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Doctor of Philosophy in Computer Science

College
College of Computing and Informatics
Department
Computer Science
Level
PhD
Study System
Thesis and Courses
Total Credit Hours
54 Cr.Hrs.
Duration
3-5 Years
Intake
Fall & Spring
Location
Sharjah Main Campus
Language
English
Study Mode
Full Time

Doctor of Philosophy in Computer Science

Introduction
The Doctor of Philosophy degree in Computer Science (PhD-CS) program's main goal is to provide advanced knowledge in the field of computer science with an in-depth research experience. The program will offer a comprehensive list of courses based on the core of computer science, research, and optimization methodologies concentrated on advanced development in computer science.

Candidates admitted to the program are expected to have completed a Master's degree in computer science or a closely related field. For the award of PhD-CS degree, candidates are required to successfully finish the course work, pass a comprehensive exam, and complete a research-based dissertation.

The PhD-CS program emphasizes proficiency in understanding fundamental and advanced topics in computer science, communicating learned knowledge with excellent oral and written skills, and taking the lead in research and development in chosen field of expertise. The candidates should demonstrate their ability to engage independently in state-of-the-art research and provide original and significant contribution in their area of specialization.
 

Program Goals and Learning Outcomes
The program goals are:

  • Assume leadership roles in the area of Computer Science with emphasis on scholarship, teamwork and professional ethics.
  • Address challenges in Computer Science to fulfill the needs of the local and international community.
  • Investigate and provide original solutions of Computer Science research problems.
  • Adapt to ever-changing research and innovation landscape, and contribute to the expansion of knowledge.

 
Program Learning Outcomes:
Upon the successful completion of the program, students should be able to:

  1. Interpret emerging knowledge in specific areas of computer science through original research of publishable quality.
  2. Develop new knowledge that advances the state of the art in computer science. 
  3. Apply advanced problem-solving skills to analyze, design and implement innovative solutions for the existing and/or new research problems. 
  4. Utilize expert communication and information technology skills to present, explain and/or critique highly complex and diverse matters to different types of audiences 
  5. Work independently with considerable authority or in team collaboration with professional integrity to complete challenging computer science projects in a timely manner. 
  6. Evaluate legal, ethical, environmental, and socio-cultural implications of computer science technologies, and take a lead in making decent informed decisions on complex issues.
 
Completion Requirements
Before graduation, student should complete all graduation requirements that include:

  • Completing successfully all courses of the program.
  • Pass the PhD Comprehensive Exam.
  • Completing successfully the thesis and/or essays as specified in the curriculum.
  • Obtaining a minimum cumulative GPA of 3.0.


Special Admission Requirements

a. The student must hold a master's degree with a minimum grade of "Very Good" (3.0 out of 4.0) and a bachelor's degree with a minimum grade of 2.5 out of 4.0 or equivalent from a university, college, or an institute recognized by the University of Sharjah and the Ministry of Higher Education and Scientific Research of the UAE. Students with a grade of "Good" may be accepted conditionally.

b. The Bachelor's and Master's degrees must be in a major that allows the student to pursue a doctorate graduate program. A student may be admitted, if his/her major is different from the program he/she is applying for, upon the recommendation of the Department and approval of the Council. A student who lacks necessary prerequisite courses may take remedial courses concomitantly or before the Doctorate program.

c. Meeting the TOEFL condition

1. Students in programs offered in English: The student must obtain 550 points on the TOEFL test or 6 on the IELTS.

2. Exemption:


    • Students whose native language is English shall be exempted from the TOEFL test score if the language of instruction in the bachelor's or master's degree is English, and the degree has been obtained from a country where English is the official language.
    • Students who graduate from academic institutions where English is the language of instruction are also exempted provided that they have a minimum score of 500 on the TOEFL or equivalent tests upon joining the undergraduate program and 550 points or equivalent upon joining the master's program.
d. Passing an admission examination or "Test-Interview" prepared by the Department.

e. The Departmental council may, with the approval of the Council, stipulate additional conditions for admissions and re-admissions.
 

