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Doctor of Philosophy in Natural Language Processing

The Doctor of Philosophy in Natural Language Processing is a research-based degree that aims to produce highly trained researchers for industry and academia. The program prepares students to apply research techniques and knowledge to solve complex problems in the field of natural language processing (NLP) and artificial intelligence (AI).

Overview

NLP focuses on system development that allows computers to communicate with people using everyday language. Natural language generation systems convert information from the computer database into readable or audible human language and vice versa. Such systems also enable sophisticated tasks such as inter-language translation, semantic understanding, text summarization, and holding a dialog. The key applications of NLP algorithms include interactive voice response applications, automated translators, digital personal assistants (e.g., Siri, Cortana, Alexa), chatbots, and smart word processors.

Program Details

• Mode: Full-time
• Credits: 60
• Location: On campus

Program Learning Outcomes

The program learning outcomes (PLOs) are aligned with the Emirates Qualifications Framework and are divided into three learning outcomes strands: knowledge (K), skills (S), and responsibility (R). Upon completion of the program requirements, graduates will be able to:

• Devise cutting-edge NLP algorithms with applications to real-life.
• Implement, evaluate, and benchmark existing state-of-the-art in NLP scholarly publications.
• Identify open research problems and formulate high-impact research questions.
• Independently develop innovative solutions to resolve unsolved research problems in high-impact real-life applications of NLP.
• Invent innovative, sustainable, and entrepreneurial state-of-the-art solutions to existing open research problems.
• Pursue an NLP project either independently or as part of a team in a collegial manner, with minimal supervision.
• Initiate, manage, and complete research manuscripts that demonstrate expert self-evaluation and advanced skills in scientifically communicating highly complex ideas.
• Initiate, manage, and complete multiple project reports and critiques on a variety of NLP problems.

Completion Requirements

The minimum degree requirements for the Doctor of Philosophy in Natural Language Processing are 60 credits, distributed as follows:

• Core courses: 8 credits
• Electives: at least 16 credits
• Internship: at least one internship of a minimum of three months duration
• Advanced research methods: 2 credits
• Research thesis: 32 credits

Core Courses

All students must take the following core courses:

• INT899: Ph.D. Internship
• NLP805: Natural Language Processing - Ph.D.
• NLP899: Natural Language Processing Ph.D. Research Thesis
• RES899: Advanced Research Methods
• Select one course from NLP806 or NLP807

Elective Courses

Students will select a total of 16 (or more) credit hours from the available elective courses, including:

• CBIO803: Single Cell Biology and Bioinformatics
• CS8201: Advanced Foundations of AI System Design
• CV801: Advanced Computer Vision
• CV802: Advanced 3D Computer Vision
• CV804: 3D Geometry Processing
• CV805: Life-long Learning Agents for Vision
• CV806: Advanced Topics in Vision and Language
• CV807: Safe and Robust Computer Vision
• CV8501: Medical Multimodal Vision
• CV8502: Trustworthy Medical Vision
• CV8503: Advanced Topics in Large Multimodal Models
• ML804: Advanced Topics in Continuous Optimization
• ML806: Advanced Topics in Reinforcement Learning
• ML808: Causality and Machine Learning
• ML809: Advanced Learning Theory
• ML813: Dimensionality Reduction and Manifold Learning
• ML815: Advanced Parallel and Distributed Machine Learning Systems
• ML818: Emerging Topics in Trustworthy ML
• ML8101: Foundations of Machine Learning
• ML8102: Advanced Machine Learning
• ML8103: Sequential Decision Making
• ML8501: Algorithms for Big Data
• ML8502: Machine Learning Security
• ML8503: Probabilistic Graphical Models
• ML8504: Data Synthesis
• ML8505: Tiny Machine Learning
• ML8506: Interpretable AI
• ML8507: Predictive Statistical Inference and Uncertainty Quantification
• ML8508: Privacy and Fairness
• ML8509: Collaborative Learning
• ML8510: Graph Machine Learning and Topics in Generative AI
• ML8511: Machine Learning for Industry
• NLP806: Advanced Natural Language Processing - Ph.D.
• NLP807: Speech Processing - Ph.D.
• NLP808: Current Topics in Natural Language Processing
• NLP809: Advanced Speech Processing
• NLP8501: Vision-to-Language Generation

Admission Criteria

• Completed degree in a STEM field with a minimum CGPA of 3.5 (on a 4.0 scale) or equivalent
• English language proficiency through one of the following:
  • IELTS Academic: Minimum overall score of 6.5
  • TOEFL iBT: Minimum score of 90
  • PTE Academic: Minimum overall score of 60
  • Cambridge C1 Advanced: Minimum 180
  • Cambridge C2 Proficiency: Minimum score 200
  • Duolingo English Test: Minimum score of 120
• Statement of purpose (500-1000 words)
• Research statement
• Referee recommendation (minimum of three referees)
• Screening exam (optional)
• Admission interview (select applicants)

Study Plan

A typical study plan is as follows:

• Semester 1: NLP805, 8 credits of electives
• Semester 2: Choose one from NLP806 or NLP807, RES899, 6 credits of electives
• Summer: INT899
• Semester 3: NLP899, 2 credits of electives
• Semester 4: NLP899
• Semester 5: NLP899
• Semester 6: NLP899
• Semester 7: NLP899
• Semester 8: NLP899

Disclaimer

Subject to change.