Ph.D. in

Computer Science

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Mode
Full-time

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Credits
60

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Location
On-campus

Overview

The goal of the Doctor of Philosophy (Ph.D.) in Computer Science is to produce highly trained researchers for industry and academia. The program prepares students to apply the research techniques and knowledge they have gained to solve complex problems in the field of Computer Science and AI.

The Ph.D. in Computer Science offers exciting opportunities to do cutting-edge applied research and produce new intellectual contributions with world leaders in their field. It is designed to prepare students for leadership careers in academia, industry research labs and education in computer science. As a graduate of this program, students will not only have strong technical and research expertise in their field but will also have the ability to work effectively in interdisciplinary teams and be able to tackle problems that require both technical and non-technical solutions.

overview

Program learning outcomes

By the end of this program, students will be able to:

  1. Analyze complex computing problems and apply principles of computing and other relevant disciplines to devise solutions
  2. Develop research projects in computer science that meet high standards of theoretical and methodological rigor
  3. Recognize social and professional responsibilities and make informed decisions, which consider the impact, sustainability and entrepreneurial feasibility of computer science solutions and innovations in global and local, economic, environmental, and societal contexts
  4. Systematically review, analyze, and interpret the body of scientific literature and innovations in Computer Science
  5. Communicate new knowledge orally and through original research of publishable quality which satisfied peer review

Completion requirements

The minimum degree requirements for the Doctor of Philosophy in Computer Science is 60 credits, distributed as follows:

Core courses Number of courses Credit hours
Core 4 16
Electives 2 8
Internship At least one internship of three months duration must be satisfactorily completed as a graduation requirement. 2
Advanced Research Methods 1 2
Research Thesis 1 32

Core courses

Doctor of Philosophy in Computer Science is primarily a research-based degree. The purpose of coursework is to equip students with the right skillset, so they can successfully accomplish their research project (thesis). Students are required to take CS801, CS802, CS803 and CS804 as mandatory courses. 

Code Course Title Credit Hours
CS801 Advanced Complexity

The course covers the following topics: • The theory of NP-completeness and its relationship to the complexity classes P and NP. • Circuit complexity and alternations. • SAT, the complexity of counting, and algebraic circuit complexity. • Circuit complexity lowerbound, hardness vs randomness, ironic complexity, and interactive proof systems.

4
CS802 Advanced Data Structures

This course covers a broad overview of the many diverse types of data structures, including persistent, retroactive, geometric data structures, like a map, and temporal data structures, as in storage that happens over a time series. A comprehensive study of these data structures is a vital component of this subject. It also covers dictionaries, static trees, strings, succinct structures, and dynamic graphs. Finally, the course will cover the major directions of research for a wide variety of such data structures.

4
CS803 Randomized Algorithms

Randomized algorithms went from being a tool in computational number theory to finding widespread application in many types of algorithms. Two benefits of randomization have spearheaded this growth: simplicity and speed. This course discusses the basic and advanced concepts of randomized signals. Specifically, it includes random sampling, tail inequalities, probabilistic methods, algebraic methods, and random walks. Further, it also covers linear programming, graph algorithms and approximate counting topics.

4
CS804 Combinatorial Optimization

This course covers the topic of polyhedra, including various mathematical concepts and algorithms such as Farkas lemma, duality, complementary slackness, and decomposition of polyhedra. The course also covers topics like integer polyhedra, matrices, matching (bipartite and non-bipartite), graphs, matroids, polymatroids and submodular functions. The course will also cover the application of these concepts in machine learning.

4

Elective courses

Students will select a minimum of two elective courses, with a total of eight (or more) credit hours. One must be selected from List A and one must be selected from List A or B based on interest, proposed research thesis, and career aspirations, in consultation with their supervisory panel. The elective courses available for the Doctor of Philosophy in Computer Science are listed in the tables below:

Code Course Title Credit Hours
NLP801 Deep Learning for Language Processing

This course focuses on recent advances in Natural Language Processing and on developing skills for performing research to advance the state of the art in Natural Language Processing. This course builds upon concepts from Natural Language Processing (NLP 701) and assumes familiarity with fundamental concepts in Word Embedding, Information Extraction and Machine Translation.

4
NLP802 Current Topics in Natural Language Processing

This course focuses on recent advances in deep learning for natural language generation. It builds upon concepts from Natural Language Processing and assumes familiarity with fundamental concepts such as Transformers, Machine Translation, and Text Summarization.

4
NLP803 Advanced Speech Processing

This course focuses on developing knowledge about the state of the art in a wide range of Speech Processing tasks, and readiness for performing research to advance the state of the art in these topics.

4
NLP804 Deep Learning for Natural Language Generation

This course focuses on recent advances in deep learning for natural language generation. It builds upon concepts from Natural Language Processing and assumes familiarity with fundamental concepts such as Transformers, Machine Translation, and Text Summarization.