Program Structure

Requirements Compulsory Elective Total
Courses Credit Hours Courses Credit Hours Courses Credit Hours
Courses 3 9 5 15 8 24
PhD Dissertation​
1 27 0 0 1 27
PhD Seminar 1 3 0
0 1 3
Total Credit Hours 39 15 54
 

Program Requirements

  • Compulsory courses (12 credit hours)
  • Elective Courses (15 credit hours)
  • Pass PhD Comprehensive Exam
  • Dissertation (27 credit hours)
 
Compulsory Courses
 
Course Code Course Title Credit Hours Pre-requisite
1501771 Advanced Data Structures and Algorithms 3 1501371 or equiv.
1501701 ​Mathematical and Statistical Essentials 3 Grad Standing
1501790 PhD Research Seminar 3 Grad Standing
1501791 Directed Studies 3 Grad Standing
1501893 PhD Comprehensive Exam 0 QE Panel Approval
1501894 PhD Dissertation 27  
 

Elective Courses
 
Group
Course Code
Course Title Credit Hours Pre-requisite
​​Artificial​ Intelligence and Applications ​ ​​

1501731 Topics in Machine Learning 3 1501371 or equiv.
1501735 Topics in Computer Vision 3 1501371 or equiv.
1501830 Topics in Artificial Intelligence 3 1501440 or equiv.
1501730 Natural Language Processing 3 1501371 or equiv.
​Networking and Security ​ ​ 1501752 Wireless Sensor Networks 3 Grad Standing
1501757 Topics in Information Security 3 Grad Standing
1501753 Topics in Networking 3 1501352 or equiv.
​​​Information and Software ​ ​ ​ ​ 1501761 Topics in Data Mining 3 1501263 or equiv.
1501768 Big Data and Data Analytics 3 1501263 or equiv.
1501760 Topics in Software Engineering 3 Grad Standing
1501762 Topics in Database Systems 3 1501263 or equiv.
1501861
Topics in Data Analytics and Cloud Computing 3 Grad Standing
 


Study Plan

The PhD-CS Program is organized into 8 semesters spanning over 4 academic years, as shown in the tables below.

First Year 
​Fall Semester
​Spring Semester
Course #
Course Title Cr Pre-req. Course # Course Title Cr Pre-req.
1501771 Advanc​ed Data Structures and Algorithms
3   1501790 PhD Research Seminar 3  
1501701 Mathematical and Statistical Essentials 3   1501xxx Elective 2 3  
1501xxx Elective 1 3          
        1501791 Directed Studies 3  
  Total 9     Total 9  
 

Second Year 
​Fall Semester 
​ ​ ​
​ Spring Semester​
​ ​ ​
Course #
Course Title Cr Pre-req. Course # Course Title Cr Pre-req.
1501893 PhD Comprehensive ​Exam 0   1501894 PhD Dissertation 9  
1501xxx Ele​ctive 3
3          
1501xxx Elective 4 3          
1501xxx Elective 5 3          
  Total 9     Total 9  
  

Third Year
​Fall Semester
​Spring Semester
Course #
Course Title Cr Pre-req. Course # Course Title Cr Pre-req.
1501894 PhD Di​ssertation 9   1501894 PhD Dissertation 9  
  Total 9     Total 9  
 

Fourth Year 
​Fall Semester
Spring Semester​
Course #

Course Title Cr Pre-req. Course # Course Title Cr Pre-req.
1501894 PhD Disser​tation
0   1501894 PhD Dissertation 0  
  Total 0     Total 0  
 


​​Course Description

1501771-Advanced Data Structures and Algorithms

This course covers advanced data structures and algorithms to solve fundamental computing problems and shows the role of data structures in algorithm design and the use of amortized complexity analysis to determine how data structures affect performance. It covers advanced methods and techniques for designing algorithms using appropriate data structures, proving their correctness and analyzing their efficiency. Advanced Data Structures such as B-Trees, Fibonacci Heaps, and Data Structures for Disjoint Sets are discussed. Many classical network optimization algorithms, as well as newer and more efficient algorithms selected from the recent technical literature.

 

1501701-Mathematical and Statistical Essentials

This course provides a comprehensive mathematical and statistical foundations for the program. It builds upon fundamental concepts in linear algebra, probability theory, basic statistics, and optimization. The course overviews basics and advanced topics that are frequently encountered in computer science applications. The students will learn the basic matrix operations and types, probability models and sampling distributions, statistical inference, regression and correlation analysis, supervised and unsupervised probabilistic learning. The student will also learn the principles and methods of optimization.

 

1501790-PhD Research Seminar

This is a 3-credit hour course intended to hone students' skills and professional development for undertaking any research-oriented task. The students will sharpen their skills from knowledge exchange in a collaborative environment, such as seminars and group discussions. The students will also learn from peers to acquire, analyze, criticize, and present in a collaborative research environment.

