Computer Science MS Program

Program Mission:
The Master’s Program in Computer Science at KAUST delivers a rigorous, research-driven academic experience that equips students with advanced theoretical knowledge and cutting-edge practical skills. Through immersion in KAUST’s world-class research environment, students develop the capacity to achieve meaningful scientific and societal impact—advancing the frontiers of computing, driving innovation, and contributing to the Kingdom’s transformation toward a knowledge-based, technology-powered future.

Program Goals:
• Provide students with a rigorous, research-informed education that builds strong foundations in advanced computer science theory, systems, and practice.
• Develop students’ capacity for high-quality research and innovation by engaging them in creative problem-solving and hands-on inquiry within KAUST’s world-class research ecosystem.
• Prepare graduates for high-impact careers by equipping them with the technical expertise, analytical skills, and interdisciplinary mindset needed to excel in academia, industry, and sectors driving Saudi Arabia’s digital transformation.
• Cultivate graduates who can apply computational thinking to address complex scientific, societal, and regional challenges, contributing to the advancement of knowledge and the growth of a knowledge-based economy.

Program Learning Outcomes (PLOs)

Knowledge and Understanding
● K1 Accurate understanding of professional processes and practices such as software development methodologies, programming languages, computing system design, computational models, security frameworks, and industry-standard tools and techniques relevant to Computer Science.
● K2 Advanced knowledge of recent developments in areas of Computer Science, including artificial intelligence and machine learning, resilient computing and cybersecurity, computational bioscience and bioinformatics, visual computing and computational imaging, high-performance computing and big data, computing systems and databases, and theoretical computer science.
● K3 Critically assess and reflect on key Computer Science concepts and theories, providing innovative and creative solutions to emerging issues in areas such as reliability, ethical AI, and secure computing.

Skills
● S1 Apply specialized theories, principles, and computational models to advanced contexts such as artificial intelligence, big data analytics, cybersecurity, and distributed systems.
● S2 Implement and evaluate sophisticated software systems, machine learning pipelines, and distributed systems to solve real-world technical problems.
● S3 Process, analyze, and interpret quantitative and qualitative data in advanced contexts, using computational models, statistical methods, and visualization tools to support evidence-based decision making.
● S4 Select, adapt, and apply advanced ICT tools—including collaborative platforms, specialized programming frameworks, and data analysis environments—to advance research projects, support innovation, and address emerging challenges in computer science and related fields.

Values, Autonomy, and Responsibility
● V1 Uphold academic honesty and adhere to professional codes of conduct, particularly in areas involving privacy, intellectual property, cybersecurity, and the societal impacts of emerging technologies.
● V2 Demonstrate high levels of autonomy in managing specialized computing tasks, advanced projects, and innovative applications in Computer Science.
● V3 Contribute to societal well-being by applying computer science knowledge and skills to address global challenges, promote sustainability, and improve the quality of life through innovative and ethical technological solutions.

MS Course Requirements

MS students must complete the following requirements:

Core Courses (12 credits)
Elective Courses (12 credits)
Research/Capstone (12 credits)
Graduate Seminar (non-credit)
Winter Enrichment Program (non-credit)

Core and Elective Courses must be technical courses and cannot be substituted with Research or Internship to fulfill degree requirements.

CS students cannot take the following courses for academic credit toward their degree: CS 201 Introduction to Programming with Python, CS 204 Data Structures and Algorithms, CS 207 Programming Methodology and Abstractions. CS 209 Applied ML for Scientists and Engineers, selected offerings of CS 294 Contemporary Topics (as mentioned in their course description).

*For CS students, CS 201, CS 204, CS 207, and CS 209 are graded on an S/U (Satisfactory/Unsatisfactory) basis and are not counted towards the degree requirements.

Core Courses (12 credits)

Core Courses provide students with the background needed to establish a solid foundation in the program area. Students must complete 12 credits (4 Core Courses) and be aware that Core Courses may be offered only once per academic year. Student must choose from the following list:

CS 213	Knowledge Representation and Reasoning	3
CS 220	Data Analytics	3
CS 224	Introduction to Reinforcement Learning	3
CS 225	Neural Network Design and Training	3
CS 229	Machine Learning	3
CS 230	Computer Systems Security	3
CS 231	Applied Network Security	3
CS 232	Applied Cryptography	3
CS 240	Computing Systems and Concurrency	3
CS 244	Computer Networks	3
CS 245	Databases	3
CS 247	Scientific Visualization	3
CS 248	Computer Graphics	3
CS 249	Algorithms in Bioinformatics	3
CS 256	Digital Design and Computer Architecture	3
CS 258	System Architecture and Performance	3
CS 260	Design and Analysis of Algorithms	3
CS 272	Geometric Modeling	3
CS 283	Deep Generative Modeling	3

Elective Courses (12 credits)

Elective Courses allow students to tailor their educational experience to meet individual research and educational objectives with the permission of the Academic Advisor. All Elective Courses must be at the 200-300 level. Students must fulfill the following requirements:

One CS course (3 credits)
One Courses from any CEMSE Program (3 credits)
Two course from any KAUST Program* (6 credits)

*For CS students, CS 201, CS 204, CS 205, and CS 207 are graded on an S/U (Satisfactory/Unsatisfactory) basis and are not counted towards the degree requirements.

Graduate Seminar (non-credit)

Students must register for CS 398 and receive a Satisfactory grade for two Semesters during their MS.

Winter Enrichment Program (non-credit)

All students must complete the Winter Enrichment Program (WE 100) for credit at least once during their studies at KAUST. Students who have previously completed WEP will be exempt from this requirement in their future studies.

MS Thesis

Students planning to pursue the Thesis option must complete a minimum of 12 credits of Thesis Research (CS 297).

For more details on the Thesis Application, Thesis Committee Formation, Thesis Defense Results, Thesis Document and Thesis Archiving please check the policy page

MS Non-Thesis

Students wishing to pursue the non-thesis option must complete a total of 12 capstone credits, with 6 credits of directed research (CS 299). Students must complete the 6 remaining credits through one of the options listed below:

• Internship: Summer internship (CS 295) – students can only take one internship
• One 300-level CS course and one 300-level course from any degree program at KAUST.