PhD Course Descriptions

Credits: 3 (3-0-0) Prerequisite: None

This PhD-level course focuses on the advanced theoretical and practical aspects of algorithms and computational complexity. The course is designed to explore cutting-edge topics such as algorithmic design techniques, optimization methods, and complexity classes. Students will engage with current research problems and gain a deep understanding of the fundamental principles underlying computational efficiency, algorithmic innovation, and complexity-theoretic challenges. Prior knowledge in undergraduate-level algorithms, probability, and discrete mathematics is required.

Credits: 3 (3-0-0) Prerequisite: None

This course explores the design, implementation, and management of distributed database systems (DDS). At the PhD level, it focuses on advanced topics such as data distribution techniques, consistency models, distributed query optimization, fault tolerance, and transaction management in distributed environments. The course will also cover current trends and research topics in DDS, such as blockchain-based databases, distributed ledger technologies, and big data processing frameworks. Students will engage with both theoretical foundations and practical aspects through projects, research papers, and simulations.

Credits: 3 (3-0-0) Prerequisite: None

This PhD-level course provides a comprehensive overview of research methodologies specific to computing and information science. It equips students with the theoretical foundations and practical skills necessary to conduct high-quality research in computer science. The course covers empirical, experimental, and qualitative research methods, as well as the design, execution, and presentation of research projects. Students will engage in critical evaluation of research literature and explore the ethical dimensions of conducting research in computing fields.

Credits: 1 (1-0-0) Prerequisite: None

This course is designed to expose doctoral students to various research areas in their domain. Speakers from the industry and academia are invited to give talks in their research experience. Students are expected to attend every class (one contact hour per week). At the end of the semester, students are required to submit a course report that summarizes presented research problems and solutions.

Credits: 1 (1-0-0) Prerequisite: CIS 614

This course is designed to escalate doctoral students with advanced skills in developing research problems that meet national and international needs. Speakers from the industry and academia are invited to deliver presentations about current research problems and possible solutions. Students are expected to attend every class (one contact hour per week). At the end of the semester, students are required to deliver presentations about recent PhD level research problems and possible solutions.

Credits: 1 (1-0-0) Prerequisite: CIS 615

This course is designed to prepare students for their PhD thesis through the presentation of their thesis proposals and the evaluation of others’ proposals. Students are required to present their proposals or their expected thesis problems. Furthermore, they should create and deliver a problem statement, literature review, and research methodology. At the end of the semester, students have to submit a report showing how they have responded to the evaluation comments.

Credits: 3 (3-0-0) Prerequisite: None

This course delves into data communication and networking topics essential for understanding and innovating within high-performance, distributed, and secure network systems. Covering protocols, algorithms, network architecture, and emerging challenges, the course prepares students to critically analyze, design, and improve networking systems, considering both theoretical foundations and real-world applications. Students will engage with cutting-edge research and participate in projects to explore new directions in network science, security, and data-intensive network applications.

Credits: 3 (3-0-0) Prerequisite: None

This course provides an in-depth exploration of advanced techniques in image processing, including the latest research topics and methodologies used for solving complex imaging problems. It is designed for PhD students in Computer Science, focusing on topics such as compressed sensing, image reconstruction, denoising, and tomographic imaging. Students will engage with both the theoretical foundations and practical applications, developing the skills to contribute to cutting-edge research in image processing.

Credits: 3 (3-0-0) Prerequisite: None

This course explores advanced topics and recent research, emphasizing theoretical and practical aspects essential for research-oriented careers. This course covers machine learning (ML) foundations, recent algorithms, and new approaches such as online, transfer, and reinforcement learning. Students engage in hands-on projects and critical literature reviews, fostering skills in algorithmic design and performance evaluation within fields like natural language processing, computer vision, and recommendation systems.

Credits: 3 (3-0-0) Prerequisite: None

This course provides an in-depth examination of software verification and testing methods, with a focus on advanced topics relevant to both academic research and industrial application. Covering verification techniques, testing paradigms, and the challenges of large-scale systems, the course prepares PhD students to contribute to cutting-edge developments in software quality assurance. Students will explore automated testing tools, formal methods, and model-based testing, enhancing their ability to design fault-tolerant systems and conduct thorough verification and validation processes.

Credits: 3 (3-0-0) Prerequisite: None

This course explores the principles and frameworks that underlie the structure and design of enterprise information systems. It focuses on the layered architecture of information systems, covering data, application, and technology layers, and examines how these architectures align with business objectives. Key topics often include systems integration, security, and scalability in modern enterprise environments, as well as the use of frameworks such as TOGAF (The Open Group Architecture Framework) or Zachman.

