intelligence personalized recommendations Generative Adversarial Networks

This thesis enhances the trustworthiness of news recommendation systems by addressing the cold start problem for new users/items, enabling efficient user data removal for privacy, and mitigating multi-dimensional filter bubbles to improve diversity.

May 14 - May 8, 2022

Introduction to Computer Simulation - A short course - Lecture 3

Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

May 10, 10:00 - 11:30

B1 L4 R4102

Computer simulations

This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
Introduction to Computer Simulation - A short course - Lecture 2

Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

May 9, 10:00 - 11:30

B1 L4 R4102

Computer simulations

This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
Introduction to Computer Simulation - A short course - Lecture 1

Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

May 8, 10:00 - 11:30

B1 L4 R4102

Computer simulations

This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

Oct 17 - Oct 11, 2020

Dynamic Programming Multi-Objective Combinatorial Optimization

Michal A. Mankowski, Ph.D., Computer Science

Oct 13, 15:00 - 16:00

KAUST

combinatorial optimization global sequence alignment multi-stage optimization algorithm

In this dissertation, we consider extensions of dynamic programming for combinatorial optimization. We introduce two exact multi-objective optimization algorithms: the multi-stage optimization algorithm that optimizes the problem relative to the ordered sequence of objectives (lexicographic optimization) and the bi-criteria optimization algorithm that simultaneously optimizes the problem relative to two objectives (Pareto optimization).

Sep 22 - Sep 16, 2018

CS Graduate Seminar by visitor Professor Vadim Lozin

Vadim Lozin, Professor, University of Warwick, UK

Sep 17, 14:00 - 15:00

KAUST

Abstract In this talk, we introduce a new graph parameter under the name functionality. We show that functionality generalizes simultaneously several other graph parameters, such as degeneracy or clique-width, by proving that bounded degeneracy or bounded clique-width imply bounded functionality. Moreover, we show that this generalization is proper by revealing classes of graphs of unbounded degeneracy and clique-width, where functionality is bounded by a constant. This includes permutation graphs and unit interval graphs. We also observe that bounded functionality implies bounded VC-dimension

Apr 22 - Apr 16, 2017

Breaking the Boundaries: from Structure to Algorithms

Vadim Lozin, Professor, University of Warwick, UK

Apr 17, 14:00 - 15:00

KAUST

maximum independent set line graphs boundary classes of graphs

Abstract Finding a maximum independent set in a graph is an NP-hard problem. However, restricted to the class of line graphs this problem becomes polynomial-time solvable due to the celebrated matching algorithm of Jack Edmonds. What makes the problem easy in the class of line graphs and what other restrictions can lead to an efficient solution? To answer these questions, we employ the notion of boundary classes of graphs. In this talk, we shed some light on the structure of the boundary separating difficult instances of the problem from polynomially solvable ones and analyze algorithmic tools