 

1501791-Directed Studies

This course helps the students in exploring their areas of interest or enables them to develop in-depth research in a field of interest. The students will be encouraged to target those areas of interest in which they are planning to carry out their theses. The course intends to complete and polish the knowledge of the students while allowing them to develop their critical thinking and analysis skills. The registration in this course and its topic should be approved in advance by the student's potential thesis supervisor and the PhD program coordinator.

 

1501893-PhD Qualifying Exam

Every PhD student must pass a Comprehensive Examination designed to evaluate the breadth and depth of the student's knowledge of his or her discipline, as well as the student's scholarly potential. The comprehensive exam consists of a written exam that will be prepared, administered, and evaluated by an examination committee from the computer science department. Students taking the comprehensive exam must be in good academic standing and complete the required coursework.

The Comprehensive Exam consists of three written exams covering three topics. One core topic (Advanced Data Structures and Algorithm Design), and two subjects are selected by the PhD student in consultation with his/her PhD academic advisor such as: Artificial Intelligence, Networking, Security, Software Engineering, and Data Science.

 

1501894-PhD Dissertation

Students must undertake and complete an independent theoretical and/or practical research under the supervision of a faculty member. Students are required to submit a dissertation documenting their research and defend it in an oral examination before a committee. The dissertation work should provide the student with advanced knowledge in computer science subjects with an in-depth research experience. Students are required to produce at least one refereed publication of their work before defending the dissertation.

 

1501731-Topics in Machine Learning

This course involves special topics in Machine Learning (ML). The course explores advanced/specialized topics in ML that are not currently offered as regular courses in the PhD in Computer Science curricula. The topics depend on the interest of the instructor and contents may vary at each offering.

The following course description followed by the weekly topics are a specific sample for this course. Main topics include regression, classification, clustering, and deep learning. In regression, we plan to cover simple and multiple models, feature selection techniques such as L1 and L2 regularization methods, and bias and variance theory. In classification, the topics include linear classifiers, logistic regression, decision trees, ensemble learning, support vector machines, and artificial neural networks. Clustering will include k-means algorithm using centroid-based and density-based types. In deep learning, we aim at covering convolutional neural networks as well as recurrent neural networks. Students will learn how to identify and implement suitable machine learning algorithms for a variety of problems.

 

1501735-Topics in Computer Vision

This is a special topics course. The topics course usually introduces advanced/specialized areas that are not currently offered in regular courses. The topics depend on the interest of the instructor and contents vary at each offering. The following course description followed by the weekly topics is a specific sample for this particular course.

Introduction to the basic and advanced concepts and techniques in computer vision. After completing this course, the students will be able to apply a variety of computer techniques for the design of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition. The topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking and motion analysis, gesture recognition, image formation and camera models, and stereo vision. The course will cover the deep learning concepts with introduction to various architectures and their applications.

 

1501830-Topics in Artificial Intelligence

This course involves selected topics in Artificial Intelligence (AI). The course explores advanced/specialized topics in Artificial Intelligence that are not currently offered as regular courses in the PhD in Computer Science curricula.

The topics depend on the interest of the instructor and contents may vary at each offering. This advanced graduate course explores in depth several important topics in modern Artificial Intelligence. The main topic list may include intelligent agents, uninformed and informed search, adversarial search, constraint satisfaction problem, Bayesian networks, decision networks, and reinforcement learning. In addition, advanced topics will be covered from the following fields: machine learning, natural language processing, computer vision, robotics, and deep learning. We will supplement the lectures with paper discussions and there will be a significant research project component to the class to learn current research issues.

 

1501730-Natural Language Processing

This course provides a broad coverage of the field of Natural Language Processing (NLP) through the study of the models, methods, and algorithms of NLP for common NLP problems. Topics include regular expressions, n-gram language models, naive Bayes, sentiment classification; lexicon for sentiments, vector semantics and embeddings, Neural networks, part-of-speech tagging; sequence processing, machine translation, transfer learning w​ith contextual embeddings and pre-trained language models, question answering, and multi-label text classification.

 

1501752-Wireless Sensor Networks

This is an advanced course in wireless sensor networks (WSNs). It covers advanced technologies and methods for establishing generic WSN. After introducing the fundamental issues, the course establishes an understanding of the current research issues in network requirements related to the design, implementation, and deployment of a distributed control network. Key topics discussed in the context of wireless sensor networks are Principle of Wireless Sensor Networks, Hardware Design for WSNs, Embedded Software Design for WSNs, Routing Technologies in WSNs, Optimization of Sink Node Positioning, Sensor Data Fusion and Event Detection, WSN Security, Mobile Target Localization and Tracking, Internet of Things. The course then covers how current research on concepts and techniques for wireless networks aim to address these requirements.  