4
CV804 3D Geometry Processing

This course provides an introduction to 3D geometry processing, an important field that intersects computer vision, computer graphics, and discrete geometry. With 3D vision systems becoming increasingly sophisticated, object recognition and modeling is no longer limited to abstracted feature representations but are often high-fidelity digitization of real-world objects and environments. While 3D geometry processing has evolved significantly in the areas of visual effects and interactive games, they are impacting other domains, ranging from metaverse technologies to robotics, biomedicine, and additive manufacturing. Augmented/virtual reality systems are using 3D scanned virtual avatars to enable immersive communication, autonomous cars are capturing their 3D surroundings in real-time, and Google earth is digitizing entire worlds using satellite and geospatial data. With the emergence of 3D scanning, real-time depth sensors, and 3D printing technologies, polygonal meshes have become the de-factor standard for 3D surface representation. This course will cover the mathematical foundations for studying 3D surfaces from a discrete differential geometric standpoint and present the full geometry processing pipeline: from 3D data capture, mesh smoothing, surface reconstruction, parameterization, registration, shape analysis (correspondence, symmetry, matching), data-driven synthesis, interactive manipulation, to 3D printing. We will also illustrate this course with important applications and recent AI advances in this field, especially with new developments in 3D deep learning, deep generative models for 3D objects, and differentiable rendering. In analogy to image processing for which inputs are 2D images and video, 3D Geometry processing involves the treatment of 3D depth maps, point clouds, polygonal meshes and volumetric data and involves many techniques from linear algebra, differential geometry, signal processing, and numerical optimization. This course will offer practical coding exercises to understand basic geometry processing algorithms and exciting project around data capture and geometry processing.

4

Research thesis

The Ph.D. thesis exposes students to cutting-edge and unsolved research problems in the field of computer science, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of three to four years.

Code Course Title Credit Hours
CS899 Computer Science Ph.D. Research Thesis

PhD thesis research exposes students to cutting-edge and unsolved research problems, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of 3 to 4 years. PhD thesis research helps train graduates to become leaders in their chosen area of research through partly supervised study, eventually transforming them into researchers who can work independently or interdependently to carry out cutting-edge research.

32
RES899 Advanced Research Methods

This course will prepare students to produce professional-quality research and solve a practical research challenge in an organization based on an innovative, sustainable, and entrepreneurial research topic. This course will provide exposure to a variety of special topics, research integrity, ethics, organizational challenges, and needs related to various disciplines. Students will design and implement a research project suitable for conference presentation or journal submission relevant to their field of interest, in addition to peer-reviewing a paper. The instructor, and guest lecturers, as appropriate, will present topics necessary to develop well-rounded researchers, innovators, and entrepreneurs in the AI disciplines.

2
INT899 PhD Internship

The MBZUAI internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning.

2

Internship

The MBZUAl internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning.

Code Course Title Credit Hours
INT899 PhD Internship (up to four months)

PhD Internship (up to four months)

2

Admission criteria

MBZUAI accepts applicants from all nationalities who hold a completed degree in Computer Science from a university accredited or recognized by the UAE Ministry of Education (MoE), demonstrate academic distinction and have a strong background in both applied and theoretical aspects of Computer Science – either:

  • Bachelor’s degree with at least 50% Computer Science content and minimum CGPA of 3.5 (on a 4.0 scale) or equivalent, or
  • Master’s degree with a minimum CGPA of 3.2 (on a 4.0 scale) or equivalent

Applicants must provide their completed degree certificates and official transcripts when submitting their application. Senior-level students can apply initially with a copy of their official transcript and expected graduation letter and upon admission must submit the official completed degree certificate and transcript. A degree attestation from UAE MoE (for degrees from the UAE) or Certificate of Recognition from UAE MoE (for degrees acquired outside the UAE) should also be furnished within students’ first semester at MBZUAI.

 

All submitted documents must either be in English, originally, or include official English translations. Additionally, official academic documents should be stamped and signed by the university authorities.

Each applicant must show proof of English language ability by providing valid certificate copies of either of the following:

 

  • TOEFL iBT with a minimum total score of 90
  • IELTS Academic with a minimum overall score of 6.5
  • EmSAT English with a minimum score of 1550

 

TOEFL iBT and IELTS academic certificates are valid for two (2) years from the date of the exam while EmSAT results are valid for eighteen (18) months. Only standard versions (i.e. conducted at physical test centers) of the accepted English language proficiency exams will be considered.

 

Waiver requests from eligible applicants who are citizens (by passport or nationality) of UK, USA, Australia, and New Zealand who completed their studies from K-12 until bachelor’s degree and master’s degree (if applicable) from those same countries will be processed. They need to submit notarized copies of their documents during the application stage and attested documents upon admission. Waiver decisions will be given within seven (7) days after receiving all requirements.