Credits: 3 (3-0-0) Prerequisite: None

This course provides a comprehensive study of the theories, frameworks, and tools for managing knowledge in organizations and engineering knowledge-based systems. It addresses the challenges in acquiring, organizing, sharing, and applying knowledge, with a focus on leveraging technology to optimize decision-making and organizational learning. Topics include knowledge representation, ontology development, data mining, and the implementation of knowledge management (KM) systems. The course also explores ethical considerations, security issues, and case studies of KM initiatives across various industries.

Credits: 3 (3-0-0) Prerequisite: None

This course covers a wide range of topics in cryptography including Symmetric Cryptography and the Asymmetric/Public Key Cryptography. Topics include: Pseudo Random Generators, Stream Ciphers (RC4), Block Ciphers (3DES, AES), Modes of Operation (ECB, CBC, CFB, OFB, CTR), Side Channel Attacks, Hash functions (MD5, SHA), MACs, RSA, El Gamal, ECC, Fiat-Shamir, Schnorr, ECDSA, key exchange protocols (Diffie-Hellman, Encrypted Key Exchange), and advanced topics like Zero Knowledge proofs, Blind Signatures, Secure Multiparty Computations, and Quantum Cryptography.

Credits: 3 (3-0-0) Prerequisite: None

This course provides in-depth knowledge in cloud computing security to address different security issues, and how to mitigate these issues. The course covers many topics such as: Security Design and Architecture for Cloud Computing, Security-as-a-Service, Identity and Access Management (IAM), Service Level Agreements, Cloud Forensics, Data Security in the Cloud and Cloud Storage Security, Data Loss Prevention, Intrusion Detection for Cloud Computing, Monitoring, Auditing and Management, Cloud Architecture for Blockchain, Cloud Ransomware, and Virtual Machine Migration and Security implications.

Credits: 3 (3-0-0) Prerequisite: None

This course presents an overview of the principles and practices of digital forensics investigation. The course provides students with solid understanding on how popular Digital Forensics is to investigate security breaches. It covers the advanced topics of today’s digital forensics that experts require such as Network Forensic Analysis, Mobile Forensic concepts and procedures, Virtual Machine Forensics, Email Forensics and Web Forensics. This course helps students gaining hand-on experience in conducting crime scene investigation using forensics.

Credits: 3 (3-0-0) Prerequisite: None

This course is designed for PhD-level students aiming to explore cutting-edge research areas within data science. It delves into advanced methodologies, algorithms, and theoretical foundations, with a strong focus on interdisciplinary applications. Students will engage with topics such as large-scale machine learning, deep learning, data mining, and data privacy, as well as emerging fields like graph-based models and AI interpretability. The course emphasizes research-oriented thinking and equips students with skills to tackle complex data-driven challenges.

Credits: 3 (3-0-0) Prerequisite: None

This course offers a comprehensive exploration of advanced concepts in data mining and data warehousing, essential for research-level work in the field of computer science. It covers the design, implementation, and use of data warehouses, as well as data mining techniques for extracting patterns, associations, and models from large datasets. The course is intended to prepare PhD students for independent research in topics related to big data, analytics, and the strategic use of data in modern organizations.

Credits: 3 (3-0-0) Prerequisite: None

This PhD-level course on Generative Artificial Intelligence (AI) delves into the theoretical foundations and advanced applications of AI systems capable of creating data, such as text, images, and other content. The course emphasizes state-of-the-art models like Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Students will explore the cutting-edge research in generative AI, addressing its impact on automation, digital transformation, and ethical considerations in various domains. Practical projects will enable students to design and implement generative AI systems for real-world tasks.

Credits: 3 (3-0-0) Prerequisite: None

The "Selected Topics in Computer Sciences" course addresses emerging and advanced areas within the field of computer science, allowing students to engage deeply with current research and methodologies in specific domains. Topics may vary each semester. The graduate committee has to approve course content before delivery

Credits: 3 (3-0-0) Prerequisite: None

The "Selected Topics in Information Systems" course addresses emerging and advanced areas within the field of information systems, allowing students to engage deeply with current research and methodologies in specific domains. Topics may vary each semester. The graduate committee has to approve course content before delivery.

Credits: 3 (3-0-0) Prerequisite: None

The "Selected Topics in Software Engineering" course addresses emerging and advanced areas within the field of Software Engineering, allowing students to engage deeply with current research and methodologies in specific domains. Topics may vary each semester. The graduate committee has to approve course contents before delivery.

Credits: 3 (3-0-0) Prerequisite: Comprehensive Examination

The PhD Thesis module in Computer Science is designed to guide doctoral candidates through the process of conducting independent, original research, culminating in the writing, presentation, and defense of a substantial dissertation. This module integrates knowledge from various areas of computer science and emphasizes the application of advanced methodologies, theories, and tools to solve novel problems. It supports students in framing research questions, applying rigorous methods, and communicating their findings effectively.