 

1501757-Topics in Information Security

This course involves special topics in information security. The topics depend on the interest of the instructor and the contents may vary at each offering. The following course description followed by the weekly topics is a specific sample for this particular offering.

In this course, students will study various malicious software attacking computer systems and networks. Most of these attacks are caused by the vulnerabilities in the design and implementation of computer systems. Students will learn different techniques to evaluate the security of computer systems and detect common vulnerabilities. They will also appreciate security principles and standards to write secure code. Students are required to conduct research on the course topics and write a review paper. A project is also required.

 

1501753-Topics in Networking

This course involves special topics in networking. The topics depend on the interest of the instructor and the contents may vary at each offering. The following course description followed by the weekly topics is a specific sample for this particular offering.

This is a graduate level course emphasizing the current research in wireless networking, especially dealing with the infrastructureless flavor of these networks, called wireless ad hoc networks. Wireless networks, such as vehicular ad hoc networks, wireless sensor networks, wireless mesh networks, mobile ad hoc networks are all based on the ad hoc paradigm. This course covers the main characteristics of ad hoc networking and focuses on routing, medium access protocols, energy conservation, and security. The important part of this course will be performance evaluation of various protocols using a simulator which will help students in carrying out their research work.

 

1501761-Topics in Data Mining

This course involves special topics in data mining. The course explores advanced/specialized topics in data mining that are not currently offered as regular courses in the PhD in Computer Science curricula. The topics depend on the interest of the instructor and contents may vary at each offering.

The following course description followed by the weekly topics are a specific sample for this topic course. Areas covered include techniques to uncover hidden information, such as patterns, in databases. The data to be mined could be complex data including multimedia, spatial, and temporal. The techniques include data processing, association rules, clustering, and classification. We will supplement the lectures with discussions recent papers in data mining. There will be a research project component on the current research issues in data mining.

 

1501768-Big Data and Data Analytics

Big geospatial data is becoming one of the most important technologies that enables organizations to store, manage, and manipulate vast amounts of spatial data efficiently to gain business insights. Big Data course provides the fundamentals, technologies, and tools to understand and apply the following the Big Data analytics in the field of geographic information systems and remote sensing. Topics covered are: Big Data types, technologies, analytical tools, numerical, textual, image and stream analysis, and applications of Big geospatial Data and remote sensing.

 

1501760-Topics in Software Engineering

This is a special topics course. The topics course usually introduces advanced/specialized areas that are not currently offered in regular courses. The topics depend on the interest of the instructor and contents vary at each offering. The following course description followed by the weekly topics is a specific sample for this particular course.

This course covers topics in software engineering. These topics include quality assurance strategies and techniques, software testing and the principles of software measurement. This course is focused on the quality assurance principles underlying the development of software that needs to meet specific external goals, where these goals need to be expressible in measurable terms. It also introduces different software testing methods and discusses the application of software measurement techniques to these testing methods.

 

1501762-Topics in Database Systems

This course involves special topics in database systems. The course explores advanced/specialized topics in database systems that are not currently offered as regular courses in the PhD in Computer Science curricula. The topics depend on the interest of the instructor and contents may vary at each offering.

The following course description followed by the weekly topics are a specific sample for this topic course. This advanced graduate course explores in depth several important topics in Databases. The course will emphasize the recent advances in database systems. Areas covered include topics such as query processing, access methods, traditional databases, spatial databases, multimedia databases, streaming databases, and distributed database. We will supplement the lectures with paper discussions and there will be a significant research project component to the class to learn current research issues. 

 

1501861-Topics in Data Analytics and Cloud Computing

This course involves special topics in data analysis and cloud computing. The course explores advanced/specialized topics in data analysis and cloud computing that are not currently offered as regular courses in the PhD in Computer Science curricula. The topics depend on the interest of the instructor and contents may vary at each offering. The following course description followed by the weekly topics are a specific sample for this topic course. Areas covered include cloud systems, data processing frameworks, networking, cloud data centers, state-of-the-art data processing frameworks, cloud workload characteristics, and resource management and scheduling. We will supplement the lectures with paper discussions and there will be a research project component to the class to learn current research issues.

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