Submission of GRE scores is optional for all applicants but will be considered a plus during the evaluation.

In a 500- to 1000-word essay, explain why you would like to pursue a graduate degree at MBZUAI and include the following information:

 

  • Motivation for applying to the university
  • Personal and academic background and how it makes you suitable for the program you are applying for
  • Experience in completing a diverse range of projects related to artificial intelligence
  • Stand-out achievements, e.g. awards, distinction, etc
  • Goals as a prospective student
  • Preferred career path and plans after graduation
  • Any other details that will support the application

The research statement is a document summarizing the potential research project an applicant is interested in working on and clearly justify the research gap which the applicant would like to fill in during the course of his/her study. It must be presented in the context of currently existing literature and provide an overview of how the applicant aims to investigate the underlying research project as well as predict the expected outcomes. It should mention the relevance and suitability of the applicant’s background and experience to the project and highlight the project’s scientific and commercial significance. The research statement should include the following details:

 

  • Title
  • Problem definition
  • Literature review
  • Proposed research/methods/solution (optional)
  • Study timeline (a table, figure or a small paragraph presenting your plans for the four years in the Ph.D. program)
  • List of references

 

Applicants are expected to write the research statement independently. MBZUAI faculty will NOT help write it for the purpose of the application. The MBZUAI Admission Committee will review the submitted document and use it as one of the measures to gauge and assess applicants’ skills.

Applicants will be required to nominate referees who can recommend their application. Ph.D. applicants should have a minimum of three (3) referees wherein at least one was a previous course instructor or faculty/research advisor and the others were current or previous work supervisors.

 

To avoid issues and delays in the provision of the recommendation, applicants have to inform their referees of their nomination beforehand and provide the latter’s accurate information in the online application portal. Automated notifications will be sent out to the referees upon application submission.

All applicants with complete files, including the required number of recommendations, will be invited to participate in an online screening exam to assess their knowledge and skills. Completion of the exam is not mandatory but highly encouraged as it would provide additional information to the evaluation committee. Waiving the exam is only recommended for those students who can provide strong evidence of their research capability, subject matter expertise, and technical skills.

 

Exam Topics

MathCalculus, probability theory, linear algebra, trigonometry and optimization

 

Machine learning: Machine learning algorithms and concepts such as linear regression, decision trees, loss functions, support vector machines, classification, regression, clustering, convolutional neural networks, dimensionality reduction, neural networks and unsupervised learning

 

Programming: Knowledge and understanding of the theory of computation, computational complexity, databases, computer architecture and operating systems

 

Specialization topics: Knowledge and understanding of the theory of computation, computational complexity, databases, computer architecture and operating systems

 

Applicants are highly encouraged to complete the following online courses to further improve their qualifications:

 

 

The exam instructions are available here

A select number of applicants may be invited to an interview with faculty as part of the screening process. The time and instructions for this will be communicated to applicants on timely bases.

Study plan

A typical study plan is as follows:

Semester 1

CS801 Advanced Complexity
CS802 Advanced Data Structures
1 elective from List

Semester 2

CS803 Randomized Algorithms
CS804 Combinatorial Optimization
+ 1 elective from List

Summer

INT899 PhD Internship (up to four months)

Semester 3

RES899 Advanced Research Methods
CS899 Computer Science Ph.D. Research Thesis

Semester 4

CS899 Computer Science Ph.D. Research Thesis

Semester 5

CS899 Computer Science Ph.D. Research Thesis

Semester 6

CS899 Computer Science Ph.D. Research Thesis

Semester 7

CS899 Computer Science Ph.D. Research Thesis

Semester 8

CS899 Computer Science Ph.D. Research Thesis

Career prospects

AI is permeating every industry. At recent employer engagement events at MBZUAI, there has been representation from multiples sectors including (but not limited to):

  • Aviation, consultancy, education, energy, finance, government entities, healthcare, media, oil and gas, security and defense, research institutes, retail, telecommunications, transportation and logistics, and startups.

Recent job opportunities advertised via the MBZUAI Student Careers Portal include (but not limited to):

  • AI solution architect, AI solution engineer, algorithmic engineer, data analyst, data engineer, data scientist, data strategy consultant, full stack software engineer, full stack web developer, predictive analytics researcher, and senior data scientist – consultant.

Other career opportunities could include (but not limited to):

  • Applied scientist, analytics engineer, augmented/virtual reality, autonomous cars, biometrics and forensics, chief data officer, data platform leadership, data journalist, data and AI technical sales specialist, growth analytics / engineers, manager: AI and cloud services planning, machine learning engineers, product manager: AI and data analytics, product data scientist, product analyst, remote sensing, research assistants, security and surveillance, senior software engineer, and VP data.

Disclaimer: Subject to change